Research On Off-line Handwritten Chinese Character Recognition

Off-line handwritten Chinese character recognition (HCCR) is the current hot spots of OCR technology research, and is also one of the most difficult task of the computer character recognition. The research on off-line handwritten Chinese character recognition is quite significant for the automatic processing of Chinese character information and the development of intelligent input of the new generation computer. Handwritten Chinese character recognition is a very complicated multi-pattern recognition issue, years of research shows that the effect of a single method is limited, and various methods have their own characteristics and advantages, but also have their limitations. Multi-feature fusion and integration of multiple schemes are considered to be a trend for the development of handwritten Chinese character recognition with the use of information fusion technology and the organic combination of multiple methods.Since the individual classifier can not fundamentally improve the classification performance effectively, and the integration of multiple classifiers is required to solve the problem, a three-level serial classifier combination model was designed on the basis of analyzing the development of current technology for Chinese character recognition. This model was based on multi-feature fusion and multiple integrated classifiers. In this model, the distance classifiers and the neural network classifier which were serially integrated were combined with three different feature extraction methods to form a recognition system. The optimal integration strategies of different design schemes of systems were discussed and finally the integration model was obtained. On the first level, the characters are rudely classified by Manhattan distance classifier based on the peripheral feature of uniform meshes; On the second level, instead of using the traditional method of extracting characters stroke density feature on the basis of uniform meshes, the characters stroke density feature was extracted on the basis of elastic meshes partition, and the fine classification was also performed using similarity classifier; On the third level, four directional line element decomposition feature was extracted on the basis of elastic meshes partition, and the popular BP neural network classifier was selected to confirm the classification of the candidate results according to the former recognition results of the two classifiers.In this paper a small number of commonly used Chinese characters were studied, and the research target is to explore the effective algorithm for recognition of off-line handwritten Chinese characters which is non-special and low limited. 50 Chinese characters are selected from first level library Chinese character of GB2312-80 in experiment. 100 samples of each Chinese character have been collected, and the total samples reached 5,000. Matlab7.1 toolbox was used to carry out a preliminary model simulation experiment, and the results show that the model is effective.In this paper, five modules of Chinese sample collection, image preprocessing, rough classification, fine classification and experimental results analysis were detailed. The preprocessing of character image samples includes the establishment and access operation of Chinese characters to samples database and the processing of image binarization, smooth denoising, lean adjustment, segmentation, normalization of size and normalization of position. The principle of BP neural network classifiers, the network architecture and the choice of its parameters were introduced in the design of classifiers. The disadvantages of BP algorithm and the way of making improvement were then discussed, and finally the BP network training and simulation are realized by programming with the use of Matlab7.1 neural network toolbox.