The Research Of An Improved SVMs Applied In Hand-Writing Character Recognition

Pattern recognition is an information processing technique of artificial intelligen -ce, which is recently widely applied in many fields such as letter recognition, finger mark recognition and remote sensing image recognition. The process of pattern recognition is approximately divided into three steps: preprocessing, character distilling and recognition. Preprocessing finishes the prophase job to make it easier to do the following recognizing work, which includes binarization, smoothness and refinement such image standardization operations. Character distilling distills recognizing character of the input object as a point or a character vector. Recognition completes the classification which classifies the above-mentioned character vector by classifier which gets the final result by decision-making function.The paper mainly researches a traditional classifier: support vector machine (SVM) which is diffusely used in recent years. Support vector machine is a machine learning method developing from the basic of statistic learning theory, which shows special superiorities in dealing problems of small sample, nonlinear and multidimen -sional recognitions. But there are many problems that need to be settled immediately in the traditional SVM: 1) There are no fixed standardizations in the selection of kernel functions and parameters of SVM; 2) SVM can only deal with the two-sample problem and can do nothing for the multi-classification problem. Genetic algorithm (GA) is a useful algorithm to search the optimal solution which imitates the natural evolution process of life and searches randomly in the objective space with artificial evolution mode, discarding the traditional optional search method. Genetic algorithm demands nothing for question itself but need to estimate every object generated by algorithm and to search the optimal object to settle problems through gene effecting on object. The evolutional searching advantage of genetic algorithm can help search the appropriate kernel function parameters of SVM in the multi-generation search that best resolves the problem of no fixed standardization for SVM parameters. At the same time, we can get the result of multi-classification by integrating SVM to normal trees mode.Chinese character recognition identifies the characters by computer that is printed or written down on paper, which belongs to the field of pattern recognition and artificial intelligence. Nowadays with the information technique developing so fast, more and more hand-writing characters are needed to be handled by computer system which makes it a serious problem to find a best way to recognize hand-writing characters.The paper presents GA-SVMs combining GA algorithm and normal tree and develops a hand-writing recognition system which applies this improved SVM. GA-SVMs can quickly find the optimal SVM parameters which enhances the accuracy of classification. Totally speaking, GA-SVMs represent elevation in accuracy of classification by study and experiments.