Electronics, Communication & Automation Technology

Recognition of Similar Handwritten Chinese Characters Based on CNN and Random Elastic Deformation

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  • School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
高学(1967-),男,博士,副教授,主要从事图像处理、模式识别与智能信息处理、手写汉字识别研究.

Received date: 2013-06-15

  Revised date: 2013-09-29

  Online published: 2013-12-01

Supported by

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)

Abstract

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 extendedby introducing a stochastic elastic deformation to enhance the generalization performance of the model.Experimen-tal results indicate that the recognition accuracy of the proposed CNN model is 1.66% higher than that of the tradi-tional CNN model,especially,for distorted handwritten Chinese characters,the recognition accuracy increases by12.85%; moreover,as compared with the traditional recognition methods,the proposed CNN model reduces therecognition error rate by 36.47%.It is thus concluded that the proposed method is effective.

Cite this article

Gao Xue Wang You- wang . Recognition of Similar Handwritten Chinese Characters Based on CNN and Random Elastic Deformation[J]. Journal of South China University of Technology(Natural Science), 2014 , 42(1) : 72 -76,83 . DOI: 10.3969/j.issn.1000-565X.2014.01.013

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