LAUSR

Chinese calligraphy has a strong artistry and appreciation. Through deep learning methods such as image translation methods, Chinese characters lacking in a set of calligraphy fonts can be quickly generated. Because of the clear structure of Chinese characters, the quality of the generated images is required to be high. However, it is difficult for a single generator to generate Chinese characters with clear texture, so we propose a multi-scale generative adversarial network (GAN), which contains two sub-GANs. The small-scale GAN generates low-resolution Chinese character outline, while the large-scale GAN supplements the details of the characters based on it. In addition to quantitatively evaluating the generated characters, we pre-train a vgg-19 network to extract stylistic characteristics of Chinese characters and compare the feature discrepancy between generated and real Chinese characters. From the experiment, using our method, the definition of the generated Chinese characters has indeed improved.