{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/image-based-table-recognition-data-model-and","title":"Image-based table recognition: data, model, and evaluation","arxiv_id":"1911.10683","date":"2019-11-25","proceeding":"ECCV 2020 8","authors":["Xu Zhong","Elaheh ShafieiBavani","Antonio Jimeno Yepes"],"abstract":"Important information that relates to a specific topic in a document is often organized in tabular format to assist readers with information retrieval and comparison, which may be difficult to provide in natural language. However, tabular data in unstructured digital documents, e.g., Portable Document Format (PDF) and images, are difficult to parse into structured machine-readable format, due to complexity and diversity in their structure and style. To facilitate image-based table recognition with deep learning, we develop the largest publicly available table recognition dataset PubTabNet (https://github.com/ibm-aur-nlp/PubTabNet), containing 568k table images with corresponding structured HTML representation. PubTabNet is automatically generated by matching the XML and PDF representations of the scientific articles in PubMed Central Open Access Subset (PMCOA). We also propose a novel attention-based encoder-dual-decoder (EDD) architecture that converts images of tables into HTML code. The model has a structure decoder which reconstructs the table structure and helps the cell decoder to recognize cell content. In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition, which more appropriately captures multi-hop cell misalignment and OCR errors than the pre-established metric. The experiments demonstrate that the EDD model can accurately recognize complex tables solely relying on the image representation, outperforming the state-of-the-art by 9.7% absolute TEDS score.","url_abs":"https://arxiv.org/abs/1911.10683v5","url_pdf":"https://arxiv.org/pdf/1911.10683v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"image-based-table-recognition-data-model-and","repo_url":"https://github.com/ibm-aur-nlp/PubTabNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"image-based-table-recognition-data-model-and","repo_url":"https://github.com/JiaquanYe/TableMASTER-mmocr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"image-based-table-recognition-data-model-and","repo_url":"https://github.com/Line290/EDD-third-party","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"image-based-table-recognition-data-model-and","repo_url":"https://github.com/namtuanly/MTL-TabNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"image-based-table-recognition-data-model-and","repo_url":"https://github.com/PaddlePaddle/PaddleOCR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"image-based-table-recognition-data-model-and","repo_url":"https://github.com/kewin1807/table-recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"table-recognition","task_name":"Table Recognition"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[{"slug":"pubtabnet","name":"PubTabNet","full_name":"PubTabNet"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/table-recognition-on-pubtabnet","task":"Table Recognition","dataset":"PubTabNet","model":"EDD","rank_in_archive_order":11,"of":13,"metrics":{"TEDS (all samples)":"88.3"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1911.10683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.10683"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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