Recognition of similar handwritten Chinese characters based on CNN and random elastic deformation

In order to recognize similar handwritten Chinese characters effectively, a convolutional neural network (CNN) model is proposed, and the topology of the network model is presented. Then, the sample set is extended by introducing a stochastic elastic deformation to enhance the generalization performance of the model. Experimental results indicate that the recognition accuracy of the proposed CNN model is 1.66% higher than that of the traditional CNN model, especially, for distorted handwritten Chinese characters, the recognition accuracy increases by 12.85%; moreover, as compared with the traditional recognition methods, the proposed CNN model reduces the recognition error rate by 36.47%. It is thus concluded that the proposed method is effective.