{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:10Z","timestamp":1761126790666,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China, Youth Fund","award":["62102318, 62006192"],"award-info":[{"award-number":["62102318, 62006192"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["G2020KY05113"],"award-info":[{"award-number":["G2020KY05113"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFA0706200, 2019YFB1703600"],"award-info":[{"award-number":["2019YFA0706200, 2019YFB1703600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702195, 61751202, U1813203, U1801262, 61751205"],"award-info":[{"award-number":["61702195, 61751202, U1813203, U1801262, 61751205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Major Project of Guangzhou","award":["202007030006"],"award-info":[{"award-number":["202007030006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Variable-length license plate segmentation and recognition has always been a challenging barrier in the application of intelligent transportation systems. Previous approaches mainly concern fixed-length license plates, lacking adaptability for variable-length license plates. Although objection detection methods can be used to address the issue, they face a series of difficulties: cross class problem, missing detections, and recognition errors between letters and digits. To solve these problems, we propose a machine learning method that regards each character as a region of interest. It covers three parts. Firstly, we explore a transfer learning algorithm based on Faster-RCNN with InceptionV2 structure to generate candidate character regions. Secondly, a strategy of cross-class removal of character is proposed to reject the overlapped results. A mechanism of template matching and position predicting is designed to eliminate missing detections. Moreover, a twofold broad learning system is designed to identify letters and digits separately. Experiments performed on Macau license plates demonstrate that our method achieves an average 99.68% of segmentation accuracy and an average 99.19% of recognition rate, outperforming some conventional and deep learning approaches. The adaptability is expected to transfer the developed algorithm to other countries or regions.<\/jats:p>","DOI":"10.3390\/rs14071560","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T23:31:43Z","timestamp":1648164703000},"page":"1560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Character Segmentation and Recognition of Variable-Length License Plates Using ROI Detection and Broad Learning System"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2603-8328","authenticated-orcid":false,"given":"Bingshu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Software, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Hongli","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Software, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Jiangbin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Software, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1803-3946","authenticated-orcid":false,"given":"Dengxiu","family":"Yu","sequence":"additional","affiliation":[{"name":"Unmanned System Research Institute, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5451-7230","authenticated-orcid":false,"given":"C. L. Philip","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/TCSVT.2012.2203741","article-title":"Automatic license plate recognition (ALPR): A state-of-the-art review","volume":"23","author":"Du","year":"2012","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lin, M., Liu, L., Wang, F., Li, J., and Pan, J. (2021). License Plate Image Reconstruction Based on Generative Adversarial Networks. 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