基于二维主成分分析与卷积神经网络的手写体汉字识别

随着计算能力的飞速增长、训练数据的不断积累以及非线性激活函数的不断完善，卷积神经网络（CNN）在手写体汉字识别中表现出较好的识别性能。针对CNN识别手写体汉字识别速度慢的问题，将二维主成分分析（2DPCA）与CNN相结合识别手写体汉字。首先，利用2DPCA提取手写体汉字的投影特征向量；然后，将得到的投影特征向量组成特征矩阵；其次，用组成的特征矩阵作为CNN的输入；最后，用Softmax函数进行分类。与基于AlexNet的CNN模型相比，所提方法的运行时间降低了78%，与基于ACNN与DCNN的模型相比，所提方法的运行时间分别降低了80%与73%。实验结果表明，该方法在不降低识别精度的同时，可以减少识别手写体汉字的运行时间。

Handwritten Chinese character recognition based on two dimensional principal component analysis and convolutional neural network

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.