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feat: Support AsciiDoc and Markdown input format (#168)

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* updated the base-model and added the asciidoc_backend

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* updated the asciidoc backend

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* Ensure all models work only on valid pages (#158)

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

* ci: run ci also on forks (#160)


---------

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>
Signed-off-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com>

* fix: fix legacy doc ref (#162)

Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>

* docs: typo fix (#155)

* Docs: Typo fix

- Corrected spelling of invidual to automatic

Signed-off-by: ABHISHEK FADAKE <31249309+fadkeabhi@users.noreply.github.com>

* add synchronize event for forks

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

---------

Signed-off-by: ABHISHEK FADAKE <31249309+fadkeabhi@users.noreply.github.com>
Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>
Co-authored-by: Michele Dolfi <dol@zurich.ibm.com>

* feat: add coverage_threshold to skip OCR for small images (#161)

* feat: add coverage_threshold to skip OCR for small images

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* filter individual boxes

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* rename option

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

---------

Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>

* chore: bump version to 2.1.0 [skip ci]

* adding tests for asciidocs

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* first working asciidoc parser

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* reformatted the code

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* fixed the mypy

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* adding test_02.asciidoc

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* Drafting Markdown backend via Marko library

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* work in progress on MD backend

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* md_backend produces docling document with headers, paragraphs, lists

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Improvements in md parsing

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Detecting and assembling tables in markdown in temporary buffers

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Added initial docling table support to md_backend

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Cleaned code, improved logging for MD

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Fixes MyPy requirements, and rest of pre-commit

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Fixed example run_md, added origin info to md_backend

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* working on asciidocs, struggling with ImageRef

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* able to parse the captions and image uri's

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* fixed the mypy

Signed-off-by: Peter Staar <taa@zurich.ibm.com>

* Update all backends with proper filename in DocumentOrigin

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

* Update to docling-core v2.1.0

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

* Fixes for MD Backend, to avoid duplicated text inserts into docling doc

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Fix styling

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

* Added support for code blocks and fenced code in MD

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* cleaned prints

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Added proper processing of in-line textual elements for MD backend

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Fixed issues with duplicated paragraphs and incorrect lists in pptx

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

* Fixed issue with group ordeering in pptx backend, added gebug log into run with formats

Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>

---------

Signed-off-by: Peter Staar <taa@zurich.ibm.com>
Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>
Signed-off-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com>
Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
Signed-off-by: ABHISHEK FADAKE <31249309+fadkeabhi@users.noreply.github.com>
Signed-off-by: Maksym Lysak <mly@zurich.ibm.com>
Co-authored-by: Peter Staar <taa@zurich.ibm.com>
Co-authored-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com>
Co-authored-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
Co-authored-by: ABHISHEK FADAKE <31249309+fadkeabhi@users.noreply.github.com>
Co-authored-by: Michele Dolfi <dol@zurich.ibm.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Maksym Lysak <mly@zurich.ibm.com>
This commit is contained in:
Christoph Auer
2024-10-23 16:14:26 +02:00
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@@ -26,7 +26,6 @@ KDD '22, August 14-18, 2022, Washington, DC, USA © 2022 Copyright held by the o
Figure 1: Four examples of complex page layouts across different document categories
<!-- image -->
<!-- image -->
@@ -59,17 +58,14 @@ A key problem in the process of document conversion is to understand the structu
In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects:
(1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.
(2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.
(3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.
(4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.
- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set.
- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources.
- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours.
- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation.
This enables experimentation with annotation uncertainty and quality control analysis.
(5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.
- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores.
All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns.
@@ -89,7 +85,6 @@ In addition to open intellectual property constraints for the source documents,
Figure 2: Distribution of DocLayNet pages across document categories.
<!-- image -->
to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing "text in the wild".
@@ -112,7 +107,6 @@ The annotation campaign was carried out in four phases. In phase one, we identif
Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.
| | | % of Total | % of Total | % of Total | % of Total | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) |
|----------------|---------|--------------|--------------|--------------|--------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|---------------------------------------------|
| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten |
@@ -131,7 +125,6 @@ Table 1: DocLayNet dataset overview. Along with the frequency of each class labe
Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.
<!-- image -->
we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.
@@ -150,17 +143,12 @@ At first sight, the task of visual document-layout interpretation appears intuit
Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:
(1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.
(2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.
(3) For every Caption , there must be exactly one corresponding Picture or Table .
(4) Connected sub-pictures are grouped together in one Picture object.
(5) Formula numbers are included in a Formula object.
(6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.
- (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object.
- (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement.
- (3) For every Caption , there must be exactly one corresponding Picture or Table .
- (4) Connected sub-pictures are grouped together in one Picture object.
- (5) Formula numbers are included in a Formula object.
- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.
The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.
@@ -168,7 +156,6 @@ Phase 3: Training. After a first trial with a small group of people, we realised
Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.
<!-- image -->
were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.
@@ -177,7 +164,6 @@ Phase 4: Production annotation. The previously selected 80K pages were annotated
Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.
| | human | MRCNN | MRCNN | FRCNN | YOLO |
|----------------|---------|---------|---------|---------|--------|
| | human | R50 | R101 | R101 | v5x6 |
@@ -202,7 +188,6 @@ The primary goal of DocLayNet is to obtain high-quality ML models capable of acc
Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.
<!-- image -->
paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work.
@@ -217,7 +202,6 @@ Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained
Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.
| Class-count | 11 | 6 | 5 | 4 |
|----------------|------|---------|---------|---------|
| Caption | 68 | Text | Text | Text |
@@ -269,7 +253,6 @@ Throughout this paper, we claim that DocLayNet's wider variety of document layou
Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.
| | | Testing on | Testing on | Testing on |
|-----------------|------------|--------------|--------------|--------------|
| Training on | labels | PLN | DB | DLN |
@@ -305,57 +288,35 @@ To date, there is still a significant gap between human and ML accuracy on the l
## REFERENCES
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[2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts rdcl2017. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1404-1410, 2017.
[3] Hervé Déjean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), April 2019. http://sac.founderit.com/.
[4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021.
[5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR) , pages 1-11, 01 2022.
[6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019.
[7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In Proceedings of the 28th International Conference on Computational Linguistics , COLING, pages 949-960. International Committee on Computational Linguistics, dec 2020.
[8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. In SemWebEval@ESWC , 2016.
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[10] Ross B. Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision , ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.
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- [1] Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013.
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- [4] Antonio Jimeno Yepes, Peter Zhong, and Douglas Burdick. Competition on scientific literature parsing. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 605-617. LNCS 12824, SpringerVerlag, sep 2021.
- [5] Logan Markewich, Hao Zhang, Yubin Xing, Navid Lambert-Shirzad, Jiang Zhexin, Roy Lee, Zhi Li, and Seok-Bum Ko. Segmentation for document layout analysis: not dead yet. International Journal on Document Analysis and Recognition (IJDAR) , pages 1-11, 01 2022.
- [6] Xu Zhong, Jianbin Tang, and Antonio Jimeno-Yepes. Publaynet: Largest dataset ever for document layout analysis. In Proceedings of the International Conference on Document Analysis and Recognition , ICDAR, pages 1015-1022, sep 2019.
- [7] Minghao Li, Yiheng Xu, Lei Cui, Shaohan Huang, Furu Wei, Zhoujun Li, and Ming Zhou. Docbank: A benchmark dataset for document layout analysis. In Proceedings of the 28th International Conference on Computational Linguistics , COLING, pages 949-960. International Committee on Computational Linguistics, dec 2020.
- [8] Riaz Ahmad, Muhammad Tanvir Afzal, and M. Qadir. Information extraction from pdf sources based on rule-based system using integrated formats. In SemWebEval@ESWC , 2016.
- [9] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition , CVPR, pages 580-587. IEEE Computer Society, jun 2014.
- [10] Ross B. Girshick. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision , ICCV, pages 1440-1448. IEEE Computer Society, dec 2015.
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- [12] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision , ICCV, pages 2980-2988. IEEE Computer Society, Oct 2017.
- [13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu
Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title
<!-- image -->
Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.
Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021.
[14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR , abs/2005.12872, 2020.
[15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR , abs/1911.09070, 2019.
[16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014.
[17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019.
[18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 1513715145, feb 2021.
[19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery.
[20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.
[21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021.
[22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018.
[23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019.
- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR , abs/2005.12872, 2020.
- [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR , abs/1911.09070, 2019.
- [16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014.
- [17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019.
- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 1513715145, feb 2021.
- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery.
- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.
- [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021.
- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018.
- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019.