Deep Deformable Artistic Font Style Transfer

• Published in: Electronics (Basel) Vol. 12; no. 7; p. 1561
• Main Authors: Zhu, Xuanying, Lin, Mugang, Wen, Kunhui, Zhao, Huihuang, Sun, Xianfang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2023
• Subjects: Algorithms; Artificial neural networks; Coders; Computer fonts; Deformation effects; Designers; Fonts; Formability; Generative adversarial networks; Image processing; Learning; Modules; Neural networks; Performance evaluation; China
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12071561

The essence of font style transfer is to move the style features of an image into a font while maintaining the font’s glyph structure. At present, generative adversarial networks based on convolutional neural networks play an important role in font style generation. However, traditional convolutional neural networks that recognize font images suffer from poor adaptability to unknown image changes, weak generalization abilities, and poor texture feature extractions. When the glyph structure is very complex, stylized font images cannot be effectively recognized. In this paper, a deep deformable style transfer network is proposed for artistic font style transfer, which can adjust the degree of font deformation according to the style and realize the multiscale artistic style transfer of text. The new model consists of a sketch module for learning glyph mapping, a glyph module for learning style features, and a transfer module for a fusion of style textures. In the glyph module, the Deform-Resblock encoder is designed to extract glyph features, in which a deformable convolution is introduced and the size of the residual module is changed to achieve a fusion of feature information at different scales, preserve the font structure better, and enhance the controllability of text deformation. Therefore, our network has greater control over text, processes image feature information better, and can produce more exquisite artistic fonts.