Similar handwritten Chinese character recognition based on deep neural networks with big data - Zhao YANG; Da-peng TAO; Shu-ye ZHANG; Lian-wen JIN (Tongxin xuebao)
The recognition rates of the traditional similar handwritten Chinese character recognition (SHCCR) systems are not very high due to the restriction of feature extraction methods.In order to improve the recognition accuracy,a new method based on deep neural networks (DNN) was proposed to learn effective features automatically and conduct recog-nition.The method of how to generate similar handwritten Chinese character sets was introduced.The architecture of the DNN for SHCCR was presented.The performances with respect to different training data scale was compared.The ex-perimental results show that,DNN can learn features automatically and efficiently.The proposed DNN can achieve better performance comparing with support vector machine (SVM) and nearest neighbor classifier (1-NN) based on gradient features.Especially,with the increase of training data the recognition rate of DNN is improved observably,indicating that large training data is crucial for the performance of DNN.