ClearText-Video: A Large-Scale Text-Centric Video Dataset Bridging Video Restoration and Scene-Text Enhancement
Authors: Jinlong Li, Jiaming Ding, Dingfu Lu, Malcolm Hsiu, Chuang Ke, Kangning Yang, Bochen Guan, Lan Fu, Jie Cai, Huiming Sun, Zibo Meng
Abstract
Multimodal Large Language Models (MLLMs) have recentlydemonstrated strong progress in visual–linguistic understanding, yet theirperformance on text-centric video reasoning remains highly sensitive toinput quality. Real-world user-provided videos frequently contain motionblur, compression artifacts, noise, and low-resolution text, substantiallyimpairing reliable text reading and downstream reasoning. WhetherMLLMs can robustly read and reason over in-the-wild text under di-verse quality conditions remains an unanswered fundamental question.We introduce ClearText-Video (CTVid), a large-scale, scene-text-awarebenchmark for studying text-centric video understanding under controlledquality variation. CTVid contains 4,639 real-world text-rich egocentricvideos, 550K+ frames, 1.6M human-verified scene-text annotations, and220K+ spatial/temporal question–answer pairs in Chinese and English.For each high-quality video, CTVid provides content-matched Degraded-Quality and Restored-Quality variants, supporting two task families:Text-Centric Video Restoration and Multi-Video Quality VideoQA. Weevaluate 18 representative restoration methods and 16 state-of-the-artMLLMs on CTVid. The results show that visual enhancement does notguarantee textual fidelity or downstream reasoning gains: blur is moredamaging than low resolution, restored videos can alter the textual evi-dence used by MLLMs, and OCR-only pipelines remain far below directmultimodal reasoning. CTVid exposes the gap between video restorationand text-grounded understanding, providing a rigorous foundation forrestoration-aware, quality-robust text-centric video systems. Benchmark:https://github.com/jinlong17/CTVid-Bench.