title: MME-VideoOCR: Evaluating OCR-Based Capabilities of Multimodal LLMs in
    Video Scenarios
summary: Multimodal Large Language Models (MLLMs) have achieved considerable accuracy
  in Optical Character Recognition (OCR) from static images. However, their
  efficacy in video OCR is significantly diminished due to factors such as motion
  blur, temporal variations, and visual effects inherent in video content. To
  provide clearer guidance for training practical MLLMs, we introduce the
  MME-VideoOCR benchmark, which encompasses a comprehensive range of video OCR
  application scenarios. MME-VideoOCR features 10 task categories comprising 25
  individual tasks and spans 44 diverse scenarios. These tasks extend beyond text
  recognition to incorporate deeper comprehension and reasoning of textual
  content within videos. The benchmark consists of 1,464 videos with varying
  resolutions, aspect ratios, and durations, along with 2,000 meticulously
  curated, manually annotated question-answer pairs. We evaluate 18
  state-of-the-art MLLMs on MME-VideoOCR, revealing that even the best-performing
  model (Gemini-2.5 Pro) achieves an accuracy of only 73.7%. Fine-grained
  analysis indicates that while existing MLLMs demonstrate strong performance on
  tasks where relevant texts are contained within a single or few frames, they
  exhibit limited capability in effectively handling tasks that demand holistic
  video comprehension. These limitations are especially evident in scenarios that
  require spatio-temporal reasoning, cross-frame information integration, or
  resistance to language prior bias. Our findings also highlight the importance
  of high-resolution visual input and sufficient temporal coverage for reliable
  OCR in dynamic video scenarios.
paper_authors._id: 683668aab445f089286ca219
paper_authors.name: Yang Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:19:59.951Z
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paper_authors.fullname: Yang Shi
paper_authors.type: user
paper_authors.user: DogNeverSleep
paper_authors._id: 683668aab445f089286ca21a
paper_authors.name: Huanqian Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T19:14:22.919Z
paper_authors._id: 64ec52887e69909b1ad1ff0b
paper_authors.avatarUrl: /avatars/30bb6fda51093fc71e80d0313c13d15e.svg
paper_authors.fullname: Lucy Wang
paper_authors.type: user
paper_authors.user: Lucywang720
paper_authors._id: 683668aab445f089286ca21b
paper_authors.name: Wulin Xie
paper_authors._id: 683668aab445f089286ca21c
paper_authors.name: Huanyao Zhang
paper_authors._id: 683668aab445f089286ca21d
paper_authors.name: Lijie Zhao
paper_authors._id: 683668aab445f089286ca21e
paper_authors.name: Yi-Fan Zhang
paper_authors._id: 683668aab445f089286ca21f
paper_authors.name: Xinfeng Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:20:46.725Z
paper_authors._id: 65af5f8f3db2280ece7fac79
paper_authors.avatarUrl: /avatars/66d88b2d744c8d00e11d39a55ab86c2e.svg
paper_authors.fullname: Xin-Feng Li
paper_authors.type: user
paper_authors.user: xinfeng1i
paper_authors._id: 683668aab445f089286ca220
paper_authors.name: Chaoyou Fu
paper_authors._id: 683668aab445f089286ca221
paper_authors.name: Zhuoer Wen
paper_authors._id: 683668aab445f089286ca222
paper_authors.name: Wenting Liu
paper_authors._id: 683668aab445f089286ca223
paper_authors.name: Zhuoran Zhang
paper_authors._id: 683668aab445f089286ca224
paper_authors.name: Xinlong Chen
paper_authors._id: 683668aab445f089286ca225
paper_authors.name: Bohan Zeng
paper_authors._id: 683668aab445f089286ca226
paper_authors.name: Sihan Yang
paper_authors._id: 683668aab445f089286ca227
paper_authors.name: Yuanxing Zhang
paper_authors._id: 683668aab445f089286ca228
paper_authors.name: Pengfei Wan
paper_authors._id: 683668aab445f089286ca229
paper_authors.name: Haotian Wang
paper_authors._id: 683668aab445f089286ca22a
paper_authors.name: Wenjing Yang
paper_title: MME-VideoOCR: Evaluating OCR-Based Capabilities of Multimodal LLMs in
    Video Scenarios
paper_summary: Multimodal Large Language Models (MLLMs) have achieved considerable accuracy
  in Optical Character Recognition (OCR) from static images. However, their
  efficacy in video OCR is significantly diminished due to factors such as motion
  blur, temporal variations, and visual effects inherent in video content. To
  provide clearer guidance for training practical MLLMs, we introduce the
  MME-VideoOCR benchmark, which encompasses a comprehensive range of video OCR
  application scenarios. MME-VideoOCR features 10 task categories comprising 25
  individual tasks and spans 44 diverse scenarios. These tasks extend beyond text
  recognition to incorporate deeper comprehension and reasoning of textual
  content within videos. The benchmark consists of 1,464 videos with varying
  resolutions, aspect ratios, and durations, along with 2,000 meticulously
  curated, manually annotated question-answer pairs. We evaluate 18
  state-of-the-art MLLMs on MME-VideoOCR, revealing that even the best-performing
  model (Gemini-2.5 Pro) achieves an accuracy of only 73.7%. Fine-grained
  analysis indicates that while existing MLLMs demonstrate strong performance on
  tasks where relevant texts are contained within a single or few frames, they
  exhibit limited capability in effectively handling tasks that demand holistic
  video comprehension. These limitations are especially evident in scenarios that
  require spatio-temporal reasoning, cross-frame information integration, or
  resistance to language prior bias. Our findings also highlight the importance
  of high-resolution visual input and sufficient temporal coverage for reliable
  OCR in dynamic video scenarios.
paper_ai_summary: MLLMs achieve modest accuracy in video OCR due to motion blur, temporal variations, and visual effects; MME-VideoOCR benchmark reveals limitations in spatio-temporal reasoning and language bias.
paper_ai_keywords: Multimodal Large Language Models MLLMs Video OCR MME-VideoOCR task categories video comprehension spatio-temporal reasoning cross-frame information integration language prior bias high-resolution visual input temporal coverage dynamic video scenarios