| Challenge: | In-image machine translation is a sub-task of Image-Based Machine Translation that aims to substitute text embedded in images with its translation into another language. |
| Approach: | They propose a simple task that renders parallel text over a plain background and a pipeline that obtains the transcript of the original image, translates it, and generates a new image similar to the original one. |
| Outcome: | The proposed approach outperforms existing models including an end-to-end approach and is competitive with other similar approaches. |
Similar Papers
In-Image Neural Machine Translation with Segmented Pixel Sequence-to-Sequence Model (2023.findings-emnlp)
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| Challenge: | In-Image Machine Translation (IIMT) aims to convert images containing texts from one language to another. |
| Approach: | They propose an end-to-end model instead of the traditional cascade methods which use optical character recognition followed by neural machine translation and text rendering. |
| Outcome: | The proposed model outperforms both cascade methods and current model in translation quality and robustness across various dimensions. |
Multimodal Neural Machine Translation Using Synthetic Images Transformed by Latent Diffusion Model (2023.acl-srw)
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| Challenge: | Existing methods to translate source language sentences using images are not optimal for machine translation. |
| Approach: | They propose a new multimodal neural machine translation model using synthetic images transformed by a latent diffusion model. |
| Outcome: | The proposed model improves translation performance on English-German translation tasks using the Multi30k dataset. |
Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation (2024.findings-acl)
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| Challenge: | In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image with translations in target language. |
| Approach: | They propose an end-to-end IIMT model with four modules that translate images . they propose a two-stage training framework to assist the model in learning alignment across languages . |
| Outcome: | The proposed model outperforms cascaded models with only 70.9% of parameters and is highly accurate. |
PRIM: Towards Practical In-Image Multilingual Machine Translation (2025.emnlp-main)
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| Challenge: | Current research on in-image machine translation focuses on synthetic data with simple background, single font, fixed text position, and bilingual translation. |
| Approach: | They propose an end-to-end model to handle the challenge of practical conditions in PRIM . they annotate a real-world one-line text image with complex background, fonts, diverse text positions . |
| Outcome: | The proposed model improves translation quality and visual effect compared to other models. |
Exploring In-Image Machine Translation with Real-World Background (2025.findings-acl)
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| Challenge: | Existing models for IIMT focus on simplified scenarios, which is far from reality and impractical for applications in the real world. |
| Approach: | They propose a model that separates the background and text-image from the source image and performs translation on the text- image directly. |
| Outcome: | The proposed model improves translation quality and visual effect in complex scenarios . it separates background and text-image from source image and performs translation on the text- image directly . |
AnyTrans: Translate AnyText in the Image with Large Scale Models (2024.findings-emnlp)
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| Challenge: | Recent advances in natural language processing and computer vision have made it possible to translate images with text in one language into equivalent images displaying that text translated into another language. |
| Approach: | They propose an all-encompassing framework for the task–In-Image Machine Translation (IIMT) that incorporates contextual cues from both textual and visual elements during translation. |
| Outcome: | The proposed framework can be constructed using open-source models and requires no training, making it highly accessible and expandable. |
UMTIT: Unifying Recognition, Translation, and Generation for Multimodal Text Image Translation (2024.lrec-main)
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| Challenge: | Current Image machine translation (IMT) relies on a cascaded system that combines Optical Character Recognition (OCR) and a complex process of rendering the translated text back onto the source image. |
| Approach: | They propose a multimodal image-text translation model that generates consistent target images . they use two image-to-text conversion steps to convert images to text to recognize source text . |
| Outcome: | The proposed model outperforms existing methods and surpasses state-of-the-art methods in text recognition tasks. |
Translation-Enhanced Multilingual Text-to-Image Generation (2023.acl-long)
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| Challenge: | Existing models for text-to-image generation are mostly based on the English language due to the lack of annotated image-caption data in other languages. |
| Approach: | They propose to use a multilingual multi-modal encoder to bootstrap mTTI systems that can be translated into other languages. |
| Outcome: | The proposed approach mitigates the language gap and improves on standard mTTI datasets. |
Document Image Machine Translation with Dynamic Multi-pre-trained Models Assembling (2024.naacl-long)
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| Challenge: | Existing TIMT tasks focus on text-line-level images. |
| Approach: | They propose to extend the existing TIMT task and introduce a new framework to translate a source document image to markdown-formatted target translation. |
| Outcome: | The proposed task aims to translate a source document image with long context and complex layout structure to markdown-formatted target translation. |
An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevance (2024.emnlp-main)
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| Challenge: | a new task is to translate images to make them culturally relevant . currently, translation systems focus on translating words and images . |
| Approach: | They propose a task of translating images to make them culturally relevant . they build pipelines comprising state-of-the-art generative models to do the task . |
| Outcome: | The proposed pipelines can translate only 5% of translated images for some countries and no translation is successful for others. |