{"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/improving-text-proposals-for-scene-images","title":"Improving Text Proposals for Scene Images with Fully Convolutional Networks","arxiv_id":"1702.05089","date":"2017-02-16","proceeding":null,"authors":["Dena Bazazian","Raul Gomez","Anguelos Nicolaou","Lluis Gomez","Dimosthenis Karatzas","Andrew D. Bagdanov"],"abstract":"Text Proposals have emerged as a class-dependent version of object proposals\n- efficient approaches to reduce the search space of possible text object\nlocations in an image. Combined with strong word classifiers, text proposals\ncurrently yield top state of the art results in end-to-end scene text\nrecognition. In this paper we propose an improvement over the original Text\nProposals algorithm of Gomez and Karatzas (2016), combining it with Fully\nConvolutional Networks to improve the ranking of proposals. Results on the\nICDAR RRC and the COCO-text datasets show superior performance over current\nstate-of-the-art.","url_abs":"http://arxiv.org/abs/1702.05089v1","url_pdf":"http://arxiv.org/pdf/1702.05089v1.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":"improving-text-proposals-for-scene-images","repo_url":"https://github.com/gombru/TextFCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"scene-text-recognition","task_name":"Scene Text Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}