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    <abstract>In-Image Machine Translation (IIMT) aims to translate texts within images from one language to another. Previous research on IIMT was primarily conducted on simplified scenarios such as images of one-line text with black font in white backgrounds, which is far from reality and impractical for applications in the real world. To make IIMT research practically valuable, it is essential to consider a complex scenario where the text backgrounds are derived from real-world images. To facilitate research of complex scenarios IIMT, we design an IIMT dataset that includes subtitle text with a real-world background. However, previous IIMT models perform inadequately in complex scenarios. To address the issue, we propose the DebackX model, which separates the background and text-image from the source image, performs translation on the text-image directly, and fuses the translated text-image with the background to generate the target image. Experimental results show that our model achieves improvements in both translation quality and visual effect.</abstract>
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%0 Conference Proceedings
%T Exploring In-Image Machine Translation with Real-World Background
%A Tian, Yanzhi
%A Liu, Zeming
%A Liu, Zhengyang
%A Guo, Yuhang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F tian-etal-2025-exploring
%X In-Image Machine Translation (IIMT) aims to translate texts within images from one language to another. Previous research on IIMT was primarily conducted on simplified scenarios such as images of one-line text with black font in white backgrounds, which is far from reality and impractical for applications in the real world. To make IIMT research practically valuable, it is essential to consider a complex scenario where the text backgrounds are derived from real-world images. To facilitate research of complex scenarios IIMT, we design an IIMT dataset that includes subtitle text with a real-world background. However, previous IIMT models perform inadequately in complex scenarios. To address the issue, we propose the DebackX model, which separates the background and text-image from the source image, performs translation on the text-image directly, and fuses the translated text-image with the background to generate the target image. Experimental results show that our model achieves improvements in both translation quality and visual effect.
%R 10.18653/v1/2025.findings-acl.6
%U https://aclanthology.org/2025.findings-acl.6/
%U https://doi.org/10.18653/v1/2025.findings-acl.6
%P 124-137

Markdown (Informal)

[Exploring In-Image Machine Translation with Real-World Background](https://aclanthology.org/2025.findings-acl.6/) (Tian et al., Findings 2025)

• Exploring In-Image Machine Translation with Real-World Background (Tian et al., Findings 2025)

ACL

• Yanzhi Tian, Zeming Liu, Zhengyang Liu, and Yuhang Guo. 2025. Exploring In-Image Machine Translation with Real-World Background. In Findings of the Association for Computational Linguistics: ACL 2025, pages 124–137, Vienna, Austria. Association for Computational Linguistics.