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Title	Arabic Vehicle Licence Plate Recognition Using Deep Learning Methods: Review
ID_Doc	10263
Authors	Alkawsi G.; Baashar Y.; Alkahtani A.A.; Kiong T.S.; Habeeb D.; Aliubari A.
Year	2021
Published	Proceedings - 2021 11th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2021
DOI	http://dx.doi.org/10.1109/ICCSCE52189.2021.9530940
Abstract	Automatic vehicle identification via its license plate is proven to be a valuable solution for smart transportation and smart city applications. The most recent studies explore the implementation of deep learning techniques to improve the license plate recognition performance concerning the challenges and difficulties associated with license plates, such as languages, fonts, distortions, hazardous situations, and blurriness and illumination diversions. In many Middle East countries, vehicle plates include letters, numbers, and city names written in Arabic. Many deep learning approaches have been conducted to improve identification accuracy, with many performance issues. This study reviews the current deep learning methods used in the automatic identification system of such license plates, focusing on the process of deduction, segmentation, and recognition. Methods were analyzed and compared based on applied attributes, strengths, weaknesses, and recognition performance. The paper aims to highlight the research gaps in this area and give some insights into filling them by providing all the related information and proposing new ideas to develop the research further. © 2021 IEEE.
Author Keywords	Arabic characters; artificial intelligent; deep learning; license plate recognition

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