Cross-lingual font style transfer with full-domain convolutional attention

Zhao, Hui-huang, Ji, Tian-le, Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680, Meng, Wei-liang and Wang, Yao-nan 2024. Cross-lingual font style transfer with full-domain convolutional attention. Pattern Recognition 155 , 110709. 10.1016/j.patcog.2024.110709 Item availability restricted.

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Abstract

In this paper, we propose a new cross-lingual font style transfer model, FCAGAN, which enables font style transfer between different languages by observing a small number of samples. Most previous work has been on style transfer of different fonts for single language content, but in our task we can learn the font style of one language and migrate it to another. We investigated the drawbacks of related studies and found that existing cross-lingual approaches cannot perfectly learn styles from other languages and maintain the integrity of their own content. Therefore, we designed a new full-domain convolutional attention (FCA) module in combination with other modules to better learn font styles, and a multi-layer perceptual discriminator to ensure character integrity. Experiments show that using this model provides more satisfying results than the current cross-lingual font style transfer methods. Code can be found at https://github.com/jtlxlf/FCAGAN.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0031-3203
Date of First Compliant Deposit:	29 June 2024
Date of Acceptance:	22 June 2024
Last Modified:	09 Nov 2024 12:15
URI:	https://orca.cardiff.ac.uk/id/eprint/170175

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