Universiti Teknologi Malaysia Institutional Repository

License Plate Recognition using Multi-cluster and Multilayer Neural Networks

Sheikh Abdullah, Siti Norul Huda and Khalid, Marzuki and Yusof, Rubiyah (2006) License Plate Recognition using Multi-cluster and Multilayer Neural Networks. 2nd International Conference on Information and Communication Technologies , 1 . pp. 1818-1823.


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Vehicle license plat recognition has been a much studied research area in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is rather different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, feature extraction and neural networks. The image-processing library is developed in-house which we referred to as Vision System Development Platform (VSDP). Multi-Cluster approach is applied to locate the license plate at the right position while Kirsch Edge feature extraction technique is used to extract features from the license plates characters which are then used as inputs to the neural network classifier. The neural network model is the standard multilayered perceptron trained using the back-propagation algorithm. The prototyped system has an accuracy of more than 91%, however, suggestions to further improve the system are dtscussed in this paper based on the analysis of the error.

Item Type:Article
Uncontrolled Keywords:License plate recognition, clustering, feature extraction, classification
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:1645
Deposited By: Dr Zaharuddin Mohamed
Deposited On:09 Mar 2007 01:21
Last Modified:01 Jun 2010 02:56

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