Research On Method Of Off-line Handwritten Chinese Characters Recognizing Based On Binary Tree SVM

The study of Chinese character recognition is regarded as not only a important theory meaning and practice value direction in pattern recognition field, but a final goal to the research of character recognition. Chinese Characters recognition is one aspect of pattern recognition field. Support Vector Machine (SVM) is a leaning method for especially studying small-sample prediction, which is based on Statistical Learning Theory. It can well solve the construction issue of a high dimensional model of small-sample set. It can get a biggish theory meaning and practice value that the SVM theory is used for the off-line Handwritten Chinese Characters Recognizing.The primary contents of this thesis are:1) Chinese characters are composed of complication and structure. A method based on the pixels density is adopted, Chinese characters is divided into simple and complexity Chinese by this method. A method based on the combination of horizontal and vertical projection with connected component is adopted, the Chinese characters is divided into impartibility Chinese and separable Chinese.2) binary tree SVM. the problems associated with complex pattern and multi-classification in off-line written Chinese characters recognition are addressed and a method of classification recognition combined with binary tree SVM(support vector machine) and "one against rest" SVM are presented. A binary tree SVM multi-classification is presented. It can make coarse classification. SVM toolbox is used as the realization methods in this thesis. The classification of various style script Chinese character images depending on the above Chinese character image classification machine structures are accomplished successfully.3) written Chinese characters recognition machine. The feature extraction method based on six method combined is proposed. because the Chinese character have different character. So the different feature extraction method is adopted. The classification based on "one against rest" SVM is adopted to recognition.The experimental results indicate that the method of classification recognition combined with binary tree SVM (support vector machine) and "one against rest" SVM can exerted the superiority for 2-class classification of SVM over simple SVM algorithms completely. The generalization ability has improved greatly. The new method yields higher precision and speeds up support vector machine multi-class classification.