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A. Nur Alisa
Technical University of Malaysia Malacca
Computer Science & IT Electrical & Electronics Engineering

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A design of license plate recognition system using convolutional neural network P. Marzuki; A. R. Syafeeza; Y. C. Wong; N. A. Hamid; A. Nur Alisa; M. M. Ibrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (685.701 KB) | DOI: 10.11591/ijece.v9i3.pp2196-2204

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.
Co-Authors A. R. Syafeeza M. M. Ibrahim N. A. Hamid P. Marzuki Y. C. Wong