| The object of pattern recognition technology is constructing a system to automatically classify, recognize and process information through computer simulation according to the mechanism of human's thinking. Pattern recognition technology is of great significance in living and science investigation, and has already been used in many fields. Character recognition is a very important and active research area in pattern recognition. Theoretically, it is not an isolated technique. It involves the problems that all the other areas of pattern recognition must face. Practically, as a kind of information processing technology, character recognition has a very broad application background. The need of market is the basic motive force of the rapid development of character recognition. Thus, it is of both theoretical and practical significance.In this thesis, the methods of feature extraction and classification frequently used in character recognition are demonstrated and the relationship between classification and integration is thoroughly analyzed. According to the basic idea of comprehensive integration, classification and integration methods are developed and a recognition system is established for typical character set.Chinese has the feature of a large vocabulary, complex structures and lots of similar characters. Large vocabulary brings about the difficulties in directly using neural network to classify and integrate. Complex structures and many similar characters make it very hard to use traditional structure analysis and statistical methods to get satisfying classification results. Aiming at these problems, the proposed network integration method is improved. Three minimum distance classifiers, which extract different local features, are proposed and they are combined to form an integration system by making use of the above methods. The measurement results show that the recognition rate of the integration system is higher than that of the best single classifier. |
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