The OCR-VQA dataset is a valuable resource for research in the field of Visual Question Answering (VQA). Let me provide you with some details about it:

  1. Dataset Overview:

    • The OCR-VQA dataset contains a total of 207,572 images along with their associated question-answer pairs.
    • These images are related to document content and are accompanied by their corresponding OCR transcriptions¹².
  2. Purpose and Significance:

    • Visual Question Answering (VQA) tasks require models to reason jointly over visual information (such as images) and natural language inputs (such as questions).
    • By using this dataset, researchers can develop and evaluate AI models that can effectively understand and answer questions based on visual content and textual context.
  3. Other Related VQA Datasets:

    • Apart from OCR-VQA, there are other VQA datasets available for research and benchmarking:
      • ScreenQA: Focused on questions related to screen content.
      • MP-DocVQA: A dataset for document-based VQA.
      • ChartQA: Specifically designed for answering questions about charts.
      • InfographicVQA: For handling questions related to infographics.

Source: Conversation with Bing, 3/15/2024 (1) OCR-VQA Dataset | Papers With Code. https://paperswithcode.com/dataset/ocr-vqa. (2) GitHub - anisha2102/docvqa: Document Visual Question Answering. https://github.com/anisha2102/docvqa. (3) VQA: Visual Question Answering. https://visualqa.org/. (4) allenai/aokvqa: Official repository for the A-OKVQA dataset - GitHub. https://github.com/allenai/aokvqa.


Paper Code Results Date Stars

Dataset Loaders

No data loaders found. You can submit your data loader here.


Similar Datasets


  • Unknown