{"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-oriented-text-detection-with-fully","title":"Multi-Oriented Text Detection with Fully Convolutional Networks","arxiv_id":"1604.04018","date":"2016-04-14","proceeding":"CVPR 2016 6","authors":["Zheng Zhang","Chengquan Zhang","Wei Shen","Cong Yao","Wenyu Liu","Xiang Bai"],"abstract":"In this paper, we propose a novel approach for text detec- tion in natural\nimages. Both local and global cues are taken into account for localizing text\nlines in a coarse-to-fine pro- cedure. First, a Fully Convolutional Network\n(FCN) model is trained to predict the salient map of text regions in a holistic\nmanner. Then, text line hypotheses are estimated by combining the salient map\nand character components. Fi- nally, another FCN classifier is used to predict\nthe centroid of each character, in order to remove the false hypotheses. The\nframework is general for handling text in multiple ori- entations, languages\nand fonts. The proposed method con- sistently achieves the state-of-the-art\nperformance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and\nICDAR2013.","url_abs":"http://arxiv.org/abs/1604.04018v2","url_pdf":"http://arxiv.org/pdf/1604.04018v2.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-oriented-text-detection-with-fully","repo_url":"https://github.com/stupidZZ/FCN_Text","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"torch","reach":null}],"tasks":[{"task_slug":"scene-text-detection","task_name":"Scene Text Detection"},{"task_slug":"text-detection","task_name":"Text Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/scene-text-detection-on-icdar-2015","task":"Scene Text Detection","dataset":"ICDAR 2015","model":"SegLink","rank_in_archive_order":40,"of":43,"metrics":{"F-Measure":"75","Precision":"73.1","Recall":"76.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.04018","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}