| Summary: | 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.
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