Template neural particle optimization for vehicle license plate recognition

The need for vehicle recognition has emerged from cases such as security, smart toll collections and traffic monitoring systems. This type of applications produces high demands especially on the accuracy of license plate recognition (LPR). The challenge of LPR is to select the best method for recogn...

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Main Author:	A Jalil, Norazira
Format:	Thesis
Language:	English English
Published:	2015
Subjects:	Q Science (General) QA Mathematics QA76 Computer software
Online Access:	http://eprints.utem.edu.my/id/eprint/16822/1/Template%20Neural%20Particle%20Optimization%20For%20Vehicle%20License%20Plate%20Recognition.pdf http://eprints.utem.edu.my/id/eprint/16822/2/Template%20neural%20particle%20optimization%20for%20vehicle%20license%20plate%20recognition.pdf
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id	my-utem-ep.16822
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Hasan Basari, Abd Samad
topic	Q Science (General) QA Mathematics QA76 Computer software
spellingShingle	Q Science (General) QA Mathematics QA76 Computer software A Jalil, Norazira Template neural particle optimization for vehicle license plate recognition
description	The need for vehicle recognition has emerged from cases such as security, smart toll collections and traffic monitoring systems. This type of applications produces high demands especially on the accuracy of license plate recognition (LPR). The challenge of LPR is to select the best method for recognizing characters. Since the importance of LPR arises over times, there is a need to find the best alternative to overcome the problem. The detection and extraction of license plate is conventionally based on image processing methods. The image processing method in license plate recognition generally comprises of five stages including pre-processing, morphological operation, feature extraction, segmentation and character recognition. Pre-processing is an initial step in image processing to improve image quality for more suitability in visualizing perception or computational processing while filtering is required to solve contrast enhancement, noise suppression, blurry issue and data reduction. Feature extraction is applied to locate accurately the license plate position and segmentation is used to find and segment the isolated characters on the plates, without losing features of the characters. Finally, character recognition determines each character, identity and displays it into machine readable form. This study introduces five methods of character recognition namely template matching (TM), back-propagation neural network (BPNN), Particle Swarm Optimization neural network (PSONN), hybrid of TM with BPNN (TM-BPNN) and hybrid of TM with PSONN (TM-PSONN). PSONN is proposed as an alternative to train feed-forward neural network, while TM-BPNN and TM-PSONN are proposed to produce a better recognition result. The performance evaluation is carried out based on mean squared error, processing time, number of training iteration, correlation value and percentage of accuracy. The performance of the selected methods was analyzed by making use real images of 300 vehicles. The hybrid of TM-BPNN gives the highest recognition result with 94% accuracy, followed by the hybrid of TM-PSONN with 91.3%, TM with 77.3%, BPNN with 61.7% and lastly PSONN with 37.7%.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	A Jalil, Norazira
author_facet	A Jalil, Norazira
author_sort	A Jalil, Norazira
title	Template neural particle optimization for vehicle license plate recognition
title_short	Template neural particle optimization for vehicle license plate recognition
title_full	Template neural particle optimization for vehicle license plate recognition
title_fullStr	Template neural particle optimization for vehicle license plate recognition
title_full_unstemmed	Template neural particle optimization for vehicle license plate recognition
title_sort	template neural particle optimization for vehicle license plate recognition
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Information And Communication Technology
publishDate	2015
url	http://eprints.utem.edu.my/id/eprint/16822/1/Template%20Neural%20Particle%20Optimization%20For%20Vehicle%20License%20Plate%20Recognition.pdf http://eprints.utem.edu.my/id/eprint/16822/2/Template%20neural%20particle%20optimization%20for%20vehicle%20license%20plate%20recognition.pdf
_version_	1747833896848850944
spelling	my-utem-ep.168222022-06-01T15:30:03Z Template neural particle optimization for vehicle license plate recognition 2015 A Jalil, Norazira Q Science (General) QA Mathematics QA76 Computer software The need for vehicle recognition has emerged from cases such as security, smart toll collections and traffic monitoring systems. This type of applications produces high demands especially on the accuracy of license plate recognition (LPR). The challenge of LPR is to select the best method for recognizing characters. Since the importance of LPR arises over times, there is a need to find the best alternative to overcome the problem. The detection and extraction of license plate is conventionally based on image processing methods. The image processing method in license plate recognition generally comprises of five stages including pre-processing, morphological operation, feature extraction, segmentation and character recognition. Pre-processing is an initial step in image processing to improve image quality for more suitability in visualizing perception or computational processing while filtering is required to solve contrast enhancement, noise suppression, blurry issue and data reduction. Feature extraction is applied to locate accurately the license plate position and segmentation is used to find and segment the isolated characters on the plates, without losing features of the characters. Finally, character recognition determines each character, identity and displays it into machine readable form. This study introduces five methods of character recognition namely template matching (TM), back-propagation neural network (BPNN), Particle Swarm Optimization neural network (PSONN), hybrid of TM with BPNN (TM-BPNN) and hybrid of TM with PSONN (TM-PSONN). PSONN is proposed as an alternative to train feed-forward neural network, while TM-BPNN and TM-PSONN are proposed to produce a better recognition result. The performance evaluation is carried out based on mean squared error, processing time, number of training iteration, correlation value and percentage of accuracy. The performance of the selected methods was analyzed by making use real images of 300 vehicles. The hybrid of TM-BPNN gives the highest recognition result with 94% accuracy, followed by the hybrid of TM-PSONN with 91.3%, TM with 77.3%, BPNN with 61.7% and lastly PSONN with 37.7%. 2015 Thesis http://eprints.utem.edu.my/id/eprint/16822/ http://eprints.utem.edu.my/id/eprint/16822/1/Template%20Neural%20Particle%20Optimization%20For%20Vehicle%20License%20Plate%20Recognition.pdf text en public http://eprints.utem.edu.my/id/eprint/16822/2/Template%20neural%20particle%20optimization%20for%20vehicle%20license%20plate%20recognition.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=96169 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Information And Communication Technology Hasan Basari, Abd Samad 1. Abbas, Q., Ahmad, J. and Bangyal, W.H., 2013. Dynamic Hidden Layers Selection of ANN Architecture Using Particle Swarm Optimization. International Journal of Engineering and Technology, 5(2), pp.195–197. 2. 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Template neural particle optimization for vehicle license plate recognition

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