Publication:
Arabic Vehicle Licence Plate Recognition Using Deep Learning Methods: Review

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dc.citedby1
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorBaashar Y.en_US
dc.contributor.authorAlkahtani A.A.en_US
dc.contributor.authorKiong T.S.en_US
dc.contributor.authorHabeeb D.en_US
dc.contributor.authorAliubari A.en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid56768090200en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid57216824752en_US
dc.contributor.authorid57219414936en_US
dc.contributor.authorid57283147900en_US
dc.date.accessioned2023-05-29T09:06:15Z
dc.date.available2023-05-29T09:06:15Z
dc.date.issued2021
dc.descriptionAutomatic vehicle identification; Automation; Deep learning; Optical character recognition; Arabic characters; Artificial intelligent; Deep learning; Identification accuracy; Learning approach; Learning methods; Learning techniques; Middle East; Performance; Vehicle license plate recognition; License plates (automobile)en_US
dc.description.abstractAutomatic 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICCSCE52189.2021.9530940
dc.identifier.epage79
dc.identifier.scopus2-s2.0-85116204647
dc.identifier.spage75
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85116204647&doi=10.1109%2fICCSCE52189.2021.9530940&partnerID=40&md5=d14eec39371472847409a8d448186a6a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26037
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings - 2021 11th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2021
dc.titleArabic Vehicle Licence Plate Recognition Using Deep Learning Methods: Reviewen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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