{"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/lineex-data-extraction-from-scientific-line","title":"LineEX: Data Extraction from Scientific Line Charts","arxiv_id":null,"date":"2023-02-06","proceeding":"Winter Conference on Applications of Computer Vision 2023 2","authors":["Shivasankaran V P","Muhammad Yusuf Hassan","Mayank Singh"],"abstract":"In this paper, we introduce LINEEX that extracts data from scientific line charts. We adapt existing vision transformers and pose detection methods and showcase significant performance gains over existing SOTA baselines. We also propose a new loss function and present its effectiveness against existing loss functions. In addition, we synthetically created the largest line chart dataset comprising 430K images.","url_abs":"https://ieeexplore.ieee.org/document/10030557","url_pdf":"https://openaccess.thecvf.com/content/WACV2023/papers/P._LineEX_Data_Extraction_From_Scientific_Line_Charts_WACV_2023_paper.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":"lineex-data-extraction-from-scientific-line","repo_url":"https://github.com/Shiva-sankaran/LineEX","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}