Chinese font migration combining local and global features learning

At present, deep learning has made great progress in the field of glyph modeling. However, existing methods of font generation have some problems, such as missing stroke, structural deformation, artifact and blur. To solve these problems, this paper proposes Chinese font style migration combining local and global feature learning (FTFNet). The model uses skipping connection and dense connection mechanism to enhance the information transfer between the network layers. At the same time, feature attention layer is introduced to capture the dependency relationship between local and global features. So as to achieve the purpose of strengthening local feature learning and global feature fusion. Experiments show that the method in this paper has better performance in the details of font generation, which simplifies the font generation process and improves the quality of generated fonts.