metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
pretty_name: TextVQA
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - visual-question-answering
task_ids:
  - visual-question-answering
dataset_info:
  - config_name: train
    features:
      - name: image_id
        dtype: string
      - name: question_id
        dtype: int32
      - name: question
        dtype: string
      - name: question_tokens
        sequence: string
      - name: image
        dtype: image
      - name: image_width
        dtype: int32
      - name: image_height
        dtype: int32
      - name: flickr_original_url
        dtype: string
      - name: flickr_300k_url
        dtype: string
      - name: answers
        sequence: string
      - name: image_classes
        sequence: string
      - name: set_name
        dtype: string
    splits:
      - name: train
        num_bytes: 21381310
        num_examples: 34602
      - name: validation
        num_bytes: 3077854
        num_examples: 5000
      - name: test
        num_bytes: 3025046
        num_examples: 5734
    download_size: 8070116310
    dataset_size: 27484210
  - config_name: val
    features:
      - name: image_id
        dtype: string
      - name: question_id
        dtype: int32
      - name: question
        dtype: string
      - name: question_tokens
        sequence: string
      - name: image
        dtype: image
      - name: image_width
        dtype: int32
      - name: image_height
        dtype: int32
      - name: flickr_original_url
        dtype: string
      - name: flickr_300k_url
        dtype: string
      - name: answers
        sequence: string
      - name: image_classes
        sequence: string
      - name: set_name
        dtype: string
    splits:
      - name: train
        num_bytes: 21381310
        num_examples: 34602
      - name: validation
        num_bytes: 3077854
        num_examples: 5000
      - name: test
        num_bytes: 3025046
        num_examples: 5734
    download_size: 8070116310
    dataset_size: 27484210
  - config_name: test
    features:
      - name: image_id
        dtype: string
      - name: question_id
        dtype: int32
      - name: question
        dtype: string
      - name: question_tokens
        sequence: string
      - name: image
        dtype: image
      - name: image_width
        dtype: int32
      - name: image_height
        dtype: int32
      - name: flickr_original_url
        dtype: string
      - name: flickr_300k_url
        dtype: string
      - name: answers
        sequence: string
      - name: image_classes
        sequence: string
      - name: set_name
        dtype: string
    splits:
      - name: train
        num_bytes: 21381310
        num_examples: 34602
      - name: validation
        num_bytes: 3077854
        num_examples: 5000
      - name: test
        num_bytes: 3025046
        num_examples: 5734
    download_size: 8070116310
    dataset_size: 27484210
  - config_name: textvqa
    features:
      - name: image_id
        dtype: string
      - name: question_id
        dtype: int32
      - name: question
        dtype: string
      - name: question_tokens
        sequence: string
      - name: image
        dtype: image
      - name: image_width
        dtype: int32
      - name: image_height
        dtype: int32
      - name: flickr_original_url
        dtype: string
      - name: flickr_300k_url
        dtype: string
      - name: answers
        sequence: string
      - name: image_classes
        sequence: string
      - name: set_name
        dtype: string
    splits:
      - name: train
        num_bytes: 22073350
        num_examples: 34602
      - name: validation
        num_bytes: 3177854
        num_examples: 5000
      - name: test
        num_bytes: 3139726
        num_examples: 5734
    download_size: 8070116310
    dataset_size: 28390930

Dataset Card for TextVQA

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: https://textvqa.org
Repository: https://github.com/facebookresearch/mmf
Paper: https://arxiv.org/abs/1904.08920
Leaderboard: https://eval.ai/web/challenges/challenge-page/874/overview
Point of Contact: mailto:amanpreet@nyu.edu

Dataset Summary

TextVQA requires models to read and reason about text in images to answer questions about them. Specifically, models need to incorporate a new modality of text present in the images and reason over it to answer TextVQA questions. TextVQA dataset contains 45,336 questions over 28,408 images from the OpenImages dataset. The dataset uses VQA accuracy metric for evaluation.

Supported Tasks and Leaderboards

visual-question-answering: The dataset can be used for Visual Question Answering tasks where given an image, you have to answer a question based on the image. For the TextVQA dataset specifically, the questions require reading and reasoning about the scene text in the given image.

Languages

The questions in the dataset are in English.

Dataset Structure

Data Instances

A typical sample mainly contains the question in question field, an image object in image field, OpenImage image id in image_id and lot of other useful metadata. 10 answers per questions are contained in the answers attribute. For test set, 10 empty strings are contained in the answers field as the answers are not available for it.

An example look like below:

  {'question': 'who is this copyrighted by?',
   'image_id': '00685bc495504d61',
   'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>,
   'image_classes': ['Vehicle', 'Tower', 'Airplane', 'Aircraft'],
   'flickr_original_url': 'https://farm2.staticflickr.com/5067/5620759429_4ea686e643_o.jpg',
   'flickr_300k_url': 'https://c5.staticflickr.com/6/5067/5620759429_f43a649fb5_z.jpg',
   'image_width': 786,
   'image_height': 1024,
   'answers': ['simon clancy',
    'simon ciancy',
    'simon clancy',
    'simon clancy',
    'the brand is bayard',
    'simon clancy',
    'simon clancy',
    'simon clancy',
    'simon clancy',
    'simon clancy'],
   'question_tokens': ['who', 'is', 'this', 'copyrighted', 'by'],
   'question_id': 3,
   'set_name': 'train'
  },

Data Fields

question: string, the question that is being asked about the image
image_id: string, id of the image which is same as the OpenImages id
image: A PIL.Image.Image object containing the image about which the question is being asked. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
image_classes: List[str], The OpenImages classes to which the image belongs to.
flickr_original_url: string, URL to original image on Flickr
flickr_300k_url: string, URL to resized and low-resolution image on Flickr.
image_width: int, Width of the original image.
image_height: int, Height of the original image.
question_tokens: List[str], A pre-tokenized list of question.
answers: List[str], List of 10 human-annotated answers for the question. These 10 answers are collected from 10 different users. The list will contain empty strings for test set for which we don't have the answers.
question_id: int, Unique id of the question.
set_name: string, the set to which this question belongs.

Data Splits

There are three splits. train, validation and test. The train and validation sets share images with OpenImages train set and have their answers available. For test set answers, we return a list of ten empty strings. To get inference results and numbers on test set, you need to go to the EvalAI leaderboard and upload your predictions there. Please see instructions at https://textvqa.org/challenge/.

Dataset Creation

Curation Rationale

From the paper:

Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today’s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new “TextVQA” dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer.

Source Data

Initial Data Collection and Normalization

The initial images were sourced from OpenImages v4 dataset. These were first filtered based on automatic heuristics using an OCR system where we only took images which had at least some text detected in them. See annotation process section to understand the next stages.

Who are the source language producers?

English Crowdsource Annotators

Annotations

Annotation process

After the automatic process of filter the images that contain text, the images were manually verified using human annotators making sure that they had text. In next stage, the annotators were asked to write questions involving scene text for the image. For some images, in this stage, two questions were collected whenever possible. Finally, in the last stage, ten different human annotators answer the questions asked in last stage.

Who are the annotators?

Annotators are from one of the major data collection platforms such as AMT. Exact details are not mentioned in the paper.

Personal and Sensitive Information

The dataset does have similar PII issues as OpenImages and can at some times contain human faces, license plates, and documents. Using provided image_classes data field is one option to try to filter out some of this information.

Considerations for Using the Data

Social Impact of Dataset

The paper helped realize the importance of scene text recognition and reasoning in general purpose machine learning applications and has led to many follow-up works including TextCaps and TextOCR. Similar datasets were introduced over the time which specifically focus on sight-disabled users such as VizWiz or focusing specifically on the same problem as TextVQA like STVQA, DocVQA and OCRVQA. Currently, most methods train on combined dataset from TextVQA and STVQA to achieve state-of-the-art performance on both datasets.

Discussion of Biases

Question-only bias where a model is able to answer the question without even looking at the image is discussed in the paper which was a major issue with original VQA dataset. The outlier bias in answers is prevented by collecting 10 different answers which are also taken in consideration by the evaluation metric.

Other Known Limitations

The dataset is english only but does involve images with non-English latin characters so can involve some multi-lingual understanding.
The performance on the dataset is also dependent on the quality of OCR used as the OCR errors can directly lead to wrong answers.
The metric used for calculating accuracy is same as VQA accuracy. This involves one-to-one matching with the given answers and thus doesn't allow analyzing one-off errors through OCR.

Additional Information

Dataset Curators

Amanpreet Singh
Vivek Natarjan
Meet Shah
Yu Jiang
Xinlei Chen
Dhruv Batra
Devi Parikh
Marcus Rohrbach

Licensing Information

CC by 4.0

Citation Information

@inproceedings{singh2019towards,
    title={Towards VQA Models That Can Read},
    author={Singh, Amanpreet and Natarjan, Vivek and Shah, Meet and Jiang, Yu and Chen, Xinlei and Batra, Dhruv and Parikh, Devi and Rohrbach, Marcus},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    pages={8317-8326},
    year={2019}
}

Contributions

Thanks to @apsdehal for adding this dataset.

Datasets:
textvqa