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                "text": "in HTML, LaTeX) which represent the structure of the table. Since the",
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                "text": "the accuracy and run-time performance of any Im2Seq model, we inves-",
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                "text": "tigate in this paper how table-structure representation can be optimised.",
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                "text": "We propose a new, optimised table-structure language (OTSL) with a",
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                "text": "minimized vocabulary and specific rules. The benefits of OTSL are that",
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                "text": "it reduces the number of tokens to 5 (HTML needs 28+) and shortens",
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                "text": "the sequence length to half of HTML on average. Consequently, model",
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                "text": "accuracy improves significantly, inference time is halved compared to",
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                "text": "tactically correct. This in turn eliminates most post-processing needs.",
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                "text": "Popular table structure data-sets will be published in OTSL format to",
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                "text": "the community.",
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                "text": "Table Structure Recognition \u00b7 Data Representation \u00b7 Trans-",
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                "text": "formers \u00b7 Optimization.",
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                "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,",
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                "text": "manuals, specification sheets or marketing material. They often encode highly",
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                "text": "valuable information and therefore need to be extracted with high accuracy.",
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                "text": "ture, making it difficult to recover their correct structure with simple analyt-",
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                "text": "ical methods. Therefore, accurate table extraction is achieved these days with",
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          "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."
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          "text": "Keywords: Table Structure Recognition \u00b7 Data Representation \u00b7 Transformers \u00b7 Optimization."
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                "text": "Tables are ubiquitous in documents such as scientific papers, patents, reports,",
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                "text": "manuals, specification sheets or marketing material. They often encode highly",
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                "text": "ture, making it difficult to recover their correct structure with simple analyt-",
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          "text": "Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community."
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          "text": "rable to human observers [7,8,10,14,23]. On the other hand, the problem of table",
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          "text": "structure recognition (TSR) is a lot more challenging and remains a very active",
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          "text": "area of research, in which many novel machine learning algorithms are being",
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          "text": "explored [3,4,5,9,11,12,13,14,17,18,21,22].",
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          "text": "Recently emerging SOTA methods for table structure recognition employ",
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          "text": "transformer-based models, in which an image of the table is provided to the net-",
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          "text": "work in order to predict the structure of the table as a sequence of tokens. These",
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          "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for",
          "orig": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for",
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          "text": "a purely data-driven solution. The tokens of the sequence typically belong to a",
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          "text": "markup language such as HTML, Latex or Markdown, which allow to describe",
          "orig": "markup language such as HTML, Latex or Markdown, which allow to describe",
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          "text": "table structure as rows, columns and spanning cells in various configurations.",
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          "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure",
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          "text": "of a particular example table. Public table-structure data sets such as PubTab-",
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          "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from",
          "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from",
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          "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily",
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          "text": "the use of HTML as ground-truth representation format for TSR.",
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                "text": "Lysak, et al.",
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                "text": "Fig. 1.",
                "orig": "Fig. 1.",
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                "text": "Comparison between HTML and OTSL table structure representation: (A)",
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                "text": "table-example with complex row and column headers, including a 2D empty span,",
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                "text": "Recently emerging SOTA methods for table structure recognition employ",
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                "text": "work in order to predict the structure of the table as a sequence of tokens. These",
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                "text": "image-to-sequence (Im2Seq) models are extremely powerful, since they allow for",
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                "text": "markup language such as HTML, Latex or Markdown, which allow to describe",
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                "text": "table structure as rows, columns and spanning cells in various configurations.",
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                "text": "In Figure 1, we illustrate how HTML is used to represent the table-structure",
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                "text": "of a particular example table. Public table-structure data sets such as PubTab-",
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                "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from",
                "orig": "Net [22], and FinTabNet [21], which were created in a semi-automated way from",
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                "text": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily",
                "orig": "paired PDF and HTML sources (e.g. PubMed Central), popularized primarily",
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                "text": "the use of HTML as ground-truth representation format for TSR.",
                "orig": "the use of HTML as ground-truth representation format for TSR.",
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                "text": "Lysak, et al.",
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          "text": "M. Lysak, et al."
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                "text": "Comparison between HTML and OTSL table structure representation: (A)",
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                "text": "minimal graphical representation of table structure using rectangular layout, (C)",
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                "text": "HTML representation, (D) OTSL representation. This example demonstrates many of",
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                "text": "its reduced sequence length (55 versus 30) and a enhanced internal structure (variable",
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                "text": "of a particular example table. Public table-structure data sets such as PubTab-",
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                "text": "Net [22], and FinTabNet [21], which were created in a semi-automated way from",
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                "text": "the use of HTML as ground-truth representation format for TSR.",
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          "text": "Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR."
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          "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,",
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          "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary",
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          "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-",
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          "text": "gular layout) and a strict syntax with rules that only look backwards. The latter",
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          "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison",
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          "text": "The paper is structured as follows. In section 2, we give an overview of the",
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          "text": "the current HTML table encoding (popularised by PubTabNet and FinTabNet)",
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          "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-",
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          "text": "cludes the language definition, syntax rules and error-correction procedures. In",
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          "text": "Former models trained on HTML and ultimately demonstrate the advantages",
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          "text": "of using OTSL. Finally, in section 6 we conclude our work and outline next",
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          "text": "Related Work",
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          "text": "Approaches to formalize the logical structure and layout of tables in electronic",
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          "text": "documents date back more than two decades [16]. In the recent past, a wide",
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          "text": "variety of computer vision methods have been explored to tackle the prob-",
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          "text": "rows and spanning cells in a given table. Broadly speaking, the current deep-",
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          "text": "learning based approaches fall into three categories: object detection (OD) meth-",
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          "text": "ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence",
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          "text": "(Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table-",
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          "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name",
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                "text": "The main contribution of this paper is the introduction of a new optimised ta-",
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                "text": "ble structure language (OTSL), specifically designed to describe table-structure",
                "orig": "ble structure language (OTSL), specifically designed to describe table-structure",
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                "text": "in an compact and structured way for Im2Seq models. OTSL has a number of",
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                "text": "key features, which make it very attractive to use in Im2Seq models. Specifically,",
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                "confidence": 1.0,
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                "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary",
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                "text": "which yields short sequence length, strong inherent structure (e.g. strict rectan-",
                "orig": "which yields short sequence length, strong inherent structure (e.g. strict rectan-",
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                "confidence": 1.0,
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                "text": "gular layout) and a strict syntax with rules that only look backwards. The latter",
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                "confidence": 1.0,
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                "rgba": {
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                "text": "allows for syntax validation during inference and ensures a syntactically correct",
                "orig": "allows for syntax validation during inference and ensures a syntactically correct",
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                "confidence": 1.0,
                "from_ocr": false
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                "text": "table-structure. These OTSL features are illustrated in Figure 1, in comparison",
                "orig": "table-structure. These OTSL features are illustrated in Figure 1, in comparison",
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                "from_ocr": false
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                "text": "to HTML.",
                "orig": "to HTML.",
                "text_direction": "left_to_right",
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                "text": "The paper is structured as follows. In section 2, we give an overview of the",
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                "text": "latest developments in table-structure reconstruction. In section 3 we review",
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                "text": "and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in-",
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                "text": "compared to other languages such as HTML, OTSL has a minimized vocabulary",
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                "text": "image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name",
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          "text": "Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell."
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          "text": "Other work [20] aims at predicting a grid for each table and deciding which cells",
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          "text": "must be merged using an attention network. Im2Seq methods cast the problem",
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          "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-",
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          "text": "structure representation language, which is often implemented with standard",
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          "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods",
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          "text": "have a natural advantage over the OD and GNN methods by virtue of directly",
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          "text": "predicting the table-structure. As such, no post-processing or rules are needed",
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          "text": "in order to obtain the table-structure, which is necessary with OD and GNN",
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          "text": "approaches. In practice, this is not entirely true, because a predicted sequence",
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          "text": "of table-structure markup does not necessarily have to be syntactically correct.",
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          "text": "Hence, depending on the quality of the predicted sequence, some post-processing",
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          "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.",
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          "text": "Within the Im2Seq method, we find several popular models, namely the",
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          "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye",
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          "text": "of HTML tags. For each decoded table cell (",
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          "text": "), the attention is passed to",
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          "orig": "cell decoder",
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          "text": "to predict the content with an embedded OCR approach. The",
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          "text": "latter makes it susceptible to transcription errors in the cell content of the table.",
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          "text": "TableFormer address this reliance on OCR and uses two transformer decoders for",
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          "text": "HTML structure and cell bounding box prediction in an end-to-end architecture.",
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          "text": "The predicted cell bounding box is then used to extract text tokens from an",
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          "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter",
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          "text": "proposes a compact double-matrix representation of table rows and columns",
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          "text": "to do error detection and error correction of HTML structure sequences based",
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          "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method",
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          "text": "using bidirectional LSTMs to predict LaTeX code. Kayal",
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          "text": "[5]",
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          "text": "introduces Gated",
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          "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to",
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          "text": "extract content.",
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          "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a",
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          "text": "full end-to-end network design that can output the final table structure without",
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          "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors",
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          "text": "to investigate if the performance (both in accuracy and inference time) can",
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          "text": "be further improved by optimising the table structure representation language.",
          "orig": "be further improved by optimising the table structure representation language.",
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          "text": "We believe this is a necessary step before further improving neural network",
          "orig": "We believe this is a necessary step before further improving neural network",
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          "text": "architectures for this task.",
          "orig": "architectures for this task.",
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          "text": "3",
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          "text": "Problem Statement",
          "orig": "Problem Statement",
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          "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.",
          "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.",
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          "text": "Given an image of a table, the Im2Seq model predicts the structure of the table",
          "orig": "Given an image of a table, the Im2Seq model predicts the structure of the table",
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          "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-",
          "orig": "by generating a sequence of tokens. These tokens originate from a finite vocab-",
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                "text": "M.",
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                "text": "Lysak, et al.",
                "orig": "Lysak, et al.",
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                "text": "Other work [20] aims at predicting a grid for each table and deciding which cells",
                "orig": "Other work [20] aims at predicting a grid for each table and deciding which cells",
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                "text": "must be merged using an attention network. Im2Seq methods cast the problem",
                "orig": "must be merged using an attention network. Im2Seq methods cast the problem",
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                "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-",
                "orig": "as a sequence generation task [4,5,9,22], and therefore need an internal table-",
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                "text": "structure representation language, which is often implemented with standard",
                "orig": "structure representation language, which is often implemented with standard",
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                "text": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods",
                "orig": "markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods",
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                "text": "have a natural advantage over the OD and GNN methods by virtue of directly",
                "orig": "have a natural advantage over the OD and GNN methods by virtue of directly",
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                "text": "predicting the table-structure. As such, no post-processing or rules are needed",
                "orig": "predicting the table-structure. As such, no post-processing or rules are needed",
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                "text": "in order to obtain the table-structure, which is necessary with OD and GNN",
                "orig": "in order to obtain the table-structure, which is necessary with OD and GNN",
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                "text": "approaches. In practice, this is not entirely true, because a predicted sequence",
                "orig": "approaches. In practice, this is not entirely true, because a predicted sequence",
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                "text": "of table-structure markup does not necessarily have to be syntactically correct.",
                "orig": "of table-structure markup does not necessarily have to be syntactically correct.",
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                "text": "Hence, depending on the quality of the predicted sequence, some post-processing",
                "orig": "Hence, depending on the quality of the predicted sequence, some post-processing",
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                "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.",
                "orig": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.",
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                "text": "Within the Im2Seq method, we find several popular models, namely the",
                "orig": "Within the Im2Seq method, we find several popular models, namely the",
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                "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye",
                "orig": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye",
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                "confidence": 1.0,
                "from_ocr": false
              },
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                },
                "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders",
                "orig": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "text": "to predict a table in HTML representation. The",
                "orig": "to predict a table in HTML representation. The",
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                "text": "tag decoder",
                "orig": "tag decoder",
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                "text": "predicts a sequence",
                "orig": "predicts a sequence",
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                "text": "to predict the content with an embedded OCR approach. The",
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                "text": "latter makes it susceptible to transcription errors in the cell content of the table.",
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                "text": "TableFormer address this reliance on OCR and uses two transformer decoders for",
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                "text": "The predicted cell bounding box is then used to extract text tokens from an",
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                "text": "originating (digital) PDF page, circumventing any need for OCR. TabSplitter",
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                "text": "proposes a compact double-matrix representation of table rows and columns",
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                "text": "to do error detection and error correction of HTML structure sequences based",
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                "text": "on predictions from [19]. This compact double-matrix representation can not be",
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                "text": "used directly by the Img2seq model training, so the model uses HTML as an",
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                "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method",
                "orig": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method",
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                "text": "using bidirectional LSTMs to predict LaTeX code. Kayal",
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                "text": "introduces Gated",
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                "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to",
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                "text": "extract content.",
                "orig": "extract content.",
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                "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a",
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                "text": "full end-to-end network design that can output the final table structure without",
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                "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated",
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                "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors",
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                "text": "to investigate if the performance (both in accuracy and inference time) can",
                "orig": "to investigate if the performance (both in accuracy and inference time) can",
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                "text": "be further improved by optimising the table structure representation language.",
                "orig": "be further improved by optimising the table structure representation language.",
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                "text": "We believe this is a necessary step before further improving neural network",
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                "text": "architectures for this task.",
                "orig": "architectures for this task.",
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                "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.",
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                "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-",
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                "text": "Other work [20] aims at predicting a grid for each table and deciding which cells",
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                "text": "must be merged using an attention network. Im2Seq methods cast the problem",
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                "text": "as a sequence generation task [4,5,9,22], and therefore need an internal table-",
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                "text": "structure representation language, which is often implemented with standard",
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                "text": "needs to be performed to ensure a syntactically valid (let alone correct) sequence.",
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          "text": "Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence."
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                "text": "Within the Im2Seq method, we find several popular models, namely the",
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                "text": "encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye",
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                "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders",
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                "text": "on predictions from [19]. This compact double-matrix representation can not be",
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                "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method",
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                "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to",
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          "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."
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                "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a",
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                "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors",
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                "text": "to investigate if the performance (both in accuracy and inference time) can",
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                "text": "be further improved by optimising the table structure representation language.",
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                "text": "We believe this is a necessary step before further improving neural network",
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                "text": "by generating a sequence of tokens. These tokens originate from a finite vocab-",
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          "text": "All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab-"
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                "text": "et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders",
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                "text": "to predict the content with an embedded OCR approach. The",
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                "text": "to do error detection and error correction of HTML structure sequences based",
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                "text": "on predictions from [19]. This compact double-matrix representation can not be",
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                "text": "used directly by the Img2seq model training, so the model uses HTML as an",
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                "text": "intermediate form. Chi et. al. [4] introduce a data set and a baseline method",
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                "text": "using bidirectional LSTMs to predict LaTeX code. Kayal",
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                "text": "introduces Gated",
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                "text": "ResNet transformers to predict LaTeX code, and a separate OCR module to",
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          "text": "Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content."
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                "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a",
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                "text": "full end-to-end network design that can output the final table structure without",
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                "text": "pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated",
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                "text": "to deliver state-of-the-art prediction accuracy [9]. This motivated the authors",
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                "text": "to investigate if the performance (both in accuracy and inference time) can",
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                "text": "be further improved by optimising the table structure representation language.",
                "orig": "be further improved by optimising the table structure representation language.",
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                "text": "We believe this is a necessary step before further improving neural network",
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                "text": "architectures for this task.",
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          "text": "Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task."
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                "text": "3",
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                "text": "Problem Statement",
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          "text": "3 Problem Statement"
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                "text": "All known Im2Seq based models for TSR fundamentally work in similar ways.",
                "orig": "All known Im2Seq based models for TSR fundamentally work in similar ways.",
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          "text": "ing Im2Seq models on HTML table structure generation from images. On the",
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          "text": "in later rows on the same column or even complete loss of vertical alignment, as",
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          "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,",
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          "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-",
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          "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously",
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          "text": "impact the TSR model performance, since they reflect not only in the task of",
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          "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,",
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          "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is",
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          "text": "designed to express table structure with a minimized vocabulary and a simple",
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          "text": "set of rules, which are both significantly reduced compared to HTML. At the",
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          "text": "same time, OTSL enables easy error detection and correction during sequence",
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          "text": "generation. We further demonstrate how the compact structure representation",
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          "text": "and minimized sequence length improves prediction accuracy and inference time",
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          "text": "in the TableFormer architecture.",
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          "text": "Language Definition",
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          "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines",
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          "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D",
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          "text": "A notable attribute of OTSL is that it has the capability of achieving lossless",
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                "text": "Lysak, et al.",
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                "text": "complex syntax rules, simply to deliver valid output.",
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                "text": "In practice, we observe two major issues with prediction quality when train-",
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                "text": "ing Im2Seq models on HTML table structure generation from images. On the",
                "orig": "ing Im2Seq models on HTML table structure generation from images. On the",
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                "text": "one hand, we find that on large tables, the visual attention of the model often",
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                "text": "starts to drift and is not accurately moving forward cell by cell anymore. This",
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                "text": "manifests itself in either in an increasing",
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                "text": "for proposed table-cells",
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                "text": "in later rows on the same column or even complete loss of vertical alignment, as",
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                "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,",
                "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,",
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                "text": "but clearly undesired. On the other hand, we find many instances of predictions",
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                "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-",
                "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-",
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                "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously",
                "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously",
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                "from_ocr": false
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                "text": "impact the TSR model performance, since they reflect not only in the task of",
                "orig": "impact the TSR model performance, since they reflect not only in the task of",
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                "text": "pure structure recognition but also in the equally crucial recognition or matching",
                "orig": "pure structure recognition but also in the equally crucial recognition or matching",
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                "text": "of table cell content.",
                "orig": "of table cell content.",
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                "text": "Optimised Table Structure Language",
                "orig": "Optimised Table Structure Language",
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                "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,",
                "orig": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,",
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                "text": "set of rules, which are both significantly reduced compared to HTML. At the",
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                "text": "generation. We further demonstrate how the compact structure representation",
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                "text": "and minimized sequence length improves prediction accuracy and inference time",
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                "text": "in the TableFormer architecture.",
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                "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines",
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                "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D",
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                "text": "complex syntax rules, simply to deliver valid output.",
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                "text": "ing Im2Seq models on HTML table structure generation from images. On the",
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                "text": "in later rows on the same column or even complete loss of vertical alignment, as",
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                "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,",
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                "text": "but clearly undesired. On the other hand, we find many instances of predictions",
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                "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-",
                "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-",
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                "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously",
                "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously",
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                "text": "impact the TSR model performance, since they reflect not only in the task of",
                "orig": "impact the TSR model performance, since they reflect not only in the task of",
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                "text": "pure structure recognition but also in the equally crucial recognition or matching",
                "orig": "pure structure recognition but also in the equally crucial recognition or matching",
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          "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."
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          "text": "4 Optimised Table Structure Language"
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                "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,",
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                "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is",
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                "text": "designed to express table structure with a minimized vocabulary and a simple",
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                "text": "set of rules, which are both significantly reduced compared to HTML. At the",
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                "text": "same time, OTSL enables easy error detection and correction during sequence",
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                "text": "generation. We further demonstrate how the compact structure representation",
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                "text": "and minimized sequence length improves prediction accuracy and inference time",
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                "text": "in the TableFormer architecture.",
                "orig": "in the TableFormer architecture.",
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          "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."
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          "text": "4.1 Language Definition"
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                "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines",
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                "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D",
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          "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."
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                "text": "A notable attribute of OTSL is that it has the capability of achieving lossless",
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          "text": "A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML."
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                "text": "generation. Implicitly, this also means that Im2Seq models need to learn these",
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                "text": "complex syntax rules, simply to deliver valid output.",
                "orig": "complex syntax rules, simply to deliver valid output.",
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          "text": "generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output."
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                "text": "In practice, we observe two major issues with prediction quality when train-",
                "orig": "In practice, we observe two major issues with prediction quality when train-",
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                "confidence": 1.0,
                "from_ocr": false
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                "text": "ing Im2Seq models on HTML table structure generation from images. On the",
                "orig": "ing Im2Seq models on HTML table structure generation from images. On the",
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                "confidence": 1.0,
                "from_ocr": false
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                "text": "one hand, we find that on large tables, the visual attention of the model often",
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                "text": "starts to drift and is not accurately moving forward cell by cell anymore. This",
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                "text": "manifests itself in either in an increasing",
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                "confidence": 1.0,
                "from_ocr": false
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                "text": "location drift",
                "orig": "location drift",
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                "confidence": 1.0,
                "from_ocr": false
              },
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                },
                "text": "for proposed table-cells",
                "orig": "for proposed table-cells",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 12,
                "rgba": {
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                },
                "text": "in later rows on the same column or even complete loss of vertical alignment, as",
                "orig": "in later rows on the same column or even complete loss of vertical alignment, as",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "index": 13,
                "rgba": {
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                  "r_y1": 224.01,
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                  "r_y3": 215.21,
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                },
                "text": "illustrated in Figure 5. Addressing this with post-processing is partially possible,",
                "orig": "illustrated in Figure 5. Addressing this with post-processing is partially possible,",
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                "confidence": 1.0,
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              },
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                "index": 14,
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                },
                "text": "but clearly undesired. On the other hand, we find many instances of predictions",
                "orig": "but clearly undesired. On the other hand, we find many instances of predictions",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                  "r_y3": 239.12,
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                },
                "text": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-",
                "orig": "with structural inconsistencies or plain invalid HTML output, as shown in Fig-",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 16,
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                },
                "text": "ure 6, which are nearly impossible to properly correct. Both problems seriously",
                "orig": "ure 6, which are nearly impossible to properly correct. Both problems seriously",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "text": "impact the TSR model performance, since they reflect not only in the task of",
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                "text": "pure structure recognition but also in the equally crucial recognition or matching",
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          "text": "In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content."
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          "text": "4 Optimised Table Structure Language"
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                "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before,",
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                "text": "we propose here our Optimised Table Structure Language (OTSL). OTSL is",
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                "text": "designed to express table structure with a minimized vocabulary and a simple",
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                "text": "set of rules, which are both significantly reduced compared to HTML. At the",
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                "text": "same time, OTSL enables easy error detection and correction during sequence",
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                "text": "generation. We further demonstrate how the compact structure representation",
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                "text": "and minimized sequence length improves prediction accuracy and inference time",
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                "text": "in the TableFormer architecture.",
                "orig": "in the TableFormer architecture.",
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          "text": "To mitigate the issues with HTML in Im2Seq-based TSR models laid out before, we propose here our Optimised Table Structure Language (OTSL). OTSL is designed to express table structure with a minimized vocabulary and a simple set of rules, which are both significantly reduced compared to HTML. At the same time, OTSL enables easy error detection and correction during sequence generation. We further demonstrate how the compact structure representation and minimized sequence length improves prediction accuracy and inference time in the TableFormer architecture."
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                "text": "Language Definition",
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          "text": "4.1 Language Definition"
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                "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines",
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                "text": "only 5 tokens that directly describe a tabular structure based on an atomic 2D",
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          "text": "In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines only 5 tokens that directly describe a tabular structure based on an atomic 2D grid."
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          "text": "E",
          "orig": "E",
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          "confidence": 1.0,
          "from_ocr": false
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          "text": "4.2",
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          "text": "Language Syntax",
          "orig": "Language Syntax",
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          "text": "The OTSL representation follows these syntax rules:",
          "orig": "The OTSL representation follows these syntax rules:",
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          "text": "1.",
          "orig": "1.",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
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          "text": "Left-looking cell rule",
          "orig": "Left-looking cell rule",
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          "from_ocr": false
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          "text": ": The left neighbour of an \"L\" cell must be either",
          "orig": ": The left neighbour of an \"L\" cell must be either",
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          "from_ocr": false
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          "text": "another \"L\" cell or a \"C\" cell.",
          "orig": "another \"L\" cell or a \"C\" cell.",
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          "confidence": 1.0,
          "from_ocr": false
        },
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          "text": "2.",
          "orig": "2.",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          "text": "Up-looking cell rule",
          "orig": "Up-looking cell rule",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          "text": ": The upper neighbour of a \"U\" cell must be either",
          "orig": ": The upper neighbour of a \"U\" cell must be either",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          "text": "another \"U\" cell or a \"C\" cell.",
          "orig": "another \"U\" cell or a \"C\" cell.",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          "text": "3.",
          "orig": "3.",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          "text": "Cross cell rule",
          "orig": "Cross cell rule",
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          "confidence": 1.0,
          "from_ocr": false
        },
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          "text": ":",
          "orig": ":",
          "text_direction": "left_to_right",
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          "from_ocr": false
        },
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          "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"",
          "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"",
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          "confidence": 1.0,
          "from_ocr": false
        },
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          "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell",
          "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          },
          "text": "or an \"L\" cell.",
          "orig": "or an \"L\" cell.",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          "text": "4.",
          "orig": "4.",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          "text": "First row rule",
          "orig": "First row rule",
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        },
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          "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.",
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        },
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          },
          "text": "5.",
          "orig": "5.",
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          "confidence": 1.0,
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          "text": "First column rule",
          "orig": "First column rule",
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        },
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          "text": ": Only \"U\" cells and \"C\" cells are allowed in the first",
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          "text": "column.",
          "orig": "column.",
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          "confidence": 1.0,
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        },
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          "text": "6.",
          "orig": "6.",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          },
          "text": "Rectangular rule",
          "orig": "Rectangular rule",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          "index": 101,
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          "text": ": The table representation is always rectangular - all rows",
          "orig": ": The table representation is always rectangular - all rows",
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          "from_ocr": false
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          "text": "must have an equal number of tokens, terminated with \"NL\" token.",
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        },
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          },
          "text": "The application of these rules gives OTSL a set of unique properties. First",
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          "text_direction": "left_to_right",
          "confidence": 1.0,
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        },
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          },
          "text": "of all, the OTSL enforces a strictly rectangular structure representation, where",
          "orig": "of all, the OTSL enforces a strictly rectangular structure representation, where",
          "text_direction": "left_to_right",
          "confidence": 1.0,
          "from_ocr": false
        },
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          },
          "text": "every new-line token starts a new row. As a consequence, all rows and all columns",
          "orig": "every new-line token starts a new row. As a consequence, all rows and all columns",
          "text_direction": "left_to_right",
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          "from_ocr": false
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          "text": "have exactly the same number of tokens, irrespective of cell spans. Secondly, the",
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          "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every",
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          "text": "at the previously predicted sequence. As such, OTSL can guarantee that every",
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          "text": "These characteristics can be easily learned by sequence generator networks,",
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          "text": "as we demonstrate further below. We find strong indications that this pattern",
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                "text": "Left-looking cell rule",
                "orig": "Left-looking cell rule",
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                "confidence": 1.0,
                "from_ocr": false
              },
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                  "coord_origin": "TOPLEFT"
                },
                "text": ": The left neighbour of an \"L\" cell must be either",
                "orig": ": The left neighbour of an \"L\" cell must be either",
                "text_direction": "left_to_right",
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                "from_ocr": false
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                  "r_y1": 367.93,
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                "from_ocr": false
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                  "coord_origin": "TOPLEFT"
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                "orig": "2.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "rgba": {
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                  "g": 0,
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                  "a": 255
                },
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                  "r_y2": 371.08,
                  "r_x3": 151.7,
                  "r_y3": 371.08,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Up-looking cell rule",
                "orig": "Up-looking cell rule",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "rgba": {
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                  "r_y2": 371.09,
                  "r_x3": 252.11,
                  "r_y3": 371.09,
                  "coord_origin": "TOPLEFT"
                },
                "text": ": The upper neighbour of a \"U\" cell must be either",
                "orig": ": The upper neighbour of a \"U\" cell must be either",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
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                  "r_y1": 391.85,
                  "r_x2": 284.84,
                  "r_y2": 383.05,
                  "r_x3": 151.7,
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                  "coord_origin": "TOPLEFT"
                },
                "text": "another \"U\" cell or a \"C\" cell.",
                "orig": "another \"U\" cell or a \"C\" cell.",
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                "from_ocr": false
              }
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                "text": "3.",
                "orig": "3.",
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                "text": "Cross cell rule",
                "orig": "Cross cell rule",
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                "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"",
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                "text": "or an \"L\" cell.",
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                "orig": "First row rule",
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                "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.",
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                "orig": "First column rule",
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                "text": "Rectangular rule",
                "orig": "Rectangular rule",
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                "text": ": The table representation is always rectangular - all rows",
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                "text": "The application of these rules gives OTSL a set of unique properties. First",
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                "text": "of all, the OTSL enforces a strictly rectangular structure representation, where",
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                "text": "every new-line token starts a new row. As a consequence, all rows and all columns",
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                "text": "OTSL representation is unambiguous: Every table structure is represented in one",
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                "text": "way. In this representation every table cell corresponds to a \"C\"-cell token, which",
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                "text": "in case of spans is always located in the top-left corner of the table cell definition.",
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                "text": "Third, OTSL syntax rules are only backward-looking. As a consequence, every",
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                "text": "at the previously predicted sequence. As such, OTSL can guarantee that every",
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                "text": "predicted sequence is syntactically valid.",
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              }
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                "text": "These characteristics can be easily learned by sequence generator networks,",
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          "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."
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          "text": "3. Cross cell rule :"
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                "text": "These characteristics can be easily learned by sequence generator networks,",
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                "text": "as we demonstrate further below. We find strong indications that this pattern",
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                    "orig": "D",
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                    "from_ocr": false
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            "confidence": 0.923,
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                "text": "4.2",
                "orig": "4.2",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
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                "text": "Language Syntax",
                "orig": "Language Syntax",
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              }
            ],
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          },
          "text": "4.2 Language Syntax"
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                "text": "The OTSL representation follows these syntax rules:",
                "orig": "The OTSL representation follows these syntax rules:",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              }
            ],
            "children": []
          },
          "text": "The OTSL representation follows these syntax rules:"
        },
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          "label": "list_item",
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                },
                "text": "1.",
                "orig": "1.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                  "r_y2": 347.17,
                  "r_x3": 151.7,
                  "r_y3": 347.17,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Left-looking cell rule",
                "orig": "Left-looking cell rule",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 80,
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                  "r_y1": 355.98,
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                  "r_x3": 257.38,
                  "r_y3": 347.18,
                  "coord_origin": "TOPLEFT"
                },
                "text": ": The left neighbour of an \"L\" cell must be either",
                "orig": ": The left neighbour of an \"L\" cell must be either",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "index": 81,
                "rgba": {
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                  "r_x1": 283.59,
                  "r_y1": 367.93,
                  "r_x2": 283.59,
                  "r_y2": 359.14,
                  "r_x3": 151.7,
                  "r_y3": 359.14,
                  "coord_origin": "TOPLEFT"
                },
                "text": "another \"L\" cell or a \"C\" cell.",
                "orig": "another \"L\" cell or a \"C\" cell.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              }
            ],
            "children": []
          },
          "text": "1. Left-looking cell rule : The left neighbour of an \"L\" cell must be either another \"L\" cell or a \"C\" cell."
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          "page_no": 6,
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                "text": "2.",
                "orig": "2.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                  "r_y2": 371.08,
                  "r_x3": 151.7,
                  "r_y3": 371.08,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Up-looking cell rule",
                "orig": "Up-looking cell rule",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                  "r_y1": 379.89,
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                  "r_y2": 371.09,
                  "r_x3": 252.11,
                  "r_y3": 371.09,
                  "coord_origin": "TOPLEFT"
                },
                "text": ": The upper neighbour of a \"U\" cell must be either",
                "orig": ": The upper neighbour of a \"U\" cell must be either",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "index": 85,
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                  "r_y1": 391.85,
                  "r_x2": 284.84,
                  "r_y2": 383.05,
                  "r_x3": 151.7,
                  "r_y3": 383.05,
                  "coord_origin": "TOPLEFT"
                },
                "text": "another \"U\" cell or a \"C\" cell.",
                "orig": "another \"U\" cell or a \"C\" cell.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              }
            ],
            "children": []
          },
          "text": "2. Up-looking cell rule : The upper neighbour of a \"U\" cell must be either another \"U\" cell or a \"C\" cell."
        },
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          "page_no": 6,
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            },
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                  "r_y1": 403.8,
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                  "r_y2": 395.01,
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                  "r_y3": 395.01,
                  "coord_origin": "TOPLEFT"
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                "text": "3.",
                "orig": "3.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
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                  "r_y1": 403.8,
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                  "r_y2": 395.0,
                  "r_x3": 151.7,
                  "r_y3": 395.0,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Cross cell rule",
                "orig": "Cross cell rule",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "index": 88,
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                  "r_y1": 403.8,
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                  "r_x3": 223.31,
                  "r_y3": 395.01,
                  "coord_origin": "TOPLEFT"
                },
                "text": ":",
                "orig": ":",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              }
            ],
            "children": []
          },
          "text": "3. Cross cell rule :"
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                "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"",
                "orig": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\"",
                "text_direction": "left_to_right",
                "confidence": 1.0,
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                  "r_y3": 418.92,
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                },
                "text": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell",
                "orig": "cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
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                  "r_y1": 439.67,
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                  "r_y3": 430.88,
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                },
                "text": "or an \"L\" cell.",
                "orig": "or an \"L\" cell.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              }
            ],
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          },
          "text": "The left neighbour of an \"X\" cell must be either another \"X\" cell or a \"U\" cell, and the upper neighbour of an \"X\" cell must be either another \"X\" cell or an \"L\" cell."
        },
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            },
            "confidence": 0.926,
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                "orig": "4.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
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                  "r_y1": 451.63,
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                  "r_y2": 442.83,
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                  "r_y3": 442.83,
                  "coord_origin": "TOPLEFT"
                },
                "text": "First row rule",
                "orig": "First row rule",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "index": 94,
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                },
                "text": ": Only \"L\" cells and \"C\" cells are allowed in the first row.",
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                "confidence": 1.0,
                "from_ocr": false
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          },
          "text": "4. First row rule : Only \"L\" cells and \"C\" cells are allowed in the first row."
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          "page_no": 6,
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                "text": "5.",
                "orig": "5.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
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                  "r_x3": 151.7,
                  "r_y3": 454.78,
                  "coord_origin": "TOPLEFT"
                },
                "text": "First column rule",
                "orig": "First column rule",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "index": 97,
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                  "r_y2": 454.79,
                  "r_x3": 240.72,
                  "r_y3": 454.79,
                  "coord_origin": "TOPLEFT"
                },
                "text": ": Only \"U\" cells and \"C\" cells are allowed in the first",
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                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
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                  "r_y2": 466.75,
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                  "coord_origin": "TOPLEFT"
                },
                "text": "column.",
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                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              }
            ],
            "children": []
          },
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          "text": "an invalid sequence and thus increase the chances for accurate predictions. Such",
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          "text": "heuristics can be applied either after the prediction of each token, or at the end",
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          "text": "on the entire predicted sequence. For example a simple heuristic which can cor-",
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          "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the",
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          "text": "Experiments",
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          "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we",
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          "text": "conducted a series of experiments based on the TableFormer model (Figure 4)",
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          "text": "with two objectives: Firstly we evaluate the prediction quality and performance",
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          "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on",
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          "text": "the",
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          "text": "canonical",
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          "text": "PubTabNet data set. Secondly we pick the best hyper-parameters",
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          "text": "found in the first step and evaluate how OTSL impacts the performance of",
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          "text": "TableFormer after training on other publicly available data sets (FinTabNet,",
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          "text": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-",
          "orig": "PubTables-1M [14]). The ground truth (GT) from all data sets has been con-",
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          "from_ocr": false
        },
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          "text": "verted into OTSL format for this purpose, and will be made publicly available.",
          "orig": "verted into OTSL format for this purpose, and will be made publicly available.",
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          "text": "Fig. 4.",
          "orig": "Fig. 4.",
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          "text": "Architecture sketch of the TableFormer model, which is a representative for the",
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          "text": "Im2Seq approach.",
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          "text": "1.",
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          "text": "Item",
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          "text": "Amount",
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          "text": "Names",
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          "text": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for",
          "orig": "We rely on standard metrics such as Tree Edit Distance score (TEDs) for",
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          "confidence": 1.0,
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          "text": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-",
          "orig": "table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter-",
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          "text": "section Over Union (IOU) threshold for the bounding-box predictions of table",
          "orig": "section Over Union (IOU) threshold for the bounding-box predictions of table",
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          "confidence": 1.0,
          "from_ocr": false
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          "text": "cells. The predicted OTSL structures were converted back to HTML format in",
          "orig": "cells. The predicted OTSL structures were converted back to HTML format in",
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          "confidence": 1.0,
          "from_ocr": false
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      "has_words": false,
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                "text": "Lysak, et al.",
                "orig": "Lysak, et al.",
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              }
            ],
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                "text": "reduces significantly the column drift seen in the HTML based models (see Fig-",
                "orig": "reduces significantly the column drift seen in the HTML based models (see Fig-",
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                "text": "ure 5).",
                "orig": "ure 5).",
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                "confidence": 1.0,
                "from_ocr": false
              }
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                "text": "4.3",
                "orig": "4.3",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
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                "text": "Error-detection and -mitigation",
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              }
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            "id": 0,
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                "text": "The design of OTSL allows to validate a table structure easily on an unfinished",
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                "confidence": 1.0,
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                "text": "sequence. The detection of an invalid sequence token is a clear indication of a",
                "orig": "sequence. The detection of an invalid sequence token is a clear indication of a",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
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                "text": "prediction mistake, however a valid sequence by itself does not guarantee pre-",
                "orig": "prediction mistake, however a valid sequence by itself does not guarantee pre-",
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                "confidence": 1.0,
                "from_ocr": false
              },
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                "text": "diction correctness. Different heuristics can be used to correct token errors in",
                "orig": "diction correctness. Different heuristics can be used to correct token errors in",
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                "confidence": 1.0,
                "from_ocr": false
              },
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                "rgba": {
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                "text": "an invalid sequence and thus increase the chances for accurate predictions. Such",
                "orig": "an invalid sequence and thus increase the chances for accurate predictions. Such",
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                "confidence": 1.0,
                "from_ocr": false
              },
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                "index": 12,
                "rgba": {
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                  "a": 255
                },
                "rect": {
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                  "r_y1": 250.85,
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                "text": "heuristics can be applied either after the prediction of each token, or at the end",
                "orig": "heuristics can be applied either after the prediction of each token, or at the end",
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                "confidence": 1.0,
                "from_ocr": false
              },
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                "index": 13,
                "rgba": {
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                  "a": 255
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                "rect": {
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                  "r_y1": 262.81,
                  "r_x2": 480.59,
                  "r_y2": 254.01,
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                  "r_y3": 254.01,
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                },
                "text": "on the entire predicted sequence. For example a simple heuristic which can cor-",
                "orig": "on the entire predicted sequence. For example a simple heuristic which can cor-",
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                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 14,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 134.76,
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                  "r_x1": 480.59,
                  "r_y1": 274.77,
                  "r_x2": 480.59,
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                  "r_x3": 134.76,
                  "r_y3": 265.97,
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                },
                "text": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the",
                "orig": "rect the predicted OTSL sequence on-the-fly is to verify if the token with the",
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                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 15,
                "rgba": {
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                  "b": 0,
                  "a": 255
                },
                "rect": {
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                  "r_x1": 480.6,
                  "r_y1": 286.72,
                  "r_x2": 480.6,
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                  "r_y3": 277.92,
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                },
                "text": "highest prediction confidence invalidates the predicted sequence, and replace it",
                "orig": "highest prediction confidence invalidates the predicted sequence, and replace it",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 16,
                "rgba": {
                  "r": 0,
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                "rect": {
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                  "r_x1": 469.4,
                  "r_y1": 298.68,
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                  "r_x3": 134.76,
                  "r_y3": 289.88,
                  "coord_origin": "TOPLEFT"
                },
                "text": "by the token with the next highest confidence until OTSL rules are satisfied.",
                "orig": "by the token with the next highest confidence until OTSL rules are satisfied.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              }
            ],
            "children": []
          },
          {
            "id": 3,
            "label": "section_header",
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                },
                "text": "5",
                "orig": "5",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 18,
                "rgba": {
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                "text": "Experiments",
                "orig": "Experiments",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              }
            ],
            "children": []
          },
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            "id": 1,
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                },
                "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we",
                "orig": "To evaluate the impact of OTSL on prediction accuracy and inference times, we",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "rgba": {
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                "text": "conducted a series of experiments based on the TableFormer model (Figure 4)",
                "orig": "conducted a series of experiments based on the TableFormer model (Figure 4)",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "rgba": {
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                },
                "text": "with two objectives: Firstly we evaluate the prediction quality and performance",
                "orig": "with two objectives: Firstly we evaluate the prediction quality and performance",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                  "r_y2": 383.11,
                  "r_x3": 134.76,
                  "r_y3": 383.11,
                  "coord_origin": "TOPLEFT"
                },
                "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on",
                "orig": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on",
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                "confidence": 1.0,
                "from_ocr": false
              },
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                },
                "text": "the",
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                "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we",
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                "text": "conducted a series of experiments based on the TableFormer model (Figure 4)",
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                "text": "of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on",
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                "text": "verted into OTSL format for this purpose, and will be made publicly available.",
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          "text": "To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available."
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                "orig": "Fig. 4.",
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                "text": "Architecture sketch of the TableFormer model, which is a representative for the",
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          "text": "Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach."
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          "text": "samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples).",
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          "text": "and large tables.",
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                "text": "order to compute the TED score. Inference timing results for all experiments",
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                "text": "evident that with OTSL, our model achieves the same TED score and slightly",
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                "text": "in the inference runtime over HTML.",
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              {
                "index": 3,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 165.22,
                  "r_y0": 133.93,
                  "r_x1": 480.59,
                  "r_y1": 133.93,
                  "r_x2": 480.59,
                  "r_y2": 125.86,
                  "r_x3": 165.22,
                  "r_y3": 125.86,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Visualization of predicted structure and detected bounding boxes on a complex",
                "orig": "Visualization of predicted structure and detected bounding boxes on a complex",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 4,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 134.76,
                  "r_y0": 144.89,
                  "r_x1": 480.59,
                  "r_y1": 144.89,
                  "r_x2": 480.59,
                  "r_y2": 136.82,
                  "r_x3": 134.76,
                  "r_y3": 136.82,
                  "coord_origin": "TOPLEFT"
                },
                "text": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally",
                "orig": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 5,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 134.76,
                  "r_y0": 155.85,
                  "r_x1": 480.59,
                  "r_y1": 155.85,
                  "r_x2": 480.59,
                  "r_y2": 147.78,
                  "r_x3": 134.76,
                  "r_y3": 147.78,
                  "coord_origin": "TOPLEFT"
                },
                "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also",
                "orig": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 6,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 134.76,
                  "r_y0": 166.81,
                  "r_x1": 480.59,
                  "r_y1": 166.81,
                  "r_x2": 480.59,
                  "r_y2": 158.74,
                  "r_x3": 134.76,
                  "r_y3": 158.74,
                  "coord_origin": "TOPLEFT"
                },
                "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and",
                "orig": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 7,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 134.76,
                  "r_y0": 177.77,
                  "r_x1": 415.84,
                  "r_y1": 177.77,
                  "r_x2": 415.84,
                  "r_y2": 169.7,
                  "r_x3": 134.76,
                  "r_y3": 169.7,
                  "coord_origin": "TOPLEFT"
                },
                "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.",
                "orig": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              }
            ],
            "children": []
          },
          {
            "id": 3,
            "label": "picture",
            "bbox": {
              "l": 168.39,
              "t": 181.97,
              "r": 447.35,
              "b": 634.01,
              "coord_origin": "TOPLEFT"
            },
            "confidence": 0.761,
            "cells": [
              {
                "index": 8,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 171.5,
                  "r_y0": 320.36,
                  "r_x1": 177.6,
                  "r_y1": 320.36,
                  "r_x2": 177.6,
                  "r_y2": 312.45,
                  "r_x3": 171.5,
                  "r_y3": 312.45,
                  "coord_origin": "TOPLEFT"
                },
                "text": "B",
                "orig": "B",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 9,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 171.06,
                  "r_y0": 500.57,
                  "r_x1": 177.15,
                  "r_y1": 500.57,
                  "r_x2": 177.15,
                  "r_y2": 492.65,
                  "r_x3": 171.06,
                  "r_y3": 492.65,
                  "coord_origin": "TOPLEFT"
                },
                "text": "C",
                "orig": "C",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 10,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 283.05,
                  "r_y0": 633.42,
                  "r_x1": 374.96,
                  "r_y1": 633.42,
                  "r_x2": 374.96,
                  "r_y2": 627.48,
                  "r_x3": 283.05,
                  "r_y3": 627.48,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Incorrect end of HTML sequence",
                "orig": "Incorrect end of HTML sequence",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 11,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 283.05,
                  "r_y0": 623.29,
                  "r_x1": 398.06,
                  "r_y1": 623.29,
                  "r_x2": 398.06,
                  "r_y2": 617.36,
                  "r_x3": 283.05,
                  "r_y3": 617.36,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Horizontally merged cells are not present",
                "orig": "Horizontally merged cells are not present",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 12,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 293.64,
                  "r_y0": 471.53,
                  "r_x1": 437.51,
                  "r_y1": 471.53,
                  "r_x2": 437.51,
                  "r_y2": 465.6,
                  "r_x3": 293.64,
                  "r_y3": 465.6,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Repeating pattern is well represented in predictions",
                "orig": "Repeating pattern is well represented in predictions",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 13,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 181.89,
                  "r_y0": 294.29,
                  "r_x1": 239.23,
                  "r_y1": 294.29,
                  "r_x2": 239.23,
                  "r_y2": 288.36,
                  "r_x3": 181.89,
                  "r_y3": 288.36,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Repeating pattern of",
                "orig": "Repeating pattern of",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 14,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 181.89,
                  "r_y0": 300.83,
                  "r_x1": 251.53,
                  "r_y1": 300.83,
                  "r_x2": 251.53,
                  "r_y2": 294.89,
                  "r_x3": 181.89,
                  "r_y3": 294.89,
                  "coord_origin": "TOPLEFT"
                },
                "text": "horizontally merged cells",
                "orig": "horizontally merged cells",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 15,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 247.83,
                  "r_y0": 194.82,
                  "r_x1": 253.61,
                  "r_y1": 194.82,
                  "r_x2": 253.61,
                  "r_y2": 184.76,
                  "r_x3": 247.83,
                  "r_y3": 184.76,
                  "coord_origin": "TOPLEFT"
                },
                "text": "A",
                "orig": "A",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 16,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 292.19,
                  "r_y0": 613.74,
                  "r_x1": 381.55,
                  "r_y1": 613.74,
                  "r_x2": 381.55,
                  "r_y2": 607.81,
                  "r_x3": 292.19,
                  "r_y3": 607.81,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Bounding box drifting at the end",
                "orig": "Bounding box drifting at the end",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 17,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 172.28,
                  "r_y0": 403.4,
                  "r_x1": 180.19,
                  "r_y1": 403.4,
                  "r_x2": 180.19,
                  "r_y2": 381.36,
                  "r_x3": 172.28,
                  "r_y3": 381.36,
                  "coord_origin": "TOPLEFT"
                },
                "text": "OTSL",
                "orig": "OTSL",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 18,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 172.28,
                  "r_y0": 578.75,
                  "r_x1": 180.19,
                  "r_y1": 578.75,
                  "r_x2": 180.19,
                  "r_y2": 555.78,
                  "r_x3": 172.28,
                  "r_y3": 555.78,
                  "coord_origin": "TOPLEFT"
                },
                "text": "HTML",
                "orig": "HTML",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              }
            ],
            "children": [
              {
                "id": 18,
                "label": "text",
                "bbox": {
                  "l": 171.5,
                  "t": 312.45,
                  "r": 177.6,
                  "b": 320.36,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 8,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 171.5,
                      "r_y0": 320.36,
                      "r_x1": 177.6,
                      "r_y1": 320.36,
                      "r_x2": 177.6,
                      "r_y2": 312.45,
                      "r_x3": 171.5,
                      "r_y3": 312.45,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "B",
                    "orig": "B",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              },
              {
                "id": 19,
                "label": "text",
                "bbox": {
                  "l": 171.06,
                  "t": 492.65,
                  "r": 177.15,
                  "b": 500.57,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 9,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 171.06,
                      "r_y0": 500.57,
                      "r_x1": 177.15,
                      "r_y1": 500.57,
                      "r_x2": 177.15,
                      "r_y2": 492.65,
                      "r_x3": 171.06,
                      "r_y3": 492.65,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "C",
                    "orig": "C",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              },
              {
                "id": 20,
                "label": "text",
                "bbox": {
                  "l": 283.05,
                  "t": 627.48,
                  "r": 374.96,
                  "b": 633.42,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 10,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 283.05,
                      "r_y0": 633.42,
                      "r_x1": 374.96,
                      "r_y1": 633.42,
                      "r_x2": 374.96,
                      "r_y2": 627.48,
                      "r_x3": 283.05,
                      "r_y3": 627.48,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "Incorrect end of HTML sequence",
                    "orig": "Incorrect end of HTML sequence",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              },
              {
                "id": 21,
                "label": "text",
                "bbox": {
                  "l": 283.05,
                  "t": 617.36,
                  "r": 398.06,
                  "b": 623.29,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 11,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 283.05,
                      "r_y0": 623.29,
                      "r_x1": 398.06,
                      "r_y1": 623.29,
                      "r_x2": 398.06,
                      "r_y2": 617.36,
                      "r_x3": 283.05,
                      "r_y3": 617.36,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "Horizontally merged cells are not present",
                    "orig": "Horizontally merged cells are not present",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              },
              {
                "id": 22,
                "label": "text",
                "bbox": {
                  "l": 293.64,
                  "t": 465.6,
                  "r": 437.51,
                  "b": 471.53,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 12,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 293.64,
                      "r_y0": 471.53,
                      "r_x1": 437.51,
                      "r_y1": 471.53,
                      "r_x2": 437.51,
                      "r_y2": 465.6,
                      "r_x3": 293.64,
                      "r_y3": 465.6,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "Repeating pattern is well represented in predictions",
                    "orig": "Repeating pattern is well represented in predictions",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              },
              {
                "id": 23,
                "label": "text",
                "bbox": {
                  "l": 181.89,
                  "t": 288.36,
                  "r": 239.23,
                  "b": 294.29,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 13,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 181.89,
                      "r_y0": 294.29,
                      "r_x1": 239.23,
                      "r_y1": 294.29,
                      "r_x2": 239.23,
                      "r_y2": 288.36,
                      "r_x3": 181.89,
                      "r_y3": 288.36,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "Repeating pattern of",
                    "orig": "Repeating pattern of",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              },
              {
                "id": 24,
                "label": "text",
                "bbox": {
                  "l": 181.89,
                  "t": 294.89,
                  "r": 251.53,
                  "b": 300.83,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 14,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 181.89,
                      "r_y0": 300.83,
                      "r_x1": 251.53,
                      "r_y1": 300.83,
                      "r_x2": 251.53,
                      "r_y2": 294.89,
                      "r_x3": 181.89,
                      "r_y3": 294.89,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "horizontally merged cells",
                    "orig": "horizontally merged cells",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              },
              {
                "id": 25,
                "label": "text",
                "bbox": {
                  "l": 247.83,
                  "t": 184.76,
                  "r": 253.61,
                  "b": 194.82,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 15,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 247.83,
                      "r_y0": 194.82,
                      "r_x1": 253.61,
                      "r_y1": 194.82,
                      "r_x2": 253.61,
                      "r_y2": 184.76,
                      "r_x3": 247.83,
                      "r_y3": 184.76,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "A",
                    "orig": "A",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              },
              {
                "id": 26,
                "label": "text",
                "bbox": {
                  "l": 292.19,
                  "t": 607.81,
                  "r": 381.55,
                  "b": 613.74,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 16,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 292.19,
                      "r_y0": 613.74,
                      "r_x1": 381.55,
                      "r_y1": 613.74,
                      "r_x2": 381.55,
                      "r_y2": 607.81,
                      "r_x3": 292.19,
                      "r_y3": 607.81,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "Bounding box drifting at the end",
                    "orig": "Bounding box drifting at the end",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              },
              {
                "id": 27,
                "label": "text",
                "bbox": {
                  "l": 172.28,
                  "t": 381.36,
                  "r": 180.19,
                  "b": 403.4,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 17,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 172.28,
                      "r_y0": 403.4,
                      "r_x1": 180.19,
                      "r_y1": 403.4,
                      "r_x2": 180.19,
                      "r_y2": 381.36,
                      "r_x3": 172.28,
                      "r_y3": 381.36,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "OTSL",
                    "orig": "OTSL",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              },
              {
                "id": 28,
                "label": "text",
                "bbox": {
                  "l": 172.28,
                  "t": 555.78,
                  "r": 180.19,
                  "b": 578.75,
                  "coord_origin": "TOPLEFT"
                },
                "confidence": 1.0,
                "cells": [
                  {
                    "index": 18,
                    "rgba": {
                      "r": 0,
                      "g": 0,
                      "b": 0,
                      "a": 255
                    },
                    "rect": {
                      "r_x0": 172.28,
                      "r_y0": 578.75,
                      "r_x1": 180.19,
                      "r_y1": 578.75,
                      "r_x2": 180.19,
                      "r_y2": 555.78,
                      "r_x3": 172.28,
                      "r_y3": 555.78,
                      "coord_origin": "TOPLEFT"
                    },
                    "text": "HTML",
                    "orig": "HTML",
                    "text_direction": "left_to_right",
                    "confidence": 1.0,
                    "from_ocr": false
                  }
                ],
                "children": []
              }
            ]
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                "text": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally",
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                "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and",
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                ],
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              }
            ]
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          "text": "",
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      ],
      "body": [
        {
          "label": "caption",
          "id": 2,
          "page_no": 10,
          "cluster": {
            "id": 2,
            "label": "caption",
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            },
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            "cells": [
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                "index": 2,
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                },
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                  "r_y1": 133.73,
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                },
                "text": "Fig. 6.",
                "orig": "Fig. 6.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 3,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 165.22,
                  "r_y0": 133.93,
                  "r_x1": 480.59,
                  "r_y1": 133.93,
                  "r_x2": 480.59,
                  "r_y2": 125.86,
                  "r_x3": 165.22,
                  "r_y3": 125.86,
                  "coord_origin": "TOPLEFT"
                },
                "text": "Visualization of predicted structure and detected bounding boxes on a complex",
                "orig": "Visualization of predicted structure and detected bounding boxes on a complex",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 4,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 134.76,
                  "r_y0": 144.89,
                  "r_x1": 480.59,
                  "r_y1": 144.89,
                  "r_x2": 480.59,
                  "r_y2": 136.82,
                  "r_x3": 134.76,
                  "r_y3": 136.82,
                  "coord_origin": "TOPLEFT"
                },
                "text": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally",
                "orig": "table with many rows. The OTSL model (B) captured repeating pattern of horizontally",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 5,
                "rgba": {
                  "r": 0,
                  "g": 0,
                  "b": 0,
                  "a": 255
                },
                "rect": {
                  "r_x0": 134.76,
                  "r_y0": 155.85,
                  "r_x1": 480.59,
                  "r_y1": 155.85,
                  "r_x2": 480.59,
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                  "r_x3": 134.76,
                  "r_y3": 147.78,
                  "coord_origin": "TOPLEFT"
                },
                "text": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also",
                "orig": "merged cells from the GT (A), unlike the HTML model (C). The HTML model also",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 6,
                "rgba": {
                  "r": 0,
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                  "b": 0,
                  "a": 255
                },
                "rect": {
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                  "r_x1": 480.59,
                  "r_y1": 166.81,
                  "r_x2": 480.59,
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                  "r_y3": 158.74,
                  "coord_origin": "TOPLEFT"
                },
                "text": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and",
                "orig": "didn\u2019t complete the HTML sequence correctly and displayed a lot more of drift and",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
                "index": 7,
                "rgba": {
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                },
                "rect": {
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                  "r_x1": 415.84,
                  "r_y1": 177.77,
                  "r_x2": 415.84,
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                  "r_y3": 169.7,
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                },
                "text": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.",
                "orig": "overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet.",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
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            ],
            "children": []
          },
          "text": "Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. \"PMC5406406_003_01.png\" PubTabNet."
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            "label": "picture",
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              "coord_origin": "TOPLEFT"
            },
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            "cells": [
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                  "r_x1": 177.6,
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                },
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                "orig": "B",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "index": 9,
                "rgba": {
                  "r": 0,
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                  "b": 0,
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                },
                "rect": {
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                },
                "text": "C",
                "orig": "C",
                "text_direction": "left_to_right",
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              },
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                "index": 10,
                "rgba": {
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                },
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                },
                "text": "Incorrect end of HTML sequence",
                "orig": "Incorrect end of HTML sequence",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                "index": 11,
                "rgba": {
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                },
                "rect": {
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                  "coord_origin": "TOPLEFT"
                },
                "text": "Horizontally merged cells are not present",
                "orig": "Horizontally merged cells are not present",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                },
                "rect": {
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                  "coord_origin": "TOPLEFT"
                },
                "text": "Repeating pattern is well represented in predictions",
                "orig": "Repeating pattern is well represented in predictions",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
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                },
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                  "r_y1": 294.29,
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                  "coord_origin": "TOPLEFT"
                },
                "text": "Repeating pattern of",
                "orig": "Repeating pattern of",
                "text_direction": "left_to_right",
                "confidence": 1.0,
                "from_ocr": false
              },
              {
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                },
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                  "r_y1": 300.83,
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                },
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                "orig": "horizontally merged cells",
                "text_direction": "left_to_right",
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                "from_ocr": false
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                "rgba": {
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                },
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                "text_direction": "left_to_right",
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                "text": "Bounding box drifting at the end",
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                },
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            ],
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                    },
                    "text": "Repeating pattern is well represented in predictions",
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                    "from_ocr": false
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          "text": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)",
          "orig": "structure recognition. arXiv preprint arXiv:1908.04729 (2019)",
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          "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific",
          "orig": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific",
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          "text": "table recognition. In: 2019 International Conference on Document Analysis and",
          "orig": "table recognition. In: 2019 International Conference on Document Analysis and",
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          "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)",
          "orig": "Recognition (ICDAR). pp. 894-901. IEEE (2019)",
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                "text": "Lysak, et al.",
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                "text": "Conclusion",
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                "text": "We demonstrated that representing tables in HTML for the task of table struc-",
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                "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.",
                "orig": "ture recognition with Im2Seq models is ill-suited and has serious limitations.",
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                "text": "Furthermore, we presented in this paper an Optimized Table Structure Language",
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                "text": "(OTSL) which, when compared to commonly used general purpose languages,",
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                "text": "has several key benefits.",
                "orig": "has several key benefits.",
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                "text": "First and foremost, given the same network configuration, inference time for",
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                "text": "a table-structure prediction is about 2 times faster compared to the conventional",
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                "text": "HTML approach. This is primarily owed to the shorter sequence length of the",
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                "text": "OTSL representation. Additional performance benefits can be obtained with",
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                "text": "HPO (hyper parameter optimization). As we demonstrate in our experiments,",
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                "text": "models trained on OTSL can be significantly smaller, e.g. by reducing the number",
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                "text": "of encoder and decoder layers, while preserving comparatively good prediction",
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                "text": "quality. This can further improve inference performance, yielding 5-6 times faster",
                "orig": "quality. This can further improve inference performance, yielding 5-6 times faster",
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                "text": "inference speed in OTSL with prediction quality comparable to models trained",
                "orig": "inference speed in OTSL with prediction quality comparable to models trained",
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                "text": "on HTML (see Table 1).",
                "orig": "on HTML (see Table 1).",
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                "text": "Secondly, OTSL has more inherent structure and a significantly restricted vo-",
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                "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)",
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                "text": "ture recognition with Im2Seq models is ill-suited and has serious limitations.",
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                "text": "Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific",
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                "text": "table recognition. In: 2019 International Conference on Document Analysis and",
                "orig": "table recognition. In: 2019 International Conference on Document Analysis and",
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                "text": "Recognition (ICDAR). pp. 894-901. IEEE (2019)",
                "orig": "Recognition (ICDAR). pp. 894-901. IEEE (2019)",
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          "text": "4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)"
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