{"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/detecting-multi-oriented-text-with-corner","title":"Detecting Multi-Oriented Text with Corner-based Region Proposals","arxiv_id":"1804.02690","date":"2018-04-08","proceeding":null,"authors":["Linjie Deng","Yanxiang Gong","Yi Lin","Jingwen Shuai","Xiaoguang Tu","Yuefei Zhang","Zheng Ma","Mei Xie"],"abstract":"Previous approaches for scene text detection usually rely on manually defined sliding windows. This work presents an intuitive two-stage region-based method to detect multi-oriented text without any prior knowledge regarding the textual shape. In the first stage, we estimate the possible locations of text instances by detecting and linking corners instead of shifting a set of default anchors. The quadrilateral proposals are geometry adaptive, which allows our method to cope with various text aspect ratios and orientations. In the second stage, we design a new pooling layer named Dual-RoI Pooling which embeds data augmentation inside the region-wise subnetwork for more robust classification and regression over these proposals. Experimental results on public benchmarks confirm that the proposed method is capable of achieving comparable performance with state-of-the-art methods. The code is publicly available at https://github.com/xhzdeng/crpn","url_abs":"https://arxiv.org/abs/1804.02690v2","url_pdf":"https://arxiv.org/pdf/1804.02690v2.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":"detecting-multi-oriented-text-with-corner","repo_url":"https://github.com/xhzdeng/crpn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"robust-classification","task_name":"Robust classification"},{"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-coco-text","task":"Scene Text Detection","dataset":"COCO-Text","model":"Corner-based Region Proposals","rank_in_archive_order":1,"of":6,"metrics":{"F-Measure":"59.1","Precision":"55.5","Recall":"63.3"},"uses_additional_data":false},{"leaderboard":"/sota/scene-text-detection-on-icdar-2013","task":"Scene Text Detection","dataset":"ICDAR 2013","model":"Corner-based Region Proposals","rank_in_archive_order":9,"of":16,"metrics":{"F-Measure":"87.6%%","Precision":"91.9","Recall":"83.9"},"uses_additional_data":false},{"leaderboard":"/sota/scene-text-detection-on-icdar-2015","task":"Scene Text Detection","dataset":"ICDAR 2015","model":"Corner-based Region Proposals","rank_in_archive_order":25,"of":43,"metrics":{"F-Measure":"84.5","Precision":"88.7","Recall":"80.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}