The car plate recognition system is an indispensable part of intelligent traffic system. In car plate recognition system

one of the most difficult problems is the feature and recognition of car plate Chinese character. The multiresolution property of wavelet is applied to extract car plate Chinese character feature extraction

and a method that directly extracts feature from gray scale image is proposed. The method firstly extracts two kinds of features: one is wavelet moments in different multiresolution

and the other is the zoning density of wavelet-decomposed image. Then

two kinds of features are combined and a feature selection algorithm based recognition rate is presented to choose better features. Finally

the selected features are as the input of BP neural network

which is adopted to recognize car plate Chinese character. As a result

the proposed method avoids the binary operation used in some traditional Chinese character feature extractions that will seriously destroy the Chinese character structure. Furthermore

the extracted features can describe the local and global property of the character. Compared with some other feature extraction methods

the proposed method can achieve better recognition performance.