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

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 number...

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Autores principales:	Dhuha Habeeb, Fuad Noman, Ammar Ahmed Alkahtani, Yazan A. Alsariera, Gamal Alkawsi, Yousef Fazea, Ammar Mohammed Al-jubari
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/569b0a5376ef400ebf6c36c9aa3b4723
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spelling	oai:doaj.org-article:569b0a5376ef400ebf6c36c9aa3b47232021-11-15T01:19:13ZDeep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition1687-527310.1155/2021/3971834https://doaj.org/article/569b0a5376ef400ebf6c36c9aa3b47232021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3971834https://doaj.org/toc/1687-5273Recognizing 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.Dhuha HabeebFuad NomanAmmar Ahmed AlkahtaniYazan A. AlsarieraGamal AlkawsiYousef FazeaAmmar Mohammed Al-jubariHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Dhuha Habeeb Fuad Noman Ammar Ahmed Alkahtani Yazan A. Alsariera Gamal Alkawsi Yousef Fazea Ammar Mohammed Al-jubari Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition
description	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.
format	article
author	Dhuha Habeeb Fuad Noman Ammar Ahmed Alkahtani Yazan A. Alsariera Gamal Alkawsi Yousef Fazea Ammar Mohammed Al-jubari
author_facet	Dhuha Habeeb Fuad Noman Ammar Ahmed Alkahtani Yazan A. Alsariera Gamal Alkawsi Yousef Fazea Ammar Mohammed Al-jubari
author_sort	Dhuha Habeeb
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	2021
url	https://doaj.org/article/569b0a5376ef400ebf6c36c9aa3b4723
work_keys_str_mv	AT dhuhahabeeb deeplearningbasedapproachforiraqiandmalaysianvehiclelicenseplaterecognition AT fuadnoman deeplearningbasedapproachforiraqiandmalaysianvehiclelicenseplaterecognition AT ammarahmedalkahtani deeplearningbasedapproachforiraqiandmalaysianvehiclelicenseplaterecognition AT yazanaalsariera deeplearningbasedapproachforiraqiandmalaysianvehiclelicenseplaterecognition AT gamalalkawsi deeplearningbasedapproachforiraqiandmalaysianvehiclelicenseplaterecognition AT youseffazea deeplearningbasedapproachforiraqiandmalaysianvehiclelicenseplaterecognition AT ammarmohammedaljubari deeplearningbasedapproachforiraqiandmalaysianvehiclelicenseplaterecognition
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Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

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