README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
multilinguality:
  - monolingual
license:
  - cc-by-4.0
pretty_name: CORD
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - token-classification
task_ids:
  - parsing

Dataset Card for CORD (Consolidated Receipt Dataset)

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Additional Information
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Repository: https://github.com/clovaai/cord
• Paper: https://openreview.net/pdf?id=SJl3z659UH
• Leaderboard: https://paperswithcode.com/dataset/cord

Dataset Summary

[More Information Needed]

Supported Tasks and Leaderboards

[More Information Needed]

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

{
  "id": datasets.Value("string"),
  "words": datasets.Sequence(datasets.Value("string")),
  "bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
  "labels": datasets.Sequence(datasets.features.ClassLabel(names=_LABELS)),
  "images": datasets.features.Image(),
}

Data Splits

• train (800 rows)
• validation (100 rows)
• test (100 rows)

Dataset Creation

Licensing Information

Creative Commons Attribution 4.0 International License

Citation Information

@article{park2019cord,
  title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},
  author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}
  booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
  year={2019}
}

Contributions

Thanks to @clovaai for adding this dataset.