A novel CNN structure for fine-grained classification of Chinese calligraphy styles

Abstract

Chinese calligraphy is a valuable cultural heritage belonging to the world. It is liked by many people, and our mission is to endeavor to pursue calligraphy and make contributions to the business with technical means. The automatic recognition of the styles of calligraphy by image processing techniques has important meaning in arts collection and auction, etc. Traditional feature operators have some drawbacks that leave room for modern methods like convolutional neural network (CNN). However, most of the studies focus on the classification of five basic fonts that is somewhat different from styles. In this paper, four kinds of styles belonging to standard font are classified with a novel CNN structure where two squeeze-and-excitation modules that emphasize informative feature maps and suppress useless features are embedded after convolution layers, and a Haar transform layer that fuses the features is imposed before softmax layer. Experiment result shows the significance of the proposed structure over other networks in both font and style classification.