{"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/sliding-line-point-regression-for-shape","title":"Sliding Line Point Regression for Shape Robust Scene Text Detection","arxiv_id":"1801.09969","date":"2018-01-30","proceeding":null,"authors":["Yixing Zhu","Jun Du"],"abstract":"Traditional text detection methods mostly focus on quadrangle text. In this\nstudy we propose a novel method named sliding line point regression (SLPR) in\norder to detect arbitrary-shape text in natural scene. SLPR regresses multiple\npoints on the edge of text line and then utilizes these points to sketch the\noutlines of the text. The proposed SLPR can be adapted to many object detection\narchitectures such as Faster R-CNN and R-FCN. Specifically, we first generate\nthe smallest rectangular box including the text with region proposal network\n(RPN), then isometrically regress the points on the edge of text by using the\nvertically and horizontally sliding lines. To make full use of information and\nreduce redundancy, we calculate x-coordinate or y-coordinate of target point by\nthe rectangular box position, and just regress the remaining y-coordinate or\nx-coordinate. Accordingly we can not only reduce the parameters of system, but\nalso restrain the points which will generate more regular polygon. Our approach\nachieved competitive results on traditional ICDAR2015 Incidental Scene Text\nbenchmark and curve text detection dataset CTW1500.","url_abs":"http://arxiv.org/abs/1801.09969v1","url_pdf":"http://arxiv.org/pdf/1801.09969v1.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":"sliding-line-point-regression-for-shape","repo_url":"https://github.com/Shualite/UBNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"curved-text-detection","task_name":"Curved Text Detection"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"region-proposal","task_name":"Region Proposal"},{"task_slug":"scene-text-detection","task_name":"Scene Text Detection"},{"task_slug":"text-detection","task_name":"Text Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"position-sensitive-roi-pooling","method_name":"Position-Sensitive RoI Pooling"},{"method_slug":"r-fcn","method_name":"R-FCN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/scene-text-detection-on-scut-ctw1500","task":"Scene Text Detection","dataset":"SCUT-CTW1500","model":"SLPR","rank_in_archive_order":17,"of":17,"metrics":{"F-Measure":"74.8","Precision":"80.1","Recall":"70.1"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}