{"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/real-time-document-localization-in-natural","title":"Real-time Document Localization in Natural Images by Recursive Application of a CNN.","arxiv_id":null,"date":"2017-11-13","proceeding":"ICDAR2017 2017 11","authors":["Khurram Javed","Faisal Shafait"],"abstract":"We propose a document segmentation algorithm that recursively uses convolutional neural networks to precisely localize a document in a natural image. The system can run in real-time on a mobile CPU, has minimal storage requirements, and achieves results comparable to the state of the art on the simple backgrounds and considerably better (Improving previous 86% to 94%) than the state of the art on the complex background of the \"ICDAR 2015 SmartDoc Competition 1\" dataset.","url_abs":"https://sites.ualberta.ca/~kjaved/","url_pdf":"https://khurramjaved96.github.io/RecursiveCNN.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":"real-time-document-localization-in-natural","repo_url":"https://github.com/Khurramjaved96/Recursive-CNNs","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}