Study On Recognition Of Off-line Similar Handwritten Chinese Haracters Based On Support Vector Machines

Off-line handwritten Chinese characters recognition technology can be widely used inmany areas, but it is very difficult in realizing the recognition system because of its complexityand until now there is not perfect product. Investigation shows that the existence of lots ofsimilar characters is a key factor affecting recognition rate. Therefore, much deeper researchesshould be made to solve the difficult problem of similar handwritten Chinese characterrecognition. Since the approach based on support vector machines (SVM) is fit for smallsamples and small categories recognition problem, the dissertation investigates some keyproblems of SVM using in similar handwritten Chinese character recognition. The contributionsof this dissertation are presented as follows.Firstly, an approach for automatic model selection for SVM classifier is proposed based onthe Riemannian geometry analysis of kernel function. The optimal model parameters areobtained based on the evaluation criterion of model selection and the global search algorithm ofcoarse grid combined with pattern search. Then, the novel conformal transformation presentedin this dissertation is adopted and the kernel function is modified by the transformation in adata-dependent way. And the experimental results show remarkable improvement of thegeneralization performance of the classifier.Secondly, two different schemes for feature selection are investigated. One is based onsingle objective improved genetic algorithms and cross-validation-based SVM classification forsingle objective feature selection problem. The other is based on pareto-optimality-based multi-objecitve genetic algorithms and SVM classification for multi-objective feature selectionproblem. They all belong to the wrapper methods, which utilize the feedback information fromSVM classifier. The feature vector with low dimension can be found without any loss to thegeneralization performance.Thirdly, aiming at the deficiency of DAGSVM classifier, a novel fuzzy multi-classDAGSVM classifier based on optimal structure is proposed. According to the performance evaluation criterion, the algorithm for optimizing the structure of DAGSVM is introduced in thetraining stage. The final recognition result is obtained by fuzzy multi-class DAGSVM classifierusing the fuzzy membership function and average operator in the testing stage. Theexperimental results show that the recognition precision and rate of the novel classifier are allbetter than those of pair-wise SVM classifier with other combination strategies.Lastly, a practical sample library for similar handwritten Chinese characters is built on theanalysis of the similar property of objective similar handwritten Chinese characters, whichprovides the foundation for the future research. A recognition approach for similar handwrittenChinese characters is presented based on extracting feature vector by wavelet transformationand elastic meshing technique, selecting feature subset by genetic algorithms and classifying bySVM. The experimental results confirm the effectiveness and practicality of the approach.