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https://github.com/cv-small-snails/Awesome-Table-Recognition

A curated list of resources dedicated to table recognition
https://github.com/cv-small-snails/Awesome-Table-Recognition
List: Awesome-Table-Recognition
dataset ocr ocr-recognition papers papers-with-code table-recognition
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A curated list of resources dedicated to table recognition
Host: GitHub
URL: https://github.com/cv-small-snails/Awesome-Table-Recognition
Owner: cv-small-snails
Created: 2022-02-10T12:16:52.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-01-28T14:00:22.000Z (5 months ago)
Last Synced: 2024-05-23T06:07:13.198Z (about 1 month ago)
Topics: dataset, ocr, ocr-recognition, papers, papers-with-code, table-recognition
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Forks: 47
Open Issues: 3
Metadata Files:
- Readme: README.md
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README

        # Awesome-Table-Recognition

A curated list of resources dedicated to table recognition

## 1. Papers

* *CODE means official code and CODE means not official code

*Conf.* | *Date* | *Title* | *Highlight* | *code* |

:---: | :---: |:--- | :---: | :---: |

IJCAI | 2023 |[Divide Rows and Conquer Cells: Towards Structure Recognition for Large Tables](https://www.ijcai.org/proceedings/2023/0152.pdf) | Sequence | No |

AAAI | 2022 |[LORE: Logical Location Regression Network for Table Structure Recognition](https://arxiv.org/pdf/2303.03730.pdf) | Detection | [*CODE](https://github.com/AlibabaResearch/AdvancedLiterateMachinery)
![](https://img.shields.io/github/stars/AlibabaResearch/AdvancedLiterateMachinery.svg?style=social) |

ACM-MM | 2022 |[TSRFormer: Table Structure Recognition with Transformers](https://arxiv.org/pdf/2208.04921.pdf) | Detection | No |

CVPR | 2022 |[TableFormer: Table Structure Understanding with Transformers.](https://arxiv.org/pdf/2203.01017.pdf) | Sequence | No |

CVPR | 2022 |[Neural Collaborative Graph Machines for Table Structure Recognition](https://arxiv.org/pdf/2111.13359.pdf) | GNN | No |

CVPR | 2022 |[PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://arxiv.org/pdf/2110.00061v3.pdf) | Dataset | [*CODE](https://github.com/microsoft/table-transformer)
![](https://img.shields.io/github/stars/microsoft/table-transformer.svg?style=social) |

arXiv | 2021/5/23 |[Multi-Type-TD-TSR -- Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition: from OCR to Structured Table Representations](https://arxiv.org/pdf/2105.11021.pdf) | Others | [*CODE](https://github.com/Psarpei/Multi-Type-TD-TSR)
![](https://img.shields.io/github/stars/Psarpei/Multi-Type-TD-TSR.svg?style=social)  |

ACM-MM | 2021 |[Show, Read and Reason: Table Structure Recognition with Flexible Context Aggregator](https://dl.acm.org/doi/pdf/10.1145/3474085.3481534?casa_token=zwKnfTC97hwAAAAA:FnqKd6otw4v7LZQq_XvJzk_RLvit8ohPCOIafevI68zxXl1KocUVxIlBg9W1VsHQ57SShne6486BUw) | GNN | No |

ICCV | 2021 |[Parsing Table Structures in the Wild](https://openaccess.thecvf.com/content/ICCV2021/papers/Long_Parsing_Table_Structures_in_the_Wild_ICCV_2021_paper.pdf) | Detection | No |

ICCV | 2021 |[TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition](https://openaccess.thecvf.com/content/ICCV2021/papers/Xue_TGRNet_A_Table_Graph_Reconstruction_Network_for_Table_Structure_Recognition_ICCV_2021_paper.pdf) | GNN | [*CODE](https://github.com/xuewenyuan/TGRNet)
![](https://img.shields.io/github/stars/xuewenyuan/TGRNet.svg?style=social) |

ICDAR Competition | 2021 |[ICDAR 2021 Competition on Scientific Literature Parsing](https://arxiv.org/pdf/2106.14616v1.pdf) | Dataset | [*CODE](https://github.com/ibm-aur-nlp/PubLayNet)
![](https://img.shields.io/github/stars/ibm-aur-nlp/PubLayNet.svg?style=social) |

ICDAR Competition | 2021 |[PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML](https://arxiv.org/pdf/2105.01848v1.pdf) | Sequence | [*CODE](https://github.com/JiaquanYe/TableMASTER-mmocr)
![](https://img.shields.io/github/stars/JiaquanYe/TableMASTER-mmocr.svg?style=social) |

ICDAR Competition | 2021 |[LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment](https://arxiv.org/pdf/2105.06224.pdf) | Others | [*CODE](https://github.com/hikopensource/DAVAR-Lab-OCR)
![](https://img.shields.io/github/stars/hikopensource/DAVAR-Lab-OCR.svg?style=social) |

WACV | 2021 |[Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context](https://openaccess.thecvf.com/content/WACV2021/papers/Zheng_Global_Table_Extractor_GTE_A_Framework_for_Joint_Table_Identification_WACV_2021_paper.pdf) | Others | No |

CVPR Workshop | 2020 |[CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents](https://arxiv.org/pdf/2004.12629v2.pdf) | Others | [*CODE](https://github.com/DevashishPrasad/CascadeTabNet)
![](https://img.shields.io/github/stars/DevashishPrasad/CascadeTabNet.svg?style=social) |

ECCV | 2020 |[Image-based table recognition: data, model, and evaluation](https://arxiv.org/pdf/1911.10683v5.pdf) | Dataset | [*CODE](https://github.com/ibm-aur-nlp/PubTabNet)
![](https://img.shields.io/github/stars/ibm-aur-nlp/PubTabNet.svg?style=social) |

ECCV | 2020 |[Table structure recognition using top-down and bottom-up cues](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730069.pdf) | Others | [*CODE](https://github.com/sachinraja13/TabStructNet)
![](https://img.shields.io/github/stars/sachinraja13/TabStructNet.svg?style=social) |

LREC | 2020 |[TableBank: A Benchmark Dataset for Table Detection and Recognition](https://arxiv.org/abs/1903.01949) | Dataset | [*CODE](https://github.com/doc-analysis/TableBank)
![](https://img.shields.io/github/stars/doc-analysis/TableBank.svg?style=social) |

arXiv | 2019/8/28 |[Complicated table structure recognition](https://arxiv.org/pdf/1908.04729.pdf) | Others | [*CODE](https://github.com/Academic-Hammer/SciTSR)
![](https://img.shields.io/github/stars/Academic-Hammer/SciTSR.svg?style=social)  |

ICDAR | 2019 |[Rethinking Table Recognition using Graph Neural Networks](https://arxiv.org/pdf/1905.13391v2.pdf) | GNN | [*CODE](https://github.com/shahrukhqasim/TIES-2.0)
![](https://img.shields.io/github/stars/shahrukhqasim/TIES-2.0.svg?style=social) |

ICDAR | 2019 |[Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images](https://arxiv.org/pdf/2001.01469.pdf) | Others | No |

ICDAR | 2019 |[Res2tim: Reconstruct syntactic structures from table images.](https://ieeexplore.ieee.org/document/8978027) | Others | [*CODE](https://github.com/xuewenyuan/ReS2TIM)
![](https://img.shields.io/github/stars/xuewenyuan/ReS2TIM.svg?style=social) |

ICDAR | 2017 |[Deepdesrt: Deep learning for detection and structure recognition of tables in document images](https://www.dfki.de/fileadmin/user_upload/import/9672_PID4966073.pdf) | Others | No |

## 2. Datasets

### 2.1 Introduction

|Dataset|Description|dataset link|

|----|----|----|

|TableBank|**English** TableBank is a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet, contains 417K high-quality labeled tables.**It only contain cell Topology groudtruth**|[TableBank](https://github.com/doc-analysis/TableBank)|

|SciTSR|***English** SciTSR is a large-scale table structure recognition dataset, which contains 15,000 tables in PDF format and their corresponding structure labels obtained from LaTeX source files.**It contain cell Topology, cell content groudtruth**|[SciTSR](https://github.com/Academic-Hammer/SciTSR)|

|PubTabNet|**English** PubTabNet is a large dataset for image-based table recognition, containing 568k+ images of tabular data annotated with the corresponding HTML representation of the tables.**It contain cell Topology, cell content and non-blank cell location groudtruth**|[PubTabNet](https://github.com/ibm-aur-nlp/PubTabNet)|

|FinTabNet|**English** This dataset contains complex tables from the annual reports of S&P 500 companies with detailed table structure annotations to help train and test structure recognition.|[FinTabNet](https://developer.ibm.com/exchanges/data/all/fintabnet/)|

|PubTables-1M|**English** A large, detailed, high-quality dataset for training and evaluating a wide variety of models for the tasks of table detection, table structure recognition, and functional analysis.|[PubTables-1M](https://github.com/microsoft/table-transformer)|

|WTW|**English and Chinese** WTW-Dataset is the first wild table dataset for table detection and table structure recongnition tasks, which is constructed from photoing, scanning and web pages, covers 7 challenging cases like: (1)Inclined tables, (2) Curved tables, (3) Occluded tables or blurredtables (4) Extreme aspect ratio tables (5) Overlaid tables, (6) Multi-color tables and (7) Irregular tables in table structure recognition.**It contain cell Topology, all cell location groudtruth**|[WTW](https://github.com/wangwen-whu/wtw-dataset)|

|TNCR|**English** a new table dataset with varying image quality collected from open access websites.TNCR contains 9428 labeled tables with approximately 6621 images.their classification into 5 different classes(Full Lined,Merged Cells,No lines,Partial Lined,Partial Lined Merged Cells).|[TNCR](https://github.com/abdoelsayed2016/TNCR_Dataset)|

|TAL_OCR_TABLE|**Chinese** TAL_OCR_TABLE dataset come from TAL Form Recognition Technology Challenge.The data of comes from the real homework of students in the education scene and the scene of the test paper. It contain 16k train image and 4k test image**It contain cell Topology, cell content and all cell location groudtruth**|[TAL_OCR_TABLE](https://www.heywhale.com/home/competition/606d6fff0e04ac0017c3bf7f/content/1)|

### 2.2 Comparison of datasets for table structure recognition.

|Dataset|Cell Topology|Cell content|Cell Location|Table Location|

|----|----|----|----|----|

|TableBank|✓|✕|✕|✓|

|SciTSR|✓|✓|✕|✓|

|PubTabNet|✓|✓|✓^{†|✓|

|FinTabNet|✓|✓|✓^{†|✓|

|PubTables-1M|✓|✓|✓|✓|

|WTW|✓|✕|✓|✓|

|TNCR|✕|✕|✕|✓|

|TAL_OCR_TABLE|✓|✓|✓|✓|}}

^† For these datasets, cell bounding boxes are given for non-blank cells only and exclude any non-text portion of a cell.

## 3. Other technical solutions

### PRCV2021 Table Recognition Technology Challenge

- Competition First Place Solution

  - [Solution Introduction](https://mp.weixin.qq.com/s?__biz=MzI1ODk1ODI5Mw%3D%3D&mid=2247489551&idx=1&sn=80bc256f88c51ae4c290debb9bc27148&chksm=ea016eb5dd76e7a3ab6be37fcc2b8d60b9dfbdbf46d7196cc27690eb6faf1ff5b9272732831f&mpshare=1&scene=1&srcid=0224YQwvvlj9xDO9aYeK2BvD&sharer_sharetime=1645665537919&sharer_shareid=72261baff3e3bd9cd5183f7dbcf5bf01#rd)

  - [Solution Report PPT](https://mp.weixin.qq.com/s?__biz=MzI1ODk1ODI5Mw%3D%3D&mid=2247489551&idx=1&sn=80bc256f88c51ae4c290debb9bc27148&chksm=ea016eb5dd76e7a3ab6be37fcc2b8d60b9dfbdbf46d7196cc27690eb6faf1ff5b9272732831f&mpshare=1&scene=1&srcid=0224YQwvvlj9xDO9aYeK2BvD&sharer_sharetime=1645665537919&sharer_shareid=72261baff3e3bd9cd5183f7dbcf5bf01#rd)

- Competition Second Place Method

  - [Solution Report PPT](https://drive.google.com/file/d/1DM7vqrc2YGrLRd0RjTkQ7wrEJuoAknQo/view?usp=sharing)

- Competition Third Place Method

  - [Solution Report PPT](https://drive.google.com/file/d/1GSM6ms6EHVZQzgJS8rZ-27OblkaiUl8N/view?usp=sharing)

### ICDAR 2021 Competition on Scientfic Literature Parsing TaskB: Table Recognition to HTML

- Competition First Place Solution

  - [Solution Report PPT](https://davar-lab.github.io/files/icdar2021_lgpma/LGPMA-slides.pdf)

  - [Solution Report Video](https://www.bilibili.com/video/BV19Q4y1Y73d?spm_id_from=333.999.0.0)

- Competition Second Place Method

  - [Solution Report PPT](https://github.com/JiaquanYe/TableMASTER-mmocr/blob/master/imgs/table2html.pdf)

  - [Solution Report Video](https://www.bilibili.com/video/BV1pU4y1E7Fq?spm_id_from=333.999.0.0)