{"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/towards-accurate-scene-text-recognition-with","title":"Towards Accurate Scene Text Recognition with Semantic Reasoning Networks","arxiv_id":"2003.12294","date":"2020-03-27","proceeding":"CVPR 2020 6","authors":["Deli Yu","Xuan Li","Chengquan Zhang","Junyu Han","Jingtuo Liu","Errui Ding"],"abstract":"Scene text image contains two levels of contents: visual texture and semantic information. Although the previous scene text recognition methods have made great progress over the past few years, the research on mining semantic information to assist text recognition attracts less attention, only RNN-like structures are explored to implicitly model semantic information. However, we observe that RNN based methods have some obvious shortcomings, such as time-dependent decoding manner and one-way serial transmission of semantic context, which greatly limit the help of semantic information and the computation efficiency. To mitigate these limitations, we propose a novel end-to-end trainable framework named semantic reasoning network (SRN) for accurate scene text recognition, where a global semantic reasoning module (GSRM) is introduced to capture global semantic context through multi-way parallel transmission. The state-of-the-art results on 7 public benchmarks, including regular text, irregular text and non-Latin long text, verify the effectiveness and robustness of the proposed method. In addition, the speed of SRN has significant advantages over the RNN based methods, demonstrating its value in practical use.","url_abs":"https://arxiv.org/abs/2003.12294v1","url_pdf":"https://arxiv.org/pdf/2003.12294v1.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":"towards-accurate-scene-text-recognition-with","repo_url":"https://github.com/PaddlePaddle/PaddleOCR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"paddle","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"towards-accurate-scene-text-recognition-with","repo_url":"https://github.com/Media-Smart/vedastr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"towards-accurate-scene-text-recognition-with","repo_url":"https://github.com/topdu/openocr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"},{"task_slug":"scene-text-recognition","task_name":"Scene Text Recognition"}],"methods":[{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"semantic-reasoning-network","method_name":"Semantic Reasoning Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/optical-character-recognition-on-benchmarking","task":"Optical Character Recognition (OCR)","dataset":"Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study","model":"SRN","rank_in_archive_order":5,"of":7,"metrics":{"Accuracy (%)":"65.0"},"uses_additional_data":false},{"leaderboard":"/sota/scene-text-recognition-on-icdar2013","task":"Scene Text Recognition","dataset":"ICDAR2013","model":"SRN","rank_in_archive_order":22,"of":38,"metrics":{"Accuracy":"95.5"},"uses_additional_data":false},{"leaderboard":"/sota/scene-text-recognition-on-svt","task":"Scene Text Recognition","dataset":"SVT","model":"SRN","rank_in_archive_order":22,"of":37,"metrics":{"Accuracy":"91.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.12294","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}