  Just like its remarkable achievements in many computer vision tasks, the
convolutional neural networks (CNN) provide an end-to-end solution in
handwritten Chinese character recognition (HCCR) with great success. However,
the process of learning discriminative features for image recognition is
difficult in cases where little data is available. In this paper, we propose a
novel method for learning siamese neural network which employ a special
structure to predict the similarity between handwritten Chinese characters and
template images. The optimization of siamese neural network can be treated as a
simple binary classification problem. When the training process has been
finished, the powerful discriminative features help us to generalize the
predictive power not just to new data, but to entirely new classes that never
appear in the training set. Experiments performed on the ICDAR-2013 offline
HCCR datasets have shown that the proposed method has a very promising
generalization ability to the new classes that never appear in the training
set.
