Attention-based deformable convolutional network for Chinese various dynasties character recognition

In this paper, we propose a new deformable convolutional network with attention mechanism to deal with the Chinese character of various dynasties. These ancient Chinese characters are hieroglyph with special spatial structure and gradually evolving into modern Chinese characters. However, because of the intrinsic limitations of traditional convolutional networks in the model, the geometric structure of the convolutional module is fixed. Therefore, we build a new architecture called attention-based deformable networks for this task. Besides, we construct a new dataset called the Chinese characters from Various Dynasties Dataset (CCDD), which includes the evolution of Chinese characters between major dynasties. Deformable convolution based on attention mechanism can obtain those more important offsets, resulting in better performance than the original deformable convolution. In the experimental stage, extensive experiments validate the performance of our approach. Firstly, the effectiveness of this network was verified on two text datasets, Mnist(99.38%) and notMnist(98.22%) both comparing with several modules. Finally, the best performance was achieved on the CCDD dataset(91.50%) compared to other classical deep learning networks. This is the first paper to apply deep learning methods to recognize and classify the characters of major dynasties in China. And archaeological workers need to be able to recognize Chinese characters from various periods in China, and our algorithm can assist them in completing this task to a certain extent. It can also help cultural relic enthusiasts roughly determine the age of cultural relics.