{"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/lprnet-license-plate-recognition-via-deep","title":"LPRNet: License Plate Recognition via Deep Neural Networks","arxiv_id":"1806.10447","date":"2018-06-27","proceeding":null,"authors":["Sergey Zherzdev","Alexey Gruzdev"],"abstract":"This paper proposes LPRNet - end-to-end method for Automatic License Plate\nRecognition without preliminary character segmentation. Our approach is\ninspired by recent breakthroughs in Deep Neural Networks, and works in\nreal-time with recognition accuracy up to 95% for Chinese license plates: 3\nms/plate on nVIDIA GeForce GTX 1080 and 1.3 ms/plate on Intel Core i7-6700K\nCPU. LPRNet consists of the lightweight Convolutional Neural Network, so it can\nbe trained in end-to-end way. To the best of our knowledge, LPRNet is the first\nreal-time License Plate Recognition system that does not use RNNs. As a result,\nthe LPRNet algorithm may be used to create embedded solutions for LPR that\nfeature high level accuracy even on challenging Chinese license plates.","url_abs":"http://arxiv.org/abs/1806.10447v1","url_pdf":"http://arxiv.org/pdf/1806.10447v1.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":"lprnet-license-plate-recognition-via-deep","repo_url":"https://github.com/SQMah/Plate-Reading-Network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"lprnet-license-plate-recognition-via-deep","repo_url":"https://github.com/Tubaher/lpr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"lprnet-license-plate-recognition-via-deep","repo_url":"https://github.com/ZosoV/license-plate-recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"lprnet-license-plate-recognition-via-deep","repo_url":"https://github.com/lyl8213/Plate_Recognition-LPRnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"lprnet-license-plate-recognition-via-deep","repo_url":"https://github.com/mesakarghm/LPRNET","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"lprnet-license-plate-recognition-via-deep","repo_url":"https://github.com/tn00378077/licenses","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"lprnet-license-plate-recognition-via-deep","repo_url":"https://github.com/FaceOnLive/License-Plate-Recognition-SDK-Android","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"license-plate-recognition","task_name":"License Plate Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/license-plate-recognition-on-chinese-license","task":"License Plate Recognition","dataset":"Chinese License Plates","model":"LPRNet basic","rank_in_archive_order":1,"of":3,"metrics":{"GFLOPs":"0.34"},"uses_additional_data":false},{"leaderboard":"/sota/license-plate-recognition-on-chinese-license","task":"License Plate Recognition","dataset":"Chinese License Plates","model":"LPRNet reduced","rank_in_archive_order":2,"of":3,"metrics":{"GFLOPs":"0.94"},"uses_additional_data":false},{"leaderboard":"/sota/license-plate-recognition-on-chinese-license","task":"License Plate Recognition","dataset":"Chinese License Plates","model":"LPRNet baseline","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"94.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.10447","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}