mlsanityData to 14 Oct 2026

Reading Between the Lanes: Text VideoQA on the Road

arXiv 2307.03948 ↗First posted 8 Jul 2023cs.CVcited by 23

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Abstract (arXiv)

Text and signs around roads provide crucial information for drivers, vital for safe navigation and situational awareness. Scene text recognition in motion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such information to assist the driver should not only extract and incorporate visual and textual cues from the video stream but also reason over time. To address this issue, we introduce RoadTextVQA, a new dataset for the task of video question answering (VideoQA) in the context of driver assistance. RoadTextVQA consists of 3,222 driving videos collected from multiple countries, annotated with 10,500 questions, all based on text or road signs present in the driving videos. We assess the performance of state-of-the-art video question answering models on our RoadTextVQA dataset, highlighting the significant potential for improvement in this domain and the usefulness of the dataset in advancing research on in-vehicle support systems and text-aware multimodal question answering. The dataset is available at http://cvit.iiit.ac.in/research/projects/cvit-projects/roadtextvqa

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 37 of its 47 references that are in the map. First posted on arXiv 8 Jul 2023.

Builds on

37 of 47 references.

References · 37

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25 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 16; 2026: 4.

23 papers.

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  1. 2025EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answeringcited by 44
  2. 2025MME-VideoOCR: Evaluating OCR-Based Capabilities of Multimodal LLMs in Video Scenarioscited by 18
  3. 2025VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference Optimization for Large Video Modelscited by 17
  4. 2025Dr.V: A Hierarchical Perception-Temporal-Cognition Framework to Diagnose Video Hallucination by Fine-grained Spatial-Temporal Groundingcited by 13
  5. 2025VidText: Towards Comprehensive Evaluation for Video Text Understandingcited by 11
  6. 2025An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubescited by 7
  7. 2026IPIBench: Evaluating Interactive Proactive Intelligence of MLLMs under Continuous Streamscited by 7
  8. 2025Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Ruminationcited by 6
  9. 2024Scene-Text Grounding for Text-Based Video Question Answeringcited by 5
  10. 2025GoMatching++: Parameter- and Data-Efficient Arbitrary-Shaped Video Text Spotting and Benchmarkingcited by 5
  11. 2025Building Egocentric Procedural AI Assistant: Methods, Benchmarks, and Challengescited by 5
  12. 2024Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Surveycited by 4
11 more
  1. 2023Understanding Video Scenes through Text: Insights from Text-based Video Question Answeringcited by 2
  2. 2025Evaluating Small Vision-Language Models on Distance-Dependent Traffic Perceptioncited by 2
  3. 2025SFA: Scan, Focus, and Amplify toward Guidance-aware Answering for Video TextVQAcited by 2
  4. 2025Read or Ignore? A Unified Benchmark for Typographic-Attack Robustness and Text Recognition in Vision-Language Modelscited by 2
  5. 2025InterAct-Video: Reasoning-Rich Video QA for Urban Trafficcited by 1
  6. 2025MITS: A Large-Scale Multimodal Benchmark Dataset for Intelligent Traffic Surveillancecited by 1
  7. 2026ViTexQA: A Multi-Frame Temporal Perception Dataset for Video Text Question Answeringcited by 1
  8. 2025VideoChain: A Transformer-Based Framework for Multi-hop Video Question Generationcited by 0
  9. 2025Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA)cited by 0
  10. 2026VTAgent: Agentic Keyframe Anchoring for Evidence-Aware Video TextVQAcited by 0
  11. 2026ClearText-Video: A Large-Scale Text-Centric Video Dataset Bridging Video Restoration and Scene-Text Enhancementcited by 0