AnyTrans: Translate AnyText in the Image with Large Scale Models
Abstract: This paper introduces AnyTrans, an all-encompassing framework for the task--Translate AnyText in the Image (TATI), which includes multilingual text translation and text fusion within images. Our framework leverages the strengths of large-scale models, such as Large Language Models (LLMs) and text-guided diffusion models, to incorporate contextual cues from both textual and visual elements during translation. The few-shot learning capability of LLMs allows for the translation of fragmented texts by considering the overall context.
Meanwhile, the advanced inpainting and editing abilities of diffusion models make it possible to fuse translated text seamlessly into the original image while preserving its style and realism. Additionally, our framework can be constructed entirely using open-source models and requires no training, making it highly accessible and easily expandable. To encourage advancement in the \taskname task, we have meticulously compiled a test dataset called MTIT6, which consists of multilingual text image translation data from six language pairs.
Paper Type: Long
Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond
Research Area Keywords: multimodality; cross-modal application; cross-modal content generation
Contribution Types: Model analysis & interpretability, NLP engineering experiment
Languages Studied: English, Chinese, Korean, Japanese
Submission Number: 2433
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