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Convolution Neural Networks (CNN) have recently achieved state-of-the artperformance on handwritten Chinese character recognition (HCCR). However, mostof CNN models employ the SoftMax activation function and minimize cross entropyloss, which may cause loss of inter-class information. To cope with thisproblem, we propose to combine cross entropy with similarity ranking functionand use it as loss function. The experiments results show that the combinationloss functions produce higher accuracy in HCCR. This report briefly reviewscross entropy loss function, a typical similarity ranking function: Euclideandistance, and also propose a new similarity ranking function: Average variancesimilarity. Experiments are done to compare the performances of a CNN modelwith three different loss functions. In the end, SoftMax cross entropy withAverage variance similarity produce the highest accuracy on handwritten Chinesecharacters recognition.

Handwritten Chinese Character Recognition by Convolutional Neural Network and Similarity Ranking