PRIM: Towards Practical In-Image Multilingual Machine Translation (2025.emnlp-main)
Copied to clipboard
| 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. |
Similar Papers
Exploring In-Image Machine Translation with Real-World Background (2025.findings-acl)
Copied to clipboard
| 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 . |
Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation (2024.findings-acl)
Copied to clipboard
| 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. |
In-Image Neural Machine Translation with Segmented Pixel Sequence-to-Sequence Model (2023.findings-emnlp)
Copied to clipboard
| 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. |
In-Image Machine Translation. A Preliminary Modular Approach (2026.eacl-srw)
Copied to clipboard
| 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. |
AnyTrans: Translate AnyText in the Image with Large Scale Models (2024.findings-emnlp)
Copied to clipboard
| 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. |
MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation (2025.coling-main)
Copied to clipboard
| Challenge: | Existing datasets suffer from limitations in scale, diversity, and quality, hindering the development and evaluation of IT models. |
| Approach: | They propose a large-scale parallel corpus of multilingual image translation with over 10M image-text pairs derived from real-world data. |
| Outcome: | The proposed model performs better in tackling challenging and complex image translation tasks in the real world. |
Multimodal Neural Machine Translation Using Synthetic Images Transformed by Latent Diffusion Model (2023.acl-srw)
Copied to clipboard
| 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. |
Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations (2022.naacl-main)
Copied to clipboard
| Challenge: | Multilingual Neural Machine Translation (MNMT) systems are often limited to many-to-one directions and suffer from poor performance in one-to one directions. |
| Approach: | They propose to build multilingual machine translation systems that serve arbitrary X-Y directions while leveraging multilinguality with a two-stage training strategy of pretraining and finetuning. |
| Outcome: | The proposed system outperforms baseline bilingual models and pivot translation models in most directions without the need for architecture change or extra data collection. |
Multimodal Neural Machine Translation: A Survey of the State of the Art (2025.emnlp-main)
Copied to clipboard
| Challenge: | Multimodal neural machine translation (MNMT) is a task that aims to translate text into the target language using neural networks. |
| Approach: | They propose to integrate other modalities with textual data to enhance translation performance. |
| Outcome: | The proposed task aims to integrate visual modality with textual data to improve translation quality. |
Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies on large language models focus on literal-level translation quality, such as adequacy and fluency. |
| Approach: | They propose a Culture-Aware Novel-Driven Parallel Dataset for Machine Translation and a multi-dimensional evaluation framework for assessing cultural translation quality. |
| Outcome: | The proposed model improves evaluation reliability in LLM-as-a-judge scenarios under culture-aware constraints. |