{"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/east-an-efficient-and-accurate-scene-text","title":"EAST: An Efficient and Accurate Scene Text Detector","arxiv_id":"1704.03155","date":"2017-04-11","proceeding":"CVPR 2017 7","authors":["Xinyu Zhou","Cong Yao","He Wen","Yuzhi Wang","Shuchang Zhou","Weiran He","Jiajun Liang"],"abstract":"Previous approaches for scene text detection have already achieved promising\nperformances across various benchmarks. However, they usually fall short when\ndealing with challenging scenarios, even when equipped with deep neural network\nmodels, because the overall performance is determined by the interplay of\nmultiple stages and components in the pipelines. In this work, we propose a\nsimple yet powerful pipeline that yields fast and accurate text detection in\nnatural scenes. The pipeline directly predicts words or text lines of arbitrary\norientations and quadrilateral shapes in full images, eliminating unnecessary\nintermediate steps (e.g., candidate aggregation and word partitioning), with a\nsingle neural network. The simplicity of our pipeline allows concentrating\nefforts on designing loss functions and neural network architecture.\nExperiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500\ndemonstrate that the proposed algorithm significantly outperforms\nstate-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR\n2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps\nat 720p resolution.","url_abs":"http://arxiv.org/abs/1704.03155v2","url_pdf":"http://arxiv.org/pdf/1704.03155v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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