Research on Handwritten Chinese Character Recognition Using Feature Fusion and Modular RBF Classifier

This paper analysis the feature extraction method of handwritten Chinese character. The contour direction feature(CDF)and directional distance distributions feature(DDDF) are extracted from Chinese character image as a pair of feature vectors having good complementarity. After dimensions reduction of original feature using Karhunen-Loeve transform, these two feature vectors are combined to produce a new feature vector has high discriminating powers. Basing on the characteristic of RBFNN classifier, a novel architecture which integrates feature fusion and modular RBF neural networks classifier approaches into a small set handwritten Chinese character recognition system is presented. Experiments show that the system has achieved impressive performance and the results are informing.