{"url":"/sota/table-recognition-on-pubtabnet","task":{"name":"Table Recognition","url":"/task/table-recognition","note":null},"dataset":{"name":"PubTabNet","url":"/dataset/pubtabnet"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"Table recognition refers to the process of automatically identifying and extracting tabular structures from unstructured data sources such as text documents, images, or scanned documents. The goal of table recognition is to accurately detect the presence of tables within the data and extract their contents, including rows, columns, headers, and cell values.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["TEDS (all samples)","TEDS-Struct"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"TEDS (all samples)":null,"TEDS-Struct":null}},"counts":{"rows":13,"rows_with_code":8,"rows_with_paper_page":13,"rows_dated":13,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MuTabNet","metrics":{"TEDS (all samples)":"96.87"},"uses_additional_data":false,"paper_date":"2024-04-20","paper":"/paper/multi-cell-decoder-and-mutual-learning-for","paper_url":"https://arxiv.org/abs/2404.13268v2","paper_title":"Multi-Cell Decoder and Mutual Learning for Table Structure and Character Recognition","code":"https://github.com/JG1VPP/MuTabNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"TableMaster","metrics":{"TEDS (all samples)":"96.76"},"uses_additional_data":false,"paper_date":"2021-05-05","paper":"/paper/pingan-vcgroup-s-solution-for-icdar-2021","paper_url":"https://arxiv.org/abs/2105.01848v1","paper_title":"PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML","code":"https://github.com/PaddlePaddle/PaddleOCR","n_code_links":3,"syntology":{"n_ran":0,"n_unverified":11,"n_samples":11,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"Multi-Task Learning Model","metrics":{"TEDS (all samples)":"96.67","TEDS-Struct":"97.88"},"uses_additional_data":false,"paper_date":"2023-03-15","paper":"/paper/an-end-to-end-multi-task-learning-model-for-1","paper_url":"https://arxiv.org/abs/2303.08648v2","paper_title":"An End-to-End Multi-Task Learning Model for Image-based Table Recognition","code":"https://github.com/namtuanly/MTL-TabNet","n_code_links":2,"syntology":null},{"rank_in_archive_order":4,"model":"ConvStem","metrics":{"TEDS (all samples)":"96.53"},"uses_additional_data":false,"paper_date":"2023-11-09","paper":"/paper/high-performance-transformers-for-table","paper_url":"https://arxiv.org/abs/2311.05565v1","paper_title":"High-Performance Transformers for Table Structure Recognition Need Early Convolutions","code":"https://github.com/poloclub/unitable","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"SLANet","metrics":{"TEDS (all samples)":"96.3","TEDS-Struct":"97.01"},"uses_additional_data":false,"paper_date":"2022-10-11","paper":"/paper/pp-structurev2-a-stronger-document-analysis","paper_url":"https://arxiv.org/abs/2210.05391v2","paper_title":"PP-StructureV2: A Stronger Document Analysis System","code":"https://github.com/PaddlePaddle/PaddleOCR","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"TRUST","metrics":{"TEDS (all samples)":"96.2","TEDS-Struct":"97.1"},"uses_additional_data":false,"paper_date":"2022-08-31","paper":"/paper/trust-an-accurate-and-end-to-end-table","paper_url":"https://arxiv.org/abs/2208.14687v1","paper_title":"TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":7,"model":"NCGM","metrics":{"TEDS (all samples)":"95.4"},"uses_additional_data":false,"paper_date":"2021-11-26","paper":"/paper/neural-collaborative-graph-machines-for-table","paper_url":"https://arxiv.org/abs/2111.13359v2","paper_title":"Neural Collaborative Graph Machines for Table Structure Recognition","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":8,"model":"LGPMA","metrics":{"TEDS (all samples)":"94.6","TEDS-Struct":"96.7"},"uses_additional_data":false,"paper_date":"2021-05-13","paper":"/paper/lgpma-complicated-table-structure-recognition","paper_url":"https://arxiv.org/abs/2105.06224v3","paper_title":"LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment","code":"https://github.com/hikopensource/davar-lab-ocr","n_code_links":2,"syntology":null},{"rank_in_archive_order":9,"model":"SEM","metrics":{"TEDS (all samples)":"93.7"},"uses_additional_data":false,"paper_date":"2021-07-12","paper":"/paper/split-embed-and-merge-an-accurate-table","paper_url":"https://arxiv.org/abs/2107.05214v3","paper_title":"Split, embed and merge: An accurate table structure recognizer","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":10,"model":"TabStruct-Net","metrics":{"TEDS (all samples)":"90.1","TEDS-Struct":"90.1"},"uses_additional_data":false,"paper_date":"2020-10-09","paper":"/paper/table-structure-recognition-using-top-down-1","paper_url":"https://arxiv.org/abs/2010.04565v1","paper_title":"Table Structure Recognition using Top-Down and Bottom-Up Cues","code":"https://github.com/sachinraja13/TabStructNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":11,"model":"EDD","metrics":{"TEDS (all samples)":"88.3"},"uses_additional_data":false,"paper_date":"2019-11-25","paper":"/paper/image-based-table-recognition-data-model-and","paper_url":"https://arxiv.org/abs/1911.10683v5","paper_title":"Image-based table recognition: data, model, and evaluation","code":"https://github.com/PaddlePaddle/PaddleOCR","n_code_links":6,"syntology":{"n_ran":2,"n_unverified":4,"n_samples":6,"n_pointer_only_licence":0}},{"rank_in_archive_order":12,"model":"TSRFormer","metrics":{"TEDS-Struct":"97.5"},"uses_additional_data":false,"paper_date":"2022-08-09","paper":"/paper/tsrformer-table-structure-recognition-with","paper_url":"https://arxiv.org/abs/2208.04921v1","paper_title":"TSRFormer: Table Structure Recognition with Transformers","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":13,"model":"RTSR","metrics":{"TEDS-Struct":"97"},"uses_additional_data":false,"paper_date":"2022-03-17","paper":"/paper/robust-table-detection-and-structure","paper_url":"https://arxiv.org/abs/2203.09056v2","paper_title":"Robust Table Detection and Structure Recognition from Heterogeneous Document Images","code":null,"n_code_links":0,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":3,"rows_with_any_sample_ran":2,"distinct_papers_with_graph_line":3,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":3,"n_unverified":16,"n_samples":19,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":3,"n_unverified":16,"n_samples":19,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}