Deep Matching Network for Handwritten Chinese Character Recognition

Abstract 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 matching network which builds a connection between template characters and handwritten characters inspired by the human learning process of writing Chinese characters. The matching network replaces 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 Chinese characters 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 generalization ability to new Chinese characters that never appear in the existing training set.