Off-line handwritten Chinese character recognition based on fusion features and LS-SVM

The proposed off-line handwritten Chinese character recognition system was composed of a feature extraction module and a recognition module. In the feature extraction module, the orthogonal Zernike moments and the elastic mesh technique were combined to get fusion features, which present the global and local features of handwritten Chinese characters and have great discriminative capability. As for the classification module, one approach that is very similar to the neural network classification strategy was used with the Least Square Vector Machine (LSSVM), which not only has the excellent performance of generalization and recognition accuracy, but also can solve the multi-classification issue effectively. Experimental results indicated that the proposed method could get good recognition results.