Hybrid classifier based method for similar handwritten Chinese character recognition

To solve the similar handwritten character recognition problem, a novel scheme is proposed to make better use of the feature's discriminative information. Different from the methods for extracting the extra feature for the similar characters, the Modified Quadratic Discriminant Function(MQDF) is first adopted to classify the feature, then the Support Vector Machine(SVM) is used to discriminate the similar characters without the extra feature. To collect the subset of similar characters, the confusion matrix is employed. A new structure for storing the dictionary of the SVM is also proposed for quickly searching. Experimental results on ETL9B show the superior performance of the proposed scheme to the methods for extracting the extra feature, which proves that the feature contains discriminative information for the similar characters and that the proposed scheme can utilize this information very effectively.