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.

目前，深度学习在字形建模领域取得了很大进展。然而，现有的字体生成方法存在笔画缺失、结构变形、伪影和模糊等问题。为了解决这些问题，本文提出了结合局部和全局特征学习（FTFNet）的中文字体样式迁移。该模型使用跳跃连​​接和密集连接机制来增强网络层之间的信息传递。同时引入特征注意力层来捕捉局部特征和全局特征之间的依赖关系。从而达到加强局部特征学习和全局特征融合的目的。实验表明，本文方法在字体生成的细节上有较好的表现，