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However, in complex environments such as vehicle sound source localization, poor illumination, or bad weather conditions, ALPR is still a challenging problem. Aiming at the problem, an end-to-end deep learning framework is developed based on depthwise over-parameterized convolution recurrent neural network for license plate character recognition. The proposed framework is composed as follows: (i) license plate correcting module based on spatial transformation network; (ii) feature extraction module based on depthwise over-parameterized convolution; (iii) sequence annotation module based on bidirectional long short-term memory; and (iv) regularized sequence decoding module based on connectionist temporal classification with maximum conditional entropy. Two open-source datasets of Chinese License Plate Datasets (SYSU) and Chinese City Parking Dataset (CCPD) are used to verify the performance of the algorithm. The proposed end-to-end framework can effectively rectify distorted and inclined license plates in spatial domain. It can recognize license plates without complex character segmentation process. Compared with some current state-of-art algorithms, the proposed algorithm achieved the best performance with the recognition accuracy of 96.31% and 88.31% based on the two datasets of SYSU and CCPD, respectively.<\/jats:p>","DOI":"10.1115\/1.4055507","type":"journal-article","created":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T07:50:39Z","timestamp":1662364239000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":5,"title":["Spatial Transform Depthwise Over-Parameterized Convolution Recurrent Neural Network for License Plate Recognition in Complex Environment"],"prefix":"10.1115","volume":"23","author":[{"given":"Jiehang","family":"Deng","sequence":"first","affiliation":[{"name":"Guangdong University of Technology School of Computer Science and Technology, , Guangzhou 510006 , China"}]},{"given":"Haomin","family":"Wei","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology School of Computer Science and Technology, , Guangzhou 510006 , China"}]},{"given":"Zhenxiang","family":"Lai","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology School of Computer Science and Technology, , Guangzhou 510006 , China"}]},{"given":"Guosheng","family":"Gu","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology School of Computer Science and Technology, , Guangzhou 510006 , China"}]},{"given":"Zhiqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"QuZhou University School of Electrical and Information Engineering, , Quzhou 324000 , China"}]},{"given":"Leo","family":"Chen","sequence":"additional","affiliation":[{"name":"Newcastle University School of Engineering, , Newcastle upon Tyne NE1 7RU , UK"}]},{"given":"Lei","family":"Ding","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology School of Computer Science and Technology, , Guangzhou 510006 , China"}]}],"member":"33","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"2022101014300857900_CIT0001","doi-asserted-by":"publisher","first-page":"11203","DOI":"10.1109\/ACCESS.2020.3047929","article-title":"Automated License Plate Recognition: a Survey on Methods and Techniques","volume":"9","author":"Shashirangana","year":"2020","journal-title":"IEEE Access"},{"issue":"1","key":"2022101014300857900_CIT0002","doi-asserted-by":"crossref","first-page":"013001","DOI":"10.1117\/1.JEI.28.1.013001","article-title":"Chinese License Plate Image Database Building Methodology for License Plate Recognition","volume":"28","author":"Zhao","year":"2019","journal-title":"J. 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