A prototype classification method and its application to handwritten character recognition

We propose a new prototype classification method that can be combined with support vector machines (SVM) (Cortes, C and Vapnik, V, 1995) for recognizing handwritten numerals and Chinese characters. This method employs a learning process for determining both the number and location of prototypes. The possible techniques used in this process for adjusting the location of prototypes include the K-means (KM) algorithm and the fuzzy c-means (FCM) algorithm (Bezdek, J. C., 1981). When the prototype classification method is applied, the SVM method can be used to process top rank candidates obtained in the prototype learning or matching process. We apply this hybrid method to the recognition of handwritten numerals and Chinese characters. Experiment results show that this hybrid method saves great amount of training and testing time when the number of character types is large, and achieves comparable accuracy rates to those achieved by using SVM solely. Our results also show that the proposed method performs better than the nearest neighbor (NN) classification method. These outcomes suggest that the proposed method can serve as an effective solution for large-scale multiclass classification