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

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

[thumbnail of fontStyleTransfer-postprint.pdf]
Preview
PDF - Accepted Post-Print Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (10MB) | Preview

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: 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: 25 Jun 2025 01:45
URI: https://orca.cardiff.ac.uk/id/eprint/170175

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics