MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation
Authors: name: Bo Li, name: Shaolin Zhu, name: Lijie Wen

Image Translation (IT) holds immense potential across diverse domains,
enabling the translation of textual content within images into various
languages. However, existing datasets often suffer from limitations in scale,
diversity, and quality, hindering the development and evaluation of IT models.
To address this issue, we introduce MIT-10M, a large-scale parallel corpus of
multilingual image translation with over 10M image-text pairs derived from
real-world data, which has undergone extensive data cleaning and multilingual
translation validation. It contains 840K images in three sizes, 28 categories,
tasks with three levels of difficulty and 14 languages image-text pairs, which
is a considerable improvement on existing datasets. We conduct extensive
experiments to evaluate and train models on MIT-10M. The experimental results
clearly indicate that our dataset has higher adaptability when it comes to
evaluating the performance of the models in tackling challenging and complex
image translation tasks in the real world. Moreover, the performance of the
model fine-tuned with MIT-10M has tripled compared to the baseline model,
further confirming its superiority.