MME-VideoOCR: Evaluating OCR-Based Capabilities of Multimodal LLMs in Video Scenarios
Journal:
arXiv
Published Date:
May 27, 2025
Abstract
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.
