License Plate Recognition using Multi-cluster and Multilayer Neural Networks

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 require-ment of an automatic license plate recognition sys-temr is rather different for each country. In this pa-per, 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 Vi-sion System Development Platform (VSDP). Multi-Cluster approach is applied to locate the license plate at the right position while Kirsch Edge feature extrac-tion technique is used to extract features from the li-cense 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 dts-cussed in this paper based on the analysis of the error