---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: question_id
    dtype: int64
  - name: question
    dtype: string
  - name: answers
    sequence: string
  - name: data_split
    dtype: string
  - name: ocr_results
    struct:
    - name: page
      dtype: int64
    - name: clockwise_orientation
      dtype: float64
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: unit
      dtype: string
    - name: lines
      list:
      - name: bounding_box
        sequence: int64
      - name: text
        dtype: string
      - name: words
        list:
        - name: bounding_box
          sequence: int64
        - name: text
          dtype: string
        - name: confidence
          dtype: string
  - name: other_metadata
    struct:
    - name: ucsf_document_id
      dtype: string
    - name: ucsf_document_page_no
      dtype: string
    - name: doc_id
      dtype: int64
    - name: image
      dtype: string
  splits:
  - name: train
    num_examples: 39463
  - name: validation
    num_examples: 5349
  - name: test
    num_examples: 5188
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: mit
task_categories:
- question-answering
language:
- en
size_categories:
- 10K<n<100K
---

# Dataset Card for DocVQA Dataset

## Dataset Description

- **Point of Contact from curators:** [Minesh Mathew](mailto:minesh.mathew@gmail.com), [Dimosthenis Karatzas](dimos@cvc.uab.es), [C. V. Jawahar](jawahar@iiit.ac.in)
- **Point of Contact Hugging Face:** [Pablo Montalvo](mailto:pablo@huggingface.co)

### Dataset Summary

DocVQA dataset is a document dataset introduced in Mathew et al. (2021) consisting of 50,000 questions defined on 12,000+ document images. 

### Usage

This dataset can be used with current releases of Hugging Face `datasets` library. 
Here is an example using a custom collator to bundle batches in a trainable way on the `train` split

```python

from datasets import load_dataset

docvqa_dataset = load_dataset("pixparse/docvqa-single-page-questions", split="train"
)
next(iter(dataset["train"])).keys()
>>> dict_keys(['image', 'question_id', 'question', 'answers', 'data_split', 'ocr_results', 'other_metadata'])
```
`image` will be a byte string containing the image contents. `answers` is a list of possible answers, aligned with the expected inputs to the [ANLS metric](https://arxiv.org/abs/1905.13648).

The loader can then be iterated on normally and yields questions. Many questions rely on the same image, so there is some amount of data duplication.

### Data Splits


#### Train
* 10194 images, 39463 questions and answers.

### Validation

* 1286 images, 5349 questions and answers.

### Test

* 1,287 images,  5,188 questions.
  
## Additional Information

### Dataset Curators

Pablo Montalvo, Ross Wightman

### Licensing Information

MIT

### Citation Information

Mathew, Minesh, Dimosthenis Karatzas, and C. V. Jawahar. "Docvqa: A dataset for vqa on document images." Proceedings of the IEEE/CVF winter conference on applications of computer vision. 20