---
dataset_info:
- config_name: all
  features:
  - name: doc_id
    dtype: string
  - name: image
    dtype: image
  - name: dataset_name
    dtype: string
  splits:
  - name: test
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    num_examples: 206267
  download_size: 39444733971
  dataset_size: 33863176699.362
- config_name: chartqa
  features:
  - name: doc_id
    dtype: string
  - name: image
    dtype: image
  - name: dataset_name
    dtype: string
  splits:
  - name: test
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    num_examples: 20882
  download_size: 955114086
  dataset_size: 1002602606.782
- config_name: default
  features:
  - name: doc_id
    dtype: string
  - name: image
    dtype: image
  - name: dataset_name
    dtype: string
  splits:
  - name: train
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    num_examples: 12230
  download_size: 5075559665
  dataset_size: 1957342799.05
- config_name: dude
  features:
  - name: doc_id
    dtype: string
  - name: image
    dtype: image
  - name: dataset_name
    dtype: string
  splits:
  - name: test
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    num_examples: 27955
  download_size: 9918891043
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- config_name: infovqa
  features:
  - name: doc_id
    dtype: string
  - name: image
    dtype: image
  - name: dataset_name
    dtype: string
  splits:
  - name: test
    num_bytes: 2037249851.38
    num_examples: 5485
  download_size: 1821864668
  dataset_size: 2037249851.38
- config_name: slidevqa
  features:
  - name: doc_id
    dtype: string
  - name: image
    dtype: image
  - name: dataset_name
    dtype: string
  splits:
  - name: test
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    num_examples: 52380
  download_size: 6477479204
  dataset_size: 6852775381.32
configs:
- config_name: all
  data_files:
  - split: test
    path: all/test-*
- config_name: chartqa
  data_files:
  - split: test
    path: chartqa/test-*
- config_name: default
  data_files:
  - split: train
    path: data/train-*
- config_name: dude
  data_files:
  - split: test
    path: dude/test-*
- config_name: infovqa
  data_files:
  - split: test
    path: infovqa/test-*
- config_name: slidevqa
  data_files:
  - split: test
    path: slidevqa/test-*
task_categories:
- visual-question-answering
- question-answering
language:
- en
size_categories:
- 100K<n<1M
extra_gated_prompt: "1.The researcher agrees to the following terms and conditions\
  \ of data sources:\n* DocVQA: https://rrc.cvc.uab.es/?ch=17&com=downloads\n* InfoVQA:\
  \ https://rrc.cvc.uab.es/?ch=17&com=downloads\n\n2.VisualMRC and SlideVQA datasets\
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  By clicking Submit below, I accept the terms of the conditions and license: checkbox
extra_gated_button_content: Submit
---
# Dataset Card for OpenDocVQA
This is a training and evaluation corpus data file for [VDocRAG](https://vdocrag.github.io/), a new RAG framework that can directly understand diverse real-world documents purely from visual features.

## Dataset Description

OpenDocVQA is the first unified collection of open-domain document visual question answering datasets, encompassing diverse document types and formats.

#### Supported Tasks and Leaderboards
Given a large collection of document images and a question, the goal of the OpenDocVQA task is to output an answer by finding the relevant images. We decompose the task into two stages. 

- **Visual document retrieval**: Given a question and a corpus of document images, the model retrieves the relevant images from which to derive the answer.
- **DocumentVQA**: The model takes a question and the retrieved images as input to generate an answer. 

OpenDocVQA covers multiple open-domain DocumentVQA datasets with diverse document types. To reflect real-world scenarios, we evaluate models with both single-pool and all-pool settings. 
In the single-pool setting, retrieval is performed from a specific pool of documents provided by each original dataset. The all-pool setting requires retrieving from the entire candidate pool, which includes documents from a wide range of domains.

#### Languages
English

## Dataset Structure

### Data Instances
```json
{
  'doc_id': 'visualmrc/okfn.org/__about__press__releases__open-revolution-rewriting-rules-information-age01.png',
  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=715x433 at 0x7F07E2565750>,
  'dataset_name': 'visualmrc'
}
```

### Data Fields
An example of a sample looks as follows:
```json
{
  'doc_id': Document ID,
  'image': PIL.Image,
  'dataset_name': Source dataset name
}
```

### Stats about the datasets in The OpenDocVQA

| Dataset              | Documents | # images | # Train&Dev | # Test  |
|----------------------|-----------|----------|-------------|---------|
| DocVQA               | Industry   | 12,767    | 6,382        | -        |
| InfoVQA               | Infographic   | 5,485    | 9,592        | 1,048        |
| VisualMRC               | Webpage   | 10,229    | 6,126        | -        |
| ChartQA               | Chart   | 20,882    | -        | 150       |
| OpenWikitable               | Table   | 1,257    | 4,261        | -        |
| DUDE               | Open   | 27,955    | 2,135        | 496        |
| MPMQA               | Manual   | 10,018    | 3,054        | -        |
| SlideVQA               | Slide   | 52,380    | -        | 760        |
| MHDocVQA  (newly created)            | Open   | 28,550    | 9,470        | -        |



## Additional Information

### License
Each of the publicly available sub-datasets present in the OpenDocVQA is governed by specific licensing conditions. 
Therefore, when making use of them, you must take into consideration each of the licenses governing each dataset.

The images of VisualMRC and SlideVQA datasets in this repo are released under the [NTT License](https://huggingface.co/NTT-hil-insight/OpenDocVQA/edit/main/LICENSE).

### Citation
```bibtex
@inproceedings{tanaka2025vdocrag,
  author    = {Ryota Tanaka and
               Taichi Iki and
               Taku Hasegawa and
               Kyosuke Nishida and
               Kuniko Saito and
               Jun Suzuki},
  title     = {VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents},
  booktitle = {CVPR},
  year      = {2025}
}
```