ClearText-Video: A Large-Scale Text-Centric Video Dataset Bridging Video Restoration and Scene-Text Enhancement
Multimodal Large Language Models (MLLMs) have recently made strong progress in visual--linguistic understanding. However, their performance on text-centric video reasoning remains highly sensitive to input quality. Real-world user-provided videos often contain motion blur, compression artifacts, noise, and low-resolution text, which impair reliable text reading and downstream reasoning. Whether MLLMs can robustly read and reason about real-world scene text under diverse quality conditions remains a fundamental open question. We introduce ClearText-Video (CTVid), a large-scale, scene-text-aware benchmark for studying text-centric video understanding under controlled quality variation. CTVi...
| Authors | Jinlong Li, Jiaming Ding, Dingfu Lu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
Models and datasets referencing this paper
- jinlong17/CTVid-BenchDataset
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