Applying a hybrid method to handwritten character recognition

We propose a new prototype learning/matching method that can be combined with support vector machines (SVM) in pattern recognition. This hybrid method has the following merits. One, the learning algorithm for constructing prototypes determines both the number and the location of prototypes. This algorithm terminates within a finite number of iterations and assures that each training sample matches in class types with the nearest prototype. Two, SVM can be used to process top-rank candidates obtained by the prototype learning/matching method so as to save time in both training and testing processes. We apply our method to recognizing handwritten numerals and handwritten Chinese/Hiragana characters. Experiment results show that the hybrid method saves great amount of training and testing time in large-scale tasks and achieves comparable accuracy rates to those achieved by using SVM solely. Our results also show that the hybrid method performs better than the nearest neighbour method.