PRIM：迈向实用的图像内多语言机器翻译
PRIM: Towards Practical In-Image Multilingual Machine Translation
AI总结：
针对当前图像内机器翻译研究依赖合成数据、与真实场景脱节的问题，研究者标注了支持多语言的真实场景PRIM数据集，提出分别处理文本与背景的VisTrans模型，在翻译质量和视觉效果上均优于现有模型。
摘要：
图像内机器翻译（IIMT）旨在将包含文本的图像从一种语言翻译为另一种语言。当前端到端IIMT研究主要在合成数据上开展，这类数据背景简单、字体单一、文本位置固定且仅支持双语翻译，无法充分反映真实世界情况，导致研究与实际场景存在显著差距。为推动真实场景下的IIMT研究，我们探索了实用图像内多语言机器翻译（IIMMT）。为解决公开数据缺失的问题，我们标注了PRIM数据集，该数据集包含真实拍摄的单行文本图像，背景复杂、字体多样、文本位置各异，支持多语言翻译方向。我们提出端到端模型VisTrans以应对PRIM中的实际场景挑战，该模型分别处理图像中的视觉文本与背景信息，在保证多语言翻译能力的同时提升了视觉质量。实验结果表明，与其他模型相比，VisTrans实现了更优的翻译质量和视觉效果。代码与数据集可在以下地址获取：https://github.com/BITHLP/PRIM。
英文摘要：
In-Image Machine Translation (IIMT) aims to translate images containing texts from one language to another. Current research of end-to-end IIMT mainly conducts on synthetic data, with simple background, single font, fixed text position, and bilingual translation, which can not fully reflect real world, causing a significant gap between the research and practical conditions. To facilitate research of IIMT in real-world scenarios, we explore Practical In-Image Multilingual Machine Translation (IIMMT). In order to convince the lack of publicly available data, we annotate the PRIM dataset, which contains real-world captured one-line text images with complex background, various fonts, diverse text positions, and supports multilingual translation directions. We propose an end-to-end model VisTrans to handle the challenge of practical conditions in PRIM, which processes visual text and background information in the image separately, ensuring the capability of multilingual translation while improving the visual quality. Experimental results indicate the VisTrans achieves a better translation quality and visual effect compared to other models. The code and dataset are available at: https://github.com/BITHLP/PRIM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
