Malaysian License Plate Recognition Algorithm Using Convolutional Neural Network

Nowadays, License Plate Recognition (LPR) becomes popular among researchers due to its compatibility in many applications. For instance, LPR significantly can be applied to toll gate systems, surveillance systems and law enforcement. However, the previous LPR system still does not meet optimum accur...

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Main Author:	Piramli, Muhamad Marzuki
Format:	Thesis
Language:	English English
Published:	2020
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/25422/1/Malaysian%20license%20plate%20recognition%20algorithm%20using%20convolutional%20neural%20network.pdf http://eprints.utem.edu.my/id/eprint/25422/2/Malaysian%20license%20plate%20recognition%20algorithm%20using%20convolutional%20neural%20network.pdf
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id	my-utem-ep.25422
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Ahmad Radzi, Syafeeza
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Piramli, Muhamad Marzuki Malaysian License Plate Recognition Algorithm Using Convolutional Neural Network
description	Nowadays, License Plate Recognition (LPR) becomes popular among researchers due to its compatibility in many applications. For instance, LPR significantly can be applied to toll gate systems, surveillance systems and law enforcement. However, the previous LPR system still does not meet optimum accuracy and speed. Current Convolutional Neural Network (CNN) improvements have the ability to solve complex visual recognition tasks. The primary aim of this system is to ensure that the character of the vehicle plate recognize accurately and efficiently using CNN techniques. A method utilizing two CNN network architectures of deep object detection was designed to solve the Malaysian License Plate Recognition (MLPR) task. The first and the second network were designed for plate detection and recognition of license plate characters respectively. Both of the networks utilized the architecture of YOLOv2 with high speed and accuracy. The accuracy and speed of the plate recognition of the MLPR obtained were 98.75% and 0.0104 seconds respectively. The MLPR has obtained high prediction accuracy and has outperformed the existing methods. In conclusion, the system adapted from deep object detection is the best solution for the MLPR problem based on the accuracy and speed achieved.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Piramli, Muhamad Marzuki
author_facet	Piramli, Muhamad Marzuki
author_sort	Piramli, Muhamad Marzuki
title	Malaysian License Plate Recognition Algorithm Using Convolutional Neural Network
title_short	Malaysian License Plate Recognition Algorithm Using Convolutional Neural Network
title_full	Malaysian License Plate Recognition Algorithm Using Convolutional Neural Network
title_fullStr	Malaysian License Plate Recognition Algorithm Using Convolutional Neural Network
title_full_unstemmed	Malaysian License Plate Recognition Algorithm Using Convolutional Neural Network
title_sort	malaysian license plate recognition algorithm using convolutional neural network
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electronics and Computer Engineering
publishDate	2020
url	http://eprints.utem.edu.my/id/eprint/25422/1/Malaysian%20license%20plate%20recognition%20algorithm%20using%20convolutional%20neural%20network.pdf http://eprints.utem.edu.my/id/eprint/25422/2/Malaysian%20license%20plate%20recognition%20algorithm%20using%20convolutional%20neural%20network.pdf
_version_	1747834124675055616
spelling	my-utem-ep.254222021-12-07T14:03:55Z Malaysian License Plate Recognition Algorithm Using Convolutional Neural Network 2020 Piramli, Muhamad Marzuki T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Nowadays, License Plate Recognition (LPR) becomes popular among researchers due to its compatibility in many applications. For instance, LPR significantly can be applied to toll gate systems, surveillance systems and law enforcement. However, the previous LPR system still does not meet optimum accuracy and speed. Current Convolutional Neural Network (CNN) improvements have the ability to solve complex visual recognition tasks. The primary aim of this system is to ensure that the character of the vehicle plate recognize accurately and efficiently using CNN techniques. A method utilizing two CNN network architectures of deep object detection was designed to solve the Malaysian License Plate Recognition (MLPR) task. The first and the second network were designed for plate detection and recognition of license plate characters respectively. Both of the networks utilized the architecture of YOLOv2 with high speed and accuracy. The accuracy and speed of the plate recognition of the MLPR obtained were 98.75% and 0.0104 seconds respectively. The MLPR has obtained high prediction accuracy and has outperformed the existing methods. In conclusion, the system adapted from deep object detection is the best solution for the MLPR problem based on the accuracy and speed achieved. 2020 Thesis http://eprints.utem.edu.my/id/eprint/25422/ http://eprints.utem.edu.my/id/eprint/25422/1/Malaysian%20license%20plate%20recognition%20algorithm%20using%20convolutional%20neural%20network.pdf text en public http://eprints.utem.edu.my/id/eprint/25422/2/Malaysian%20license%20plate%20recognition%20algorithm%20using%20convolutional%20neural%20network.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=119752 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Electronics and Computer Engineering Ahmad Radzi, Syafeeza 1. Baştanlar, Y. and Özuysal, M., 2014. Introduction to machine learning. miRNomics: MicroRNA Biology and Computational Analysis, pp. 105-128. 2. Dickson N. and Sahari K.S.M., 2016. Character recognition of Malaysian vehicle license plate with deep convolutional neural networks. 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Malaysian License Plate Recognition Algorithm Using Convolutional Neural Network

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