{"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/reading-car-license-plates-using-deep","title":"Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs","arxiv_id":"1601.05610","date":"2016-01-21","proceeding":null,"authors":["Hui Li","Chunhua Shen"],"abstract":"In this work, we tackle the problem of car license plate detection and\nrecognition in natural scene images. Inspired by the success of deep neural\nnetworks (DNNs) in various vision applications, here we leverage DNNs to learn\nhigh-level features in a cascade framework, which lead to improved performance\non both detection and recognition.\n  Firstly, we train a $37$-class convolutional neural network (CNN) to detect\nall characters in an image, which results in a high recall, compared with\nconventional approaches such as training a binary text/non-text classifier.\nFalse positives are then eliminated by the second plate/non-plate CNN\nclassifier. Bounding box refinement is then carried out based on the edge\ninformation of the license plates, in order to improve the\nintersection-over-union (IoU) ratio. The proposed cascade framework extracts\nlicense plates effectively with both high recall and precision. Last, we\npropose to recognize the license characters as a {sequence labelling} problem.\nA recurrent neural network (RNN) with long short-term memory (LSTM) is trained\nto recognize the sequential features extracted from the whole license plate via\nCNNs. The main advantage of this approach is that it is segmentation free. By\nexploring context information and avoiding errors caused by segmentation, the\nRNN method performs better than a baseline method of combining segmentation and\ndeep CNN classification; and achieves state-of-the-art recognition accuracy.","url_abs":"http://arxiv.org/abs/1601.05610v1","url_pdf":"http://arxiv.org/pdf/1601.05610v1.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":"reading-car-license-plates-using-deep","repo_url":"https://github.com/brightyoun/TITS-LPST","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"license-plate-detection","task_name":"License Plate Detection"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1601.05610","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}