A Design Of License Plate Recognition System Using Convolutional Neural Network

Ahmad Radzi, Syafeeza and Piramli, Muhamad Marzuki and Wong, Yan Chiew and Abdul Hamid, Norihan and Ali, Nur Alisa and Mat Ibrahim, Masrullizam (2019) A Design Of License Plate Recognition System Using Convolutional Neural Network. International Journal Of Electrical And Computer Engineering (IJECE), 9 (3). pp. 2196-2204. ISSN 2088-8708

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Abstract

This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.

Item Type:	Article
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Electronics and Computer Engineering > Department of Computer Engineering
Depositing User:	Mohd Hannif Jamaludin
Date Deposited:	05 Mar 2020 12:14
Last Modified:	05 Mar 2020 12:14
URI:	http://eprints.utem.edu.my/id/eprint/24052
Statistic Details:	View Download Statistic

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