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

Accidents; 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; tra...

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Main Authors: Habeeb D., Noman F., Alkahtani A.A., Alsariera Y.A., Alkawsi G., Fazea Y., Al-Jubari A.M.
Other Authors: 57219414936
Format: Article
Published: Hindawi Limited 2023
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spelling my.uniten.dspace-264272023-05-29T17:10:24Z Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition Habeeb D. Noman F. Alkahtani A.A. Alsariera Y.A. Alkawsi G. Fazea Y. Al-Jubari A.M. 57219414936 55327881300 55646765500 57216243342 57191982354 56803894200 36607497500 Accidents; 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 Learning Recognizing 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. Final 2023-05-29T09:10:24Z 2023-05-29T09:10:24Z 2021 Article 10.1155/2021/3971834 2-s2.0-85121990937 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121990937&doi=10.1155%2f2021%2f3971834&partnerID=40&md5=473b1cb6070e4ccfa75002450746e8ce https://irepository.uniten.edu.my/handle/123456789/26427 2021 3971834 All Open Access, Gold, Green Hindawi Limited Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Accidents; 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 Learning
author2 57219414936
author_facet 57219414936
Habeeb D.
Noman F.
Alkahtani A.A.
Alsariera Y.A.
Alkawsi G.
Fazea Y.
Al-Jubari A.M.
format Article
author Habeeb D.
Noman F.
Alkahtani A.A.
Alsariera Y.A.
Alkawsi G.
Fazea Y.
Al-Jubari A.M.
spellingShingle Habeeb D.
Noman F.
Alkahtani A.A.
Alsariera Y.A.
Alkawsi G.
Fazea Y.
Al-Jubari A.M.
Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition
author_sort Habeeb D.
title Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition
title_short Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition
title_full Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition
title_fullStr Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition
title_full_unstemmed Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition
title_sort deep-learning-based approach for iraqi and malaysian vehicle license plate recognition
publisher Hindawi Limited
publishDate 2023
_version_ 1806428441437798400
score 13.222552

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