ClearText-Video: A Large-Scale Text-Centric Video Dataset Bridging Video Restoration and Scene-Text Enhancement

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.