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《Journal of South China University of Technology(Natural Science Edition)》 2014-01
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Recognition of Similar Handwritten Chinese Characters Based on CNN and Random Elastic Deformation

Gao Xue;Wang You-wang;School of Electronic and Information Engineering,South China University of Technology;  
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.
【Fund】: 国家自然科学基金资助项目(61271314);; 国家科技支撑计划项目(2013BAH65F01-2013BAH65F04)
【CateGory Index】: TP391.43
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