Awesome-Table-Recognition

A curated list of resources dedicated to table recognition

github会长期维护和更新，欢迎star,fork,pr,issue等

1. Papers

  • *CODE means official code and CODE means not official code
Conf. Date Title Highlight code
arXiv 2021/12/2 Flexible Table Recognition and Semantic Interpretation System Others *CODE
STARS:0
arXiv 2021/11/18 PubTables-1M: Towards comprehensive table extraction from unstructured documents Dataset *CODE
STARS:94
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 Others **CODE
STARS:95
ICCV 2021 Parsing Table Structures in the Wild Dectction No
ICCV 2021 TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition GNN *CODE
STARS:40
ICDAR Competition 2021 ICDAR 2021 Competition on Scientific Literature Parsing Dataset *CODE
STARS:571
ICDAR Competition 2021 PingAn-VCGroup’s Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML Sequence *CODE
STARS:190
ICDAR Competition 2021 LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment Others *CODE
STARS:306
WACV 2021 Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context Others No
CVPR Workshop 2020 CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents Others *CODE
STARS:1018
ECCV 2020 Image-based table recognition: data, model, and evaluation Dataset *CODE
STARS:229
ECCV 2020 Table structure recognition using top-down and bottom-up cues Others *CODE
STARS:96
LREC 2020 TableBank: A Benchmark Dataset for Table Detection and Recognition Dataset *CODE
STARS:759
arXiv 2019/8/28 Complicated table structure recognition Others *CODE
STARS:232
ICDAR 2019 Rethinking Table Recognition using Graph Neural Networks GNN *CODE
STARS:238
ICDAR 2019 Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images Others No
ICDAR 2019 Res2tim: Reconstruct syntactic structures from table images. Others *CODE
STARS:14
ICDAR 2017 Deepdesrt: Deep learning for detection and structure recognition of tables in document images Others No

2. Datasets

Dataset Description Examples 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 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 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 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 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 PubTables-1M
WTW English 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. WTW WTW
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 TNCR
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 imageIt contain cell Topology, cell content and all cell location groudtruth TAL_OCR_TABLE TAL_OCR_TABLE

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