  Text-Centric Visual Question Answering (TEC-VQA) in its proper format not
only facilitates human-machine interaction in text-centric visual environments
but also serves as a de facto gold proxy to evaluate AI models in the domain of
text-centric scene understanding. Nonetheless, most existing TEC-VQA benchmarks
have focused on high-resource languages like English and Chinese. Despite
pioneering works to expand multilingual QA pairs in non-text-centric VQA
datasets through translation engines, the translation-based protocol encounters
a substantial "visual-textual misalignment" problem when applied to TEC-VQA.
Specifically, it prioritizes the text in question-answer pairs while
disregarding the visual text present in images. Moreover, it fails to address
complexities related to nuanced meaning, contextual distortion, language bias,
and question-type diversity. In this work, we tackle multilingual TEC-VQA by
introducing MTVQA, the first benchmark featuring high-quality human expert
annotations across 9 diverse languages, consisting of 6,778 question-answer
pairs across 2,116 images. Further, by comprehensively evaluating numerous
state-of-the-art Multimodal Large Language Models (MLLMs), including GPT-4o,
GPT-4V, Claude3, and Gemini, on the MTVQA dataset, it is evident that there is
still a large room for performance improvement, underscoring the value of
MTVQA. Additionally, we supply multilingual training data within the MTVQA
dataset, demonstrating that straightforward fine-tuning with this data can
substantially enhance multilingual TEC-VQA performance. We aspire that MTVQA
will offer the research community fresh insights and stimulate further
exploration in multilingual visual text comprehension. The project homepage is
available at https://bytedance.github.io/MTVQA/.
