关键词: 车牌字符识别, 多特征提取, 支持向量机, 神经网络, K近邻

Abstract: To solve the limitation of most of the single-feature extraction methods in vehicle license plate recognition，a method based on multi-feature extraction is presented.After pre-processing，different kinds of features are extracted，including the strokes features，inflection point features and contour features of four outsides of characters as well as the internal pixel features based on the thinned characters.These features are then converted into a lower-dimension feature vector set，on which Support Vector Machines（SVM），K Nearest Neighbor（KNN），Back Propagation Neural Network（BP-NN），Radial Basis Function Neural Network（RBF-NN） can be built.These classifiers are tested on the vehicle images taken from the Lufeng Freeway.This paper compares the proposed method with the pattern matching method，grid method and the wavelet moment method on performance.The experimental results show that the proposed multi-feature extraction method has high recognition rate and robustness.

Key words: vehicle license plate recognition, multiple features extraction, support vector machine, neural network, K Nearest Neighbor（KNN）