{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:44:28Z","timestamp":1781844268388,"version":"3.54.5"},"reference-count":64,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T00:00:00Z","timestamp":1592956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004386","name":"Universiti Malaya","doi-asserted-by":"publisher","award":["BKS082-2017 and IIRG012C-2019"],"award-info":[{"award-number":["BKS082-2017 and IIRG012C-2019"]}],"id":[{"id":"10.13039\/501100004386","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001782","name":"University of Melbourne","doi-asserted-by":"publisher","award":["Melbourne Research Scholarship (MRS)"],"award-info":[{"award-number":["Melbourne Research Scholarship (MRS)"]}],"id":[{"id":"10.13039\/501100001782","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001798","name":"Edith Cowan University","doi-asserted-by":"publisher","award":["School of Science Collaborative Research Grant Scheme 2019"],"award-info":[{"award-number":["School of Science Collaborative Research Grant Scheme 2019"]}],"id":[{"id":"10.13039\/501100001798","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.<\/jats:p>","DOI":"10.3390\/s20123578","type":"journal-article","created":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T10:54:59Z","timestamp":1592996099000},"page":"3578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2172-7041","authenticated-orcid":false,"given":"Kh Tohidul","family":"Islam","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Faculty of Computer Science &amp; Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"},{"name":"Department of Surgery (Otolaryngology), Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8627-1113","authenticated-orcid":false,"given":"Ram Gopal","family":"Raj","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Faculty of Computer Science &amp; Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3200-2903","authenticated-orcid":false,"given":"Syed Mohammed","family":"Shamsul Islam","sequence":"additional","affiliation":[{"name":"Discipline of Computing and Security, School of Science, Edith Cowan University (ECU), Joondalup, WA 6027, Australia"},{"name":"Department of Computer Science and Software Engineering, The University of Western Australia, Crawley, WA 6009, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8015-8577","authenticated-orcid":false,"given":"Sudanthi","family":"Wijewickrema","sequence":"additional","affiliation":[{"name":"Department of Surgery (Otolaryngology), Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0028-1917","authenticated-orcid":false,"given":"Md Sazzad","family":"Hossain","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1179-3873","authenticated-orcid":false,"given":"Tayla","family":"Razmovski","sequence":"additional","affiliation":[{"name":"Department of Surgery (Otolaryngology), Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"O\u2019Leary","sequence":"additional","affiliation":[{"name":"Department of Surgery (Otolaryngology), Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,24]]},"reference":[{"key":"ref_1","first-page":"27","article-title":"Localization of License Plates from Surveillance Camera Images: A Color Feature Based ANN Approach","volume":"1","author":"Saha","year":"2010","journal-title":"Int. J. Comput. Appl."},{"key":"ref_2","unstructured":"Hongliang, B., and Changping, L. (2004, January 23\u201326). A hybrid license plate extraction method based on edge statistics and morphology. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8355","DOI":"10.3390\/s120608355","article-title":"License Plate Recognition Algorithm for Passenger Cars in Chinese Residential Areas","volume":"12","author":"Jin","year":"2012","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gervasi, O., Gavrilova, M.L., Kumar, V., Lagan\u00e1, A., Lee, H.P., Mun, Y., Taniar, D., and Tan, C.J.K. (2005, January 9\u201312). Automatic License Plate Recognition System Based on Color Image Processing. Proceedings of the Computational Science and Its Applications, Singapore.","DOI":"10.1007\/b136278"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rizvi, S., Patti, D., Bj\u00f6rklund, T., Cabodi, G., and Francini, G. (2017). Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform. Future Internet, 9.","DOI":"10.3390\/fi9040066"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6429","DOI":"10.1007\/s00500-017-2696-2","article-title":"Vehicle license plate detection using region-based convolutional neural networks","volume":"22","author":"Rafique","year":"2018","journal-title":"Soft Comput."},{"key":"ref_7","unstructured":"Salau, A.O., Yesufu, T.K., and Ogundare, B.S. (2019). Vehicle plate number localization using a modified GrabCut algorithm. J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kakani, B.V., Gandhi, D., and Jani, S. (2017, January 3\u20135). Improved OCR based automatic vehicle number plate recognition using features trained neural network. Proceedings of the 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India.","DOI":"10.1109\/ICCCNT.2017.8203916"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Arafat, M.Y., Khairuddin, A.S.M., and Paramesran, R. (2018). A Vehicular License Plate Recognition Framework For Skewed Images. KSII Trans. Internet Inf. Syst., 12.","DOI":"10.3837\/tiis.2018.11.019"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yogheedha, K., Nasir, A., Jaafar, H., and Mamduh, S. (2018, January 15\u201317). Automatic Vehicle License Plate Recognition System Based on Image Processing and Template Matching Approach. Proceedings of the 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), Kuching, Malaysia.","DOI":"10.1109\/ICASSDA.2018.8477639"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"175","DOI":"10.14445\/22312803\/IJCTT-V35P133","article-title":"License Number Plate Recognition using Template Matching","volume":"35","author":"Ansari","year":"2016","journal-title":"Int. J. Comput. Trends Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s12293-016-0187-0","article-title":"A memetic-based fuzzy support vector machine model and its application to license plate recognition","volume":"8","author":"Samma","year":"2016","journal-title":"Memetic Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.procs.2016.09.447","article-title":"A Hybrid KNN-SVM Model for Iranian License Plate Recognition","volume":"102","author":"Tabrizi","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gao, Y., and Lee, H. (2016). Local Tiled Deep Networks for Recognition of Vehicle Make and Model. Sensors, 16.","DOI":"10.3390\/s16020226"},{"key":"ref_15","unstructured":"Leeds City Council (2019, February 06). Automatic Number Plate Recognition (ANPR) Project, Available online: http:\/\/data.gov.uk\/dataset\/f90db76e-e72f-4ab6-9927-765101b7d997."},{"key":"ref_16","unstructured":"Dynamics, S. (2019, February 06). Australia\u2019s Leading ANPR\u2014Automatic Number Plate Recognition Provider. Available online: http:\/\/www.sensordynamics.com.au\/."},{"key":"ref_17","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_18","unstructured":"Silva, S.M., and Jung, C.R. (2019, February 07). License Plate Detection and Recognition in Unconstrained Scenarios. Available online: http:\/\/www.inf.ufrgs.br\/~smsilva\/alpr-unconstrained\/."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Resende Gon\u00e7alves, G., Alves Diniz, M., Laroca, R., Menotti, D., and Robson Schwartz, W. (November, January 29). Real-Time Automatic License Plate Recognition through Deep Multi-Task Networks. Proceedings of the 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Parana, Brazil.","DOI":"10.1109\/SIBGRAPI.2018.00021"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ullah, F., Anwar, H., Shahzadi, I., Ur Rehman, A., Mehmood, S., Niaz, S., Mahmood Awan, K., Khan, A., and Kwak, D. (2019). Barrier Access Control Using Sensors Platform and Vehicle License Plate Characters Recognition. Sensors, 19.","DOI":"10.3390\/s19133015"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Suryanarayana, P., Mitra, S., Banerjee, A., and Roy, A. (2005, January 11\u201313). A Morphology Based Approach for Car License Plate Extraction. Proceedings of the 2005 Annual IEEE India Conference-Indicon, Chennai, India.","DOI":"10.1109\/INDCON.2005.1590116"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mahini, H., Kasaei, S., Dorri, F., and Dorri, F. (2006, January 20\u201324). An Efficient Features\u2014Based License Plate Localization Method. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.239"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1016\/j.patrec.2005.04.014","article-title":"An efficient method of license plate location","volume":"26","author":"Zheng","year":"2005","journal-title":"Pattern Recognit. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Luo, Y., Li, Y., Huang, S., and Han, F. (2018, January 27\u201329). Multiple Chinese Vehicle License Plate Localization in Complex Scenes. Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, China.","DOI":"10.1109\/ICIVC.2018.8492857"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0"},{"key":"ref_26","unstructured":"Hsieh, C.T., Juan, Y.S., and Hung, K.M. (2005, January 28\u201330). Multiple License Plate Detection for Complex Background. Proceedings of the 19th International Conference on Advanced Information Networking and Applications (AINA\u201905) Volume 1 (AINA papers), Taipei, Taiwan."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/BF01456326","article-title":"Zur Theorie der orthogonalen Funktionensysteme","volume":"69","author":"Haar","year":"1910","journal-title":"Math. Ann."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Niu, B., Huang, L., and Hu, J. (2015, January 21\u201322). Hybrid Method for License Plate Detection from Natural Scene Images. Proceedings of the First International Conference on Information Science and Electronic Technology, Wuhan, China.","DOI":"10.2991\/iset-15.2015.9"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Arafat, M.Y., Khairuddin, A.S.M., Khairuddin, U., and Paramesran, R. (2019). Systematic review on vehicular licence plate recognition framework in intelligent transport systems. IET Intell. Transp. Syst.","DOI":"10.1049\/iet-its.2018.5151"},{"key":"ref_30","unstructured":"Arai, K., Kapoor, S., and Bhatia, R. (2019). Extraction, Segmentation and Recognition of Vehicle\u2019s License Plate Numbers. Advances in Information and Communication Networks, Springer International Publishing."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dhar, P., Guha, S., Biswas, T., and Abedin, M.Z. (2018, January 8\u20139). A System Design for License Plate Recognition by Using Edge Detection and Convolution Neural Network. Proceedings of the 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh.","DOI":"10.1109\/IC4ME2.2018.8465630"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"De Gaetano Ariel, O., Mart\u00edn, D.F., and Ariel, A. (2018, January 25\u201328). ALPR character segmentation algorithm. Proceedings of the 2018 IEEE 9th Latin American Symposium on Circuits Systems (LASCAS), Puerto Vallarta, Mexico.","DOI":"10.1109\/LASCAS.2018.8399954"},{"key":"ref_33","unstructured":"Yaz\u0131c\u0131, A., and \u015eener, C. (2003, January 3\u20135). License Plate Character Segmentation Based on the Gabor Transform and Vector Quantization. Proceedings of the Computer and Information Sciences\u2014ISCIS 2003, Antalya, Turkey."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wu, Q., Zhang, H., Jia, W., He, X., Yang, J., and Hintz, T. (2006, January 22\u201324). Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features. Proceedings of the 2006 IEEE International Conference on Video and Signal Based Surveillance, Sydney, Australia.","DOI":"10.1109\/AVSS.2006.30"},{"key":"ref_35","unstructured":"Zhang, H., Jia, W., He, X., and Wu, Q. (2006, January 20\u201324). Learning-Based License Plate Detection Using Global and Local Features. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China."},{"key":"ref_36","first-page":"15","article-title":"Object enhancement and extraction","volume":"10","author":"Prewitt","year":"1970","journal-title":"Pict. Process. Psychopictorics"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Thakur, M., Raj, I., and P, G. (2015, January 19\u201320). The cooperative approach of genetic algorithm and neural network for the identification of vehicle License Plate number. Proceedings of the 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India.","DOI":"10.1109\/ICIIECS.2015.7193090"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3857","DOI":"10.3390\/e17063857","article-title":"Radial Wavelet Neural Network with a Novel Self-Creating Disk-Cell-Splitting Algorithm for License Plate Character Recognition","volume":"17","author":"Cheng","year":"2015","journal-title":"Entropy"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Brillantes, A.K.M., Bandala, A.A., Dadios, E.P., and Jose, J.A. (2018, January 28\u201331). Detection of Fonts and Characters with Hybrid Graphic-Text Plate Numbers. Proceedings of the TENCON 2018\u20142018 IEEE Region 10 Conference, Jeju, South Korea.","DOI":"10.1109\/TENCON.2018.8650097"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mukherjee, R., Pundir, A., Mahato, D., Bhandari, G., and Saxena, G.J. (2017, January 22\u201324). A robust algorithm for morphological, spatial image-filtering and character feature extraction and mapping employed for vehicle number plate recognition. Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India.","DOI":"10.1109\/WiSPNET.2017.8299884"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A Computational Approach to Edge Detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","unstructured":"Sobel, I. (1990). An isotropic 3 \u00d7 3 image gradient operater. Machine Vision for Three-Dimensional Scenes, ResearchGate."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"405","DOI":"10.2991\/ijcis.2017.10.1.28","article-title":"Numeric Character Recognition System for Chilean License Plates in semicontrolled scenarios","volume":"10","author":"Urrea","year":"2017","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Han, J., Yao, J., Zhao, J., Tu, J., and Liu, Y. (2019). Multi-Oriented and Scale-Invariant License Plate Detection Based on Convolutional Neural Networks. Sensors, 19.","DOI":"10.3390\/s19051175"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, S., and Li, Y. (2015, January 19\u201320). A Recognition Algorithm for Similar Characters on License Plates Based on Improved CNN. Proceedings of the 2015 11th International Conference on Computational Intelligence and Security (CIS), Shenzhen, China.","DOI":"10.1109\/CIS.2015.9"},{"key":"ref_46","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"Volume 1","author":"Krizhevsky","year":"2012","journal-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lee, S., Son, K., Kim, H., and Park, J. (2017, January 17\u201319). Car plate recognition based on CNN using embedded system with GPU. Proceedings of the 2017 10th International Conference on Human System Interactions (HSI), Ulsan, South Korea.","DOI":"10.1109\/HSI.2017.8005037"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.eswa.2019.06.036","article-title":"A two-stage deep neural network for multi-norm license plate detection and recognition","volume":"136","author":"Kessentini","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.imavis.2019.04.007","article-title":"Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning","volume":"87","author":"Chen","year":"2019","journal-title":"Image Vis. Comput."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yonetsu, S., Iwamoto, Y., and Chen, Y.W. (2019, January 11\u201313). Two-Stage YOLOv2 for Accurate License-Plate Detection in Complex Scenes. Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, USA.","DOI":"10.1109\/ICCE.2019.8661944"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Abdullah, S., Mahedi Hasan, M., and Muhammad Saiful Islam, S. (2018, January 21\u201322). YOLO-Based Three-Stage Network for Bangla License Plate Recognition in Dhaka Metropolitan City. Proceedings of the 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), Sylhet, Bangladesh.","DOI":"10.1109\/ICBSLP.2018.8554668"},{"key":"ref_54","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Laroca, R., Severo, E., Zanlorensi, L.A., Oliveira, L.S., Gon\u00e7alves, G.R., Schwartz, W.R., and Menotti, D. (2018, January 8\u201313). A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489629"},{"key":"ref_57","unstructured":"Silva, S.M., and Jung, C.R. (2017, January 17\u201320). Real-Time Brazilian License Plate Detection and Recognition Using Deep Convolutional Neural Networks. Proceedings of the 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Niteroi, Brazil."},{"key":"ref_58","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1214\/aoms\/1177729586","article-title":"A Stochastic Approximation Method","volume":"22","author":"Robbins","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Mladenov, V., Jayne, C., and Iliadis, L. (2014). Remarks on Computational Facial Expression Recognition from HOG Features Using Quaternion Multi-layer Neural Network. Engineering Applications of Neural Networks, Springer International Publishing.","DOI":"10.1007\/978-3-319-11071-4"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/TPAMI.2008.111","article-title":"Efficient Visual Search of Videos Cast as Text Retrieval","volume":"31","author":"Sivic","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lowe, D. (1999, January 20\u201327). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_64","unstructured":"Anagnostopoulos, I., Psoroulas, I., Loumos, V., Kayafas, E., Anagnostopoulos, C., and Medialab LPR Database (2019, November 07). Multimedia Technology Laboratory, National Technical University of Athens. Available online: http:\/\/www.medialab.ntua.gr\/research\/LPRdatabase.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3578\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:42:27Z","timestamp":1760175747000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,24]]},"references-count":64,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20123578"],"URL":"https:\/\/doi.org\/10.3390\/s20123578","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,24]]}}}