  Convolution Neural Networks (CNN) have recently achieved state-of-the art
performance on handwritten Chinese character recognition (HCCR). However, most
of CNN models employ the SoftMax activation function and minimize cross entropy
loss, which may cause loss of inter-class information. To cope with this
problem, we propose to combine cross entropy with similarity ranking function
and use it as loss function. The experiments results show that the combination
loss functions produce higher accuracy in HCCR. This report briefly reviews
cross entropy loss function, a typical similarity ranking function: Euclidean
distance, and also propose a new similarity ranking function: Average variance
similarity. Experiments are done to compare the performances of a CNN model
with three different loss functions. In the end, SoftMax cross entropy with
Average variance similarity produce the highest accuracy on handwritten Chinese
characters recognition.
