| The information fusion technique has been widely and successfully applied to many fields, and is valuable especially to pattern recognition. It has been turned out that, for complicated pattern recognition problems such as recognizing handwritten characters, there exists no reliable and simple way to perform a high recognition rate. Every method has its advantages, limitations and applications, and is complementary for each other. So how to combine the advantages of various methods to develop an information fusion recognition system and overcome the disadvantages became one of the hottest themes of pattern recognition. The main works of this paper are listed as follows:1. To handle the deformations is one of the key problems of handwritten Chinese characters recognition. The goal of normalization is to correct the deformation of handwritten character images and reduce the differences of the character images, which belong to the same class. A new nonlinear normalization method was developed, after I carefully researched the existed normalization methods. This method is based on line density method, and the line density filling in algorithm is used to perform the coordinate transformation. This makes the description of the stroke density more reasonable, and the distribution of strokes in normalized character image more proportional. 2. The features of Chinese characters characterize the unitary and local shape of the characters. Under a set of well-selected features, the distances of character samples of same class ought to be small; at the same time, the distances of character samples of different classes ought to be prominently large. According to the characteristics of handwritten Chinese characters, I developed some new features such as improved directional element feature, stroke sub-area moment, extended circumjacent stroke directional element feature, etc. These features are proved to be effective in experiments.3. I studied the handwritten Chinese character recognition techniques based on adaptive information fusion and module neural networks. I provided a new method of multi-feature fusion on the basis of thetraditional feature-extraction methods. The high dimension of feature vector can be reduced by generalized K-L transformation, and the new feature vector is produce with the predominance of the single features for classification. The classifier is a multi-module neural network, so the conflict between the scale of BP network and the complexity of recognition tasks can be transformed to the conflict between the scale of recognition system and the complexity of recognition tasks. In the meantime, an adaptive feature subspace was constructed by using genetic algorithm to select features and optimize the structure of the neural network. The subspace of classification was optimized by training samples too. Finally, an adaptive feature-generating neural network module was formed. The recognition experiments have verified the method.4. By analyzing the theory of neural network integration, I developed a multi-level neural network model for recognition handwritten Chinese characters. The PCA network is used as the first level to extract the principle component of the features of characters, and reduce the scale of the neural network of subsequent level. The second level is a GLVQ network. The function of the level is to classify the principle component feature vectors produced by the PCA network. The novel GLVQ algorithm was induced to optimize an object function, it provides a new way to overcome the exist problem of LVQ algorithms. I analyzed and improved the basic mathematic theory of GLVQ, designed a more efficient classification algorithm based on the theory, and applied the algorithm to handwritten Chinese character recognition. The algorithm improves the recognition rate evidently, and has well capability of generalization. It is obviously superior to traditional BP algorithms.5. A multi-neural network fusion structure was developed and was applied to recognize the limited set of...
|
PDF Full Text Request