Pengecaman Nombor Plat Kenderaan Menggunakan Rangkaian Neural dan Pengelompokan Berbilang Aras

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). The 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 multi-layered perceptron trained using the back-propagation algorithm. The prototyped system has an accuracy of about 91%, however, suggestions to further improve the system are discussed in this paper based on the analysis of the error.