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New ImgMT dataset evaluates text-in-image translation across 129 languages

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ImgMT, a new dataset and benchmark designed to evaluate text-in-image translation capabilities. This dataset, derived from localized SVG illustrations on Wikimedia Commons, includes over 35,000 image pairs across 129 languages and supports evaluation of both text extraction and translation quality. Initial benchmarking of multimodal large language models revealed that the source language and writing system significantly impact translation performance. AI

IMPACT This dataset could advance research in multimodal AI by providing a standardized way to evaluate text-in-image translation.

RANK_REASON The cluster contains a research paper detailing a new dataset and benchmark for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ImgMT dataset evaluates text-in-image translation across 129 languages

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The cluster contains a research paper detailing a new dataset and benchmark for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · \v{S}\'arka Uramov\'a, Petar Kirilov Petrov, Josef Jon, Ond\v{r}ej Bojar, Michal Nov\'ak ·

    ImgMT: Wikipedia-based Dataset For Evaluating Text-in-Image Translation

    arXiv:2610.35416v1 Announce Type: new Abstract: Text-in-image translation aims to translate all textual content embedded in an image. In Text Image Translation (TIT), the output is translated text; In-Image Machine Translation (IIMT) additionally renders the translation back into…

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