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

  • Jiulong ZhangEmail author
  • Mingtao Guo
  • Jianping Fan
Original Paper
  • 56 Downloads

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.

Keywords

Deep learning Convolutional neural network (CNN) Chinese calligraphy Styles classification 

Notes

Acknowledgement

This work is support by National Key Research and Development Plan (No. 2017YFB1402103), Xi’an Science and Technology Bureau Project (201805037YD15CG21(6)). The authors are grateful for the anonymous reviewers for their significant comments.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Computer Science and EngineeringXi’an University of TechnologyXi’anChina
  2. 2.Shaanxi Key Laboratory for Network Computing and Security TechnologyXi’anChina
  3. 3.University of North Carolina at CharlotteCharlotteUSA

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