Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis
This addresses the problem of synthesizing Chinese calligraphy for applications in digital art and cultural preservation, though it is incremental as it builds on existing image translation methods.
The paper tackles Chinese calligraphy synthesis by treating it as an image-to-image translation problem, proposing a deep neural network model that directly generates calligraphy images from standard font inputs, and constructs a large-scale benchmark dataset, with experimental results showing the model's effectiveness.
In this paper, we investigate the Chinese calligraphy synthesis problem: synthesizing Chinese calligraphy images with specified style from standard font(eg. Hei font) images (Fig. 1(a)). Recent works mostly follow the stroke extraction and assemble pipeline which is complex in the process and limited by the effect of stroke extraction. We treat the calligraphy synthesis problem as an image-to-image translation problem and propose a deep neural network based model which can generate calligraphy images from standard font images directly. Besides, we also construct a large scale benchmark that contains various styles for Chinese calligraphy synthesis. We evaluate our method as well as some baseline methods on the proposed dataset, and the experimental results demonstrate the effectiveness of our proposed model.