Publication:
Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

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dc.citedby2
dc.contributor.authorHabeeb D.en_US
dc.contributor.authorNoman F.en_US
dc.contributor.authorAlkahtani A.A.en_US
dc.contributor.authorAlsariera Y.A.en_US
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorFazea Y.en_US
dc.contributor.authorAl-Jubari A.M.en_US
dc.contributor.authorid57219414936en_US
dc.contributor.authorid55327881300en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid57216243342en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid56803894200en_US
dc.contributor.authorid36607497500en_US
dc.date.accessioned2023-05-29T09:10:24Z
dc.date.available2023-05-29T09:10:24Z
dc.date.issued2021
dc.descriptionAccidents; Deep learning; Deterioration; Optical character recognition; Atmospheric environment; Learning-based approach; Learning-based methods; Malaysia; Malaysians; Recognition systems; State-of-the-art methods; Vehicle license plate recognition; License plates (automobile); machine learning; traffic accident; Accidents, Traffic; Deep Learning; Machine Learningen_US
dc.description.abstractRecognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obstacle, particularly in countries whose plate numbers are written in different languages or blended with Latin alphabets. This paper introduces a recognition system for Arabic and Latin alphabet license plates using a deep-learning-based approach in conjugation with data collected from two specific countries: Iraq and Malaysia. The system under study is proposed to detect, segment, and recognize vehicle plate numbers. Moreover, Iraqi and Malaysian plates were used to compare these processes. A total of 404 Iraqi images and 681 Malaysian images were tested and used for the proposed techniques. The evaluation took place under various atmospheric environments, including fog, different contrasts, dirt, different colours, and distortion problems. The proposed approach showed an average recognition rate of 85.56% and 88.86% on Iraqi and Malaysian datasets, respectively. Thus, this evidences that the deep-learning-based method outperforms other state-of-the-art methods as it can successfully detect plate numbers regardless of the deterioration level of image quality. Copyright � 2021 Dhuha Habeeb et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo3971834
dc.identifier.doi10.1155/2021/3971834
dc.identifier.scopus2-s2.0-85121990937
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85121990937&doi=10.1155%2f2021%2f3971834&partnerID=40&md5=473b1cb6070e4ccfa75002450746e8ce
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26427
dc.identifier.volume2021
dc.publisherHindawi Limiteden_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleComputational Intelligence and Neuroscience
dc.titleDeep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognitionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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