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Continuously updating. 3 | 4 | If you find any relevant academic papers that have not been included in our research, please submit a request for an update. We welcome contributions from everyone. 5 | ## Popular Datasets 6 | | Name | Tables | Data Type | TD | TSR | TCR | Year| 7 | | ---- | ---- | ---- | ---- | ---- | ---- | ----| 8 | | [ICDAR2013](https://paperswithcode.com/dataset/icdar-2013) | 156 | Digital | √ | √ | √ |2013| 9 | | [SciTSR](https://github.com/Academic-Hammer/SciTSR) | 15K | Digital | × | √ | √ |2019| 10 | | [TableBank](https://doc-analysis.github.io/tablebank-page/) | 417K | Digital | √ | × | × |2020| 11 | | [TableBank](https://doc-analysis.github.io/tablebank-page/) | 145K | Digital | × | √ | × |2020| 12 | | [PubTabNet](https://github.com/ibm-aur-nlp/PubTabNet) | 1M+ | Digital | × | √ | √ |2020| 13 | | [PubTables-1M](https://github.com/microsoft/table-transformer?tab=readme-ov-file)| 1M+ | Digital | √ | √ | × |2021| 14 | | [FinbTabNet](https://developer.ibm.com/exchanges/data/all/fintabnet/) | 91596 | Digital | × | √ | √ |2021| 15 | | [WTW](https://github.com/wangwen-whu/WTW-Dataset) | 14581 | Both | × | √ | × |2021| 16 | | [SynthTabNet](https://github.com/IBM/SynthTabNet) | 600K | Digital | × | √ | √ |2022| 17 | | [TabRecSet](https://github.com/MaxKinny/TabRecSet) | 38177 | Both | √ | √ | √ |2023| 18 | | [iFLYTAB](https://github.com/ZZR8066/SEMv2?tab=readme-ov-file) | 12104 | Both | √ | √ | × |2023| 19 | 20 | **TD** means **T**able **D**etection 21 | **TSR** means **T**able **S**tructure **R**ecognition 22 | **TCD** means **T**able **C**ontent **R**ecognition 23 | **Both** means including both digital and physical data 24 | ## SOTA Models 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 |
Method TypeMethod NameCitation CountYearVenueOpen Source
Bottom-upRes2TIM372019ICDAR√
CascadeTabNet1822020CVPR√
TabStruct-Net872020ECCV
LGPMA632021ICDAR√
Cycle-CenterNet392021ICCV
TGRNet422021ICCV√
FLAG-NET302021MM
GTE1162021WACV
NCGM252022CVPR
LORE72023AAAI
Image2MarkupEDD1712020ECCV√
TableMaster342021ICDAR√
TableFormer412022CVPR
VAST122023CVPR
UniTable2024√
OminParser2024CVPR√
Split-and-Merge BasedSPLERGE912019ICDAR√
SEM402022PR√
TSRFormer202022MM
RobusTabNet322023PR
SEMv232024PR√
TSRFormer-DQ-DETR 2024PR
OthersTableNet1882019ICDAR√
DETR602022CVPR√
TRACE32023ICDAR√
218 | 219 | 220 | ## Influential Papers 221 | ### CVPR 2024 222 | + OMNIPARSER: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition-[Paper](https://arxiv.org/abs/2403.19128),[code]( https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/OmniParser) 223 | ### PR 2024 224 | + SEMv2: Table Separation Line Detection Based on Conditional Convolution-[Paper](https://www.semanticscholar.org/paper/SEMv2%3A-Table-Separation-Line-Detection-Based-on-Zhang-Hu/c78daabab3666d08d945098bc462f882b78803fd), 225 | [code](https://github.com/ZZR8066/SEMv2) 226 | + Robust table structure recognition with dynamic queries enhanced detection transformer-[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320323005150) 227 | ### CVPR 2023 228 | + Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling-[Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper.pdf) 229 | ### AAAI 2023 230 | + LORE: Logical Location Regression Network for Table Structure Recognition=[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/25402/25174) 231 | ### PR 2023 232 | + Robust Table Detection and Structure Recognition from Heterogeneous Document Images-[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320322004861) 233 | + Scene table structure recognition with segmentation collaboration and alignment-[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0167865522003828?via%3Dihub) 234 | ### ACL 2023 235 | + TableVLM: Multi-modal Pre-training for Table Structure Recognition-[Paper](https://aclanthology.org/2023.acl-long.137/) 236 | ### ICDAR 2023 237 | + TRACE: Table Reconstruction Aligned to Corner and Edges-[Paper](https://link.springer.com/chapter/10.1007/978-3-031-41734-4_29) 238 | + Aligning benchmark datasets for table structure recognition-[Paper](https://link.springer.com/chapter/10.1007/978-3-031-41734-4_23) 239 | + Optimized Table Tokenization for Table Structure Recognition-[Paper](https://link.springer.com/chapter/10.1007/978-3-031-41679-8_3) 240 | ### CVPR 2022 241 | + Neural Collaborative Graph Machines for Table Structure Recognition-[Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Neural_Collaborative_Graph_Machines_for_Table_Structure_Recognition_CVPR_2022_paper.pdf) 242 | + TableFormer: Table Structure Understanding with Transformers-[Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Nassar_TableFormer_Table_Structure_Understanding_With_Transformers_CVPR_2022_paper.pdf) 243 | + PubTables-1M: Towards comprehensive table extraction from unstructured documents-[Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.pdf), 244 | [code](https://github.com/microsoft/table-transformer) 245 | ### PR 2022 246 | + Split, Embed and Merge: An accurate table structure recognizer-[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320322000462) 247 | ### WACV 2022 248 | + Visual Understanding of Complex Table Structures from Document Images-[Paper](https://openaccess.thecvf.com/content/WACV2022/papers/Raja_Visual_Understanding_of_Complex_Table_Structures_From_Document_Images_WACV_2022_paper.pdf) 249 | ### MM 2022 250 | + TSRFormer: Table Structure Recognition with Transformers-[Paper](https://dl.acm.org/doi/abs/10.1145/3503161.3548038) 251 | ### ICCV 2021 252 | + Parsing Table Structures in the Wild-[Paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Long_Parsing_Table_Structures_in_the_Wild_ICCV_2021_paper.pdf) 253 | + TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition-[Paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Xue_TGRNet_A_Table_Graph_Reconstruction_Network_for_Table_Structure_Recognition_ICCV_2021_paper.pdf), 254 | [code](https://github.com/xuewenyuan/TGRNet) 255 | ### WACV 2021 256 | + Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context-[Paper](https://openaccess.thecvf.com/content/WACV2021/papers/Zheng_Global_Table_Extractor_GTE_A_Framework_for_Joint_Table_Identification_WACV_2021_paper.pdf) 257 | ### MM 2021 258 | + Show, Read and Reason: Table Structure Recognition with Flexible Context Aggregator-[Paper](https://dl.acm.org/doi/abs/10.1145/3474085.3481534) 259 | ### ICDAR 2021 260 | + LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment-[Paper](https://link.springer.com/chapter/10.1007/978-3-030-86549-8_7) 261 | + PINGAN-VCGROUP’S SOLUTION FOR ICDAR 2021 COMPETITION ON SCIENTIFIC LITERATURE PARSING TASK B:TABLE RECOGNITION TO HTML-[Paper](https://www.semanticscholar.org/paper/PingAn-VCGroup%27s-Solution-for-ICDAR-2021-on-Table-He-Qi/754087ddb922b22873c20b3b4eec3272898326d9), 262 | [code](https://github.com/JiaquanYe/TableMASTER-mmocr) 263 | + TabLeX: A Benchmark Dataset for Structure and Content Information Extraction from Scientific Tables-[Paper](https://link.springer.com/chapter/10.1007/978-3-030-86331-9_36) 264 | ### CVPRW 2020 265 | + CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents-[Paper](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w34/Prasad_CascadeTabNet_An_Approach_for_End_to_End_Table_Detection_and_CVPRW_2020_paper.pdf), 266 | [code](https://github.com/DevashishPrasad/CascadeTabNet) 267 | ### ECCV 2020 268 | + Image-based table recognition: data, model, and evaluation-[Paper](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660562.pdf) 269 | + Table Structure Recognition using Top-Down and Bottom-Up Cues-[Paper](https://link.springer.com/chapter/10.1007/978-3-030-58604-1_5) 270 | ### LERC 2020 271 | + TableBank: Table Benchmark for Image-based Table Detection and Recognition-[Paper](https://aclanthology.org/2020.lrec-1.236/) 272 | ### ICDAR 2019 273 | + Challenges in end-to-end neural scientific table recognition-[Paper](https://ieeexplore.ieee.org/document/8978078) 274 | + Deep Splitting and Merging for Table Structure Decomposition-[Paper](https://ieeexplore.ieee.org/document/8977975) 275 | + DeepTabStR: Deep Learning based Table Structure Recognition-[Paper](https://ieeexplore.ieee.org/document/8978137) 276 | + Rethinking Table Recognition using Graph Neural Networks-[Paper](https://www.computer.org/csdl/proceedings-article/icdar/2019/301400a142/1h81qHhrzaM) 277 | + ReS2TIM: Reconstruct Syntactic Structures from Table Images-[Paper](https://ieeexplore.ieee.org/document/8978027) 278 | + TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images-[Paper](https://www.computer.org/csdl/proceedings-article/icdar/2019/301400a128/1h81vwkHTwY),[code](https://github.com/AmanSavaria1402/TableNet),[model](https://drive.google.com/file/d/11cl-QP5xsYmuM-IwCtc1psMH14bb7kFx/view) 279 | ## Others 280 | #### 2024 281 | + UniTable: Towards a Unified Framework for Table Structure Recognition via Self-Supervised Pretraining-[Paper](https://arxiv.org/abs/2403.04822) 282 | #### 2023 283 | + A large-scale dataset for end-to-end table recognition in the wild-[Paper](https://www.nature.com/articles/s41597-023-01985-8),[code](https://github.com/MaxKinny/TabRecSet) 284 | #### 2021 285 | + 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-[Paper](https://link.springer.com/chapter/10.1007/978-3-030-87626-5_8), 286 | [code](https://github.com/Psarpei/Multi-Type-TD-TSR) 287 | ## Surveys 288 | + **[ICDAR 2023]** A Study on Reproducibility and Replicability of Table Structure Recognition Methods-[Paper](https://link.springer.com/chapter/10.1007/978-3-031-41679-8_1) 289 | + Deep Learning for Table Detection and Structure Recognition: A Survey-[Paper](https://dl.acm.org/doi/abs/10.1145/3657281) 290 | ## Star History 291 | 292 | [![Star History Chart](https://api.star-history.com/svg?repos=MathamPollard/awesome-table-structure-recognition&type=Date)](https://star-history.com/#MathamPollard/awesome-table-structure-recognition&Date) 293 | 294 | --------------------------------------------------------------------------------