Vehicle License Plate Recognition System Using Wavelet And Neural Networks

Vehicle license plate recognition technology is very important in Intelligent Transportation Systems. There are three primary parts in vehicle license plate recognition system: license plate location, license plate characters segmentation and license plate characters recognition. The three pivotal technologies are studied and corresponding settle methods are presented in this paper. The research work is developed in the following aspects.(1) A kind of location method of vehicle license plate based on energy filter and wavelet is presented in this paper. The energy function is constructed according to the high and concentrated energy in the horizontal direction. The vehicle license plate image is roughly located by energy filter, and the license plate is accurately located by wavelet analysis and a series of morphological operations. The experimental results demonstrate the efficiency of the proposed approach.(2) A kind of segmentation method of vehicle license plate characters based on neural networks and color feature is presented. The vehicle license plate images are binarized using BP neural network after the kinds of the vehicle license plates have been judged, the vehicle license plate characters are accurately segmented by using the projection method and the characters' connexity. The experimental results demonstrate the efficiency of the proposed approach.(3) The kind and the dimension of the feature vectors have important infection on vehicle license plate recognition. A kind of character recognition method of the vehicle license plate based on the wavelet packet and zernike moments is presented in this paper. The wavelet packet coefficients and the zernike moments make up the feature space, which is processed by reducing the dimension. The digits of the vehicle license plate are recognized by BP neural network. The experimental results demonstrate the efficiency of the proposed approach.(4) A kind of character recognition method of the vehicle license plate based on support vector machines is presented in this paper. Firstly, a Chinese character recognition sub-network, a English character recognition sub-network, a Chinese character and English character recognition sub-network and a digital character recognition sub-network are constructed for Chinese vehicle license plate characters' properties. Then, the characters are recognized by SVM in every sub-network. The experimental results: vehicle license plate character recognition using SVM is better than the BP neural networks and RBF neural networks.(5) The performance of SVM with Gauss kernel is influenced greatly by the penalty parameter C and the scale parameter o. A kind of method to select these parameters using genetic algorithms(GA) is proposed based on the study of vehicle license plate characters recognition. The characters of the vehicle license plate are recognized by SVM with optimized parameters in various sub-networks. The experimental results demonstrate the efficiency of the proposed approach.