Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Deep deformable artistic font style transfer

Zhu, Xuanying, Lin, Mugang, Wen, Kunhui, Zhao, Huihuang and Sun, Xianfang ORCID: 2023. Deep deformable artistic font style transfer. Electronics 12 (7) , 1561. 10.3390/electronics12071561

[thumbnail of electronics-12-01561.pdf] PDF - Published Version
Available under License Creative Commons Attribution.

Download (3MB)


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.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Additional Information: License information from Publisher: LICENSE 1: URL:, Type: open-access
Publisher: MDPI
Date of First Compliant Deposit: 6 April 2023
Date of Acceptance: 24 March 2023
Last Modified: 02 May 2023 20:50

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics