Handwritten Chinese character recognition based on two dimensional principal component analysis and convolutional neural network
ZHENG Yanbin1,2, HAN Mengyun1, FAN Wenxin1
1. College of Computer and Information Engineering, Henan Normal University, Xinxiang Henan 453007, China; 2. Henan Engineering Laboratory of Intellectual Business and Internet of Things Technologies(Henan Normal University), Xinxiang Henan 453007, China
Abstract：With the rapid growth of computing power, the accumulation of training data and the improvement of nonlinear activation function, Convolutional Neural Network (CNN) has a good recognition performance in handwritten Chinese character recognition. To solve the problem of slow speed of CNN for handwritten Chinese character recognition, Two Dimensional Principal Component Analysis (2DPCA) and CNN were combined to identify handwritten Chinese characters. Firstly, 2DPCA was used to extract the projection eigenvectors of handwritten Chinese characters. Secondly, the obtained projection eigenvectors were formed into an eigenmatrix. Thirdly, the formed eigenmatrix was used as the input of CNN. Finally, the softmax function was used for classification. Compared with the model based on AlexNet, the proposed method has the running time reduced by 78%; and compared with the model based on ACNN and DCNN, the proposed method has the running time reduced by 80% and 73%, respectively. Experimental results show that the proposed method can reduce the running time of handwritten Chinese character recognition without reducing the recognition accuracy.
郑延斌, 韩梦云, 樊文鑫. 基于二维主成分分析与卷积神经网络的手写体汉字识别[J]. 计算机应用, 2020, 40(8): 2465-2471.
ZHENG Yanbin, HAN Mengyun, FAN Wenxin. Handwritten Chinese character recognition based on two dimensional principal component analysis and convolutional neural network. Journal of Computer Applications, 2020, 40(8): 2465-2471.
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2020, Vol. 40 
