{"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/multi-task-handwritten-document-layout","title":"Multi-Task Handwritten Document Layout Analysis","arxiv_id":"1806.08852","date":"2018-06-22","proceeding":null,"authors":["Lorenzo Quirós"],"abstract":"Document Layout Analysis is a fundamental step in Handwritten Text Processing\nsystems, from the extraction of the text lines to the type of zone it belongs\nto. We present a system based on artificial neural networks which is able to\ndetermine not only the baselines of text lines present in the document, but\nalso performs geometric and logic layout analysis of the document. Experiments\nin three different datasets demonstrate the potential of the method and show\ncompetitive results with respect to state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1806.08852v3","url_pdf":"http://arxiv.org/pdf/1806.08852v3.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":"multi-task-handwritten-document-layout","repo_url":"https://github.com/lquirosd/P2PaLA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"document-layout-analysis","task_name":"Document Layout Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}