VisualMRC is a visual machine reading comprehension dataset that proposes a task: given a question and a document image, a model produces an abstractive answer.

You can find more details, analyses, and baseline results in our paper. You can cite it as follows:

@inproceedings{VisualMRC2021,
  author    = {Ryota Tanaka and
               Kyosuke Nishida and
               Sen Yoshida},
  title     = {VisualMRC: Machine Reading Comprehension on Document Images},
  booktitle = {AAAI},
  year      = {2021}
}

• [2025.03.27] Our VisualMRC dataset is available on 🤗HuggingFace.

• 🤗VisualMRC

• 10,197 images
• 30,562 QA pairs
• 10.53 average question tokens (tokenizing with NLTK tokenizer)
• 9.53 average answer tokens (tokenizing wit NLTK tokenizer)
• 151.46 average OCR tokens (tokenizing with NLTK tokenizer)