{"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/cascadetabnet-an-approach-for-end-to-end","title":"CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents","arxiv_id":"2004.12629","date":"2020-04-27","proceeding":null,"authors":["Devashish Prasad","Ayan Gadpal","Kshitij Kapadni","Manish Visave","Kavita Sultanpure"],"abstract":"An automatic table recognition method for interpretation of tabular data in document images majorly involves solving two problems of table detection and table structure recognition. The prior work involved solving both problems independently using two separate approaches. More recent works signify the use of deep learning-based solutions while also attempting to design an end to end solution. In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model. We propose CascadeTabNet: a Cascade mask Region-based CNN High-Resolution Network (Cascade mask R-CNN HRNet) based model that detects the regions of tables and recognizes the structural body cells from the detected tables at the same time. We evaluate our results on ICDAR 2013, ICDAR 2019 and TableBank public datasets. We achieved 3rd rank in ICDAR 2019 post-competition results for table detection while attaining the best accuracy results for the ICDAR 2013 and TableBank dataset. We also attain the highest accuracy results on the ICDAR 2019 table structure recognition dataset. Additionally, we demonstrate effective transfer learning and image augmentation techniques that enable CNNs to achieve very accurate table detection results. Code and dataset has been made available at: https://github.com/DevashishPrasad/CascadeTabNet","url_abs":"https://arxiv.org/abs/2004.12629v2","url_pdf":"https://arxiv.org/pdf/2004.12629v2.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":"cascadetabnet-an-approach-for-end-to-end","repo_url":"https://github.com/DevashishPrasad/CascadeTabNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"cascadetabnet-an-approach-for-end-to-end","repo_url":"https://github.com/hmnth1/table_ocr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"cascadetabnet-an-approach-for-end-to-end","repo_url":"https://github.com/virtualsociety/ai-table-recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-augmentation","task_name":"Image Augmentation"},{"task_slug":"table-detection","task_name":"Table Detection"},{"task_slug":"table-recognition","task_name":"Table Recognition"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"cascade-mask-r-cnn","method_name":"Cascade Mask R-CNN"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"hrnet","method_name":"HRNet"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roi-align","method_name":"RoIAlign"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/table-detection-on-icdar2013-1","task":"Table Detection","dataset":"ICDAR2013","model":"cascadetabnet","rank_in_archive_order":1,"of":3,"metrics":{"Avg F1":"1.0"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2004.12629","atlas_url":"https://app.syntology.ai/?focus=2004.12629","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.12629"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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