DSE-Net: Artistic Font Image Synthesis via Disentangled Style Encoding
Xiang Li, Lei Wu, Xu Chen, Lei Meng, Xiangxu Meng
Abstract
Recently, the artistic font generation has made significant progress. However, existing methods typically treat the style of artistic font as a whole. Their performance is usually limited to the artistic fonts with complex style elements in glyph and text effect. To solve these problems, this paper presents a disentangled style encoding network, termed DSE-Net, to synthesize artistic fonts. In order to obtain the disentangled text effect features, we introduce a perspective transformation network. We propose a cross-layer fusion mechanism to improve the artistic fonts' structure and texture according to their different representations in CNN. Notably, encoding different style elements for artistic font generation is a new task, so there is no publicly-accessible dataset. Therefore, a new dataset, termed SSAF, has been constructed. Extensive experiments demonstrate that our model significantly outperforms the state-of-the-art methods, with more fine-grained text effect and accurate stroke details.