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    <abstract>With the rise of multi-modal large language models, accurately extracting and understanding textual information from video content—referred to as video-based optical character recognition (Video OCR)—has become a crucial capability. This paper introduces a novel benchmark designed to evaluate the video OCR performance of multi-modal models in videos. Comprising 1,028 videos and 2,961 question-answer pairs, this benchmark proposes several key challenges through 6 distinct sub-tasks: (1) Recognition of text content itself and its basic visual attributes, (2) Semantic and Spatial Comprehension of OCR objects in videos (3) Dynamic Motion detection and Temporal Localization. We developed this benchmark using a semi-automated approach that integrates the OCR ability of image LLMs with manual refinement, balancing efficiency, cost, and data quality. Our resource aims to help advance research in video LLMs and underscores the need for improving OCR ability for video LLMs. The benchmark will be released on https://github.com/YuHuiGao/FG-Bench.git.</abstract>
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%0 Conference Proceedings
%T Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study
%A Fei, Yulin
%A Gao, Yuhui
%A Xian, Xingyuan
%A Zhang, Xiaojin
%A Wu, Tao
%A Chen, Wei
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F fei-etal-2025-current
%X With the rise of multi-modal large language models, accurately extracting and understanding textual information from video content—referred to as video-based optical character recognition (Video OCR)—has become a crucial capability. This paper introduces a novel benchmark designed to evaluate the video OCR performance of multi-modal models in videos. Comprising 1,028 videos and 2,961 question-answer pairs, this benchmark proposes several key challenges through 6 distinct sub-tasks: (1) Recognition of text content itself and its basic visual attributes, (2) Semantic and Spatial Comprehension of OCR objects in videos (3) Dynamic Motion detection and Temporal Localization. We developed this benchmark using a semi-automated approach that integrates the OCR ability of image LLMs with manual refinement, balancing efficiency, cost, and data quality. Our resource aims to help advance research in video LLMs and underscores the need for improving OCR ability for video LLMs. The benchmark will be released on https://github.com/YuHuiGao/FG-Bench.git.
%U https://aclanthology.org/2025.coling-main.659/
%P 9860-9876

Markdown (Informal)

[Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study](https://aclanthology.org/2025.coling-main.659/) (Fei et al., COLING 2025)

• Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study (Fei et al., COLING 2025)

ACL

• Yulin Fei, Yuhui Gao, Xingyuan Xian, Xiaojin Zhang, Tao Wu, and Wei Chen. 2025. Do Current Video LLMs Have Strong OCR Abilities? A Preliminary Study. In Proceedings of the 31st International Conference on Computational Linguistics, pages 9860–9876, Abu Dhabi, UAE. Association for Computational Linguistics.