Affiliations: [a] School of Computer Science and Engineering, North Minzu University, Yinchuan, Ningxia, China
| [b] Key Laboratory of Images & Graphics Intelligent Processing of National Ethnic Affairs Commission, North Minzu University, Yinchuan, Ningxia, China
Correspondence:
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Corresponding author. Jinlin Ma, School of Computer Science and Engineering, Key Laboratory of Images & Graphics Intelligent Processing of National Ethnic Affairs Commission, North Minzu University, Yinchuan, Ningxia, China. E-mail: majinlin@nmu.edu.cn.
Abstract: Tangut characters were created by the Tangut of the Western Xia (Xi Xia) Dynasty in ancient China and are over 1000 years old. In deep-learning-based recognition studies on Tangut characters, the lack of category-complete datasets has been problematic. Data augmentation cannot augment the character categories of unknown styles, whereas the use of image generation can effectively solve the problem. In this study, we consider the generation of antique book calligraphy styles of Tangut characters as a problem of learning to map from existing printed styles to personalized antique book calligraphy styles. We present M-ResNet, a multi-scale feature extraction residual unit, and Tangut-CycleGAN, a model for generation Tangut characters that combine M-ResNet and a cycle-consistent adversarial network (CycleGAN). This method uses unpaired data to generate Tangut character images in the calligraphy style of ancient books. To enhance the response of the model to significant channels, a squeezing-and-excitation (SE) module is introduced based on Tangut-CycleGAN to design the Tangut-CycleGAN+SE method for generating images of Tangut characters. This method is not only suitable for Tangut character image generation, but also can effectively generate calligraphy with aesthetic value. In addition, we propose an overall quality discrepancy evaluation metric, FA (Fréchet inception distance + Accuracy), to evaluate the quality of character image generation, which combines style discrepancy and content accuracy metrics.
