Deep Matching Network for Handwritten Chinese Character Recognition

Highlights：

• In this paper, we propose a matching network which builds a connection be- tween template characters and handwritten characters inspired by the human learning process of writing Chinese characters. The matching network replace the parameters in the softmax regression layer with the features extracted from the template character images. After the training process has been finished, the powerful discriminative features help us to generalize the predictive power not just to new data, but to entire new classes that never appear in the training set before. Experiments performed on the ICDAR-2013 offline HCCR datasets have shown that the proposed method achieves a comparable performance to current CNN-based classifiers. Besides, the matching network has a very promising gen- eralization ability to the new classes that never appear in the existing training set.