README.md

metadata

annotations_creators:
  - machine-generated
language:
  - en
language_creators:
  - found
license:
  - cdla-permissive-1.0
multilinguality:
  - monolingual
pretty_name: PubLayNet
size_categories: []
source_datasets:
  - original
tags:
  - graphic design
  - layout-generation
task_categories:
  - image-classification
  - image-segmentation
  - image-to-text
  - question-answering
  - other
  - multiple-choice
  - token-classification
  - tabular-to-text
  - object-detection
  - table-question-answering
  - text-classification
  - table-to-text
task_ids:
  - multi-label-image-classification
  - multi-class-image-classification
  - semantic-segmentation
  - image-captioning
  - extractive-qa
  - closed-domain-qa
  - multiple-choice-qa
  - named-entity-recognition

Dataset Card for PubLayNet

Table of Contents

• Dataset Card Creation Guide
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Supported Tasks and Leaderboards
    • Languages
  • Dataset Structure
    • Data Instances
    • Data Fields
    • Data Splits
  • Dataset Creation
    • Curation Rationale
    • Source Data
      • Initial Data Collection and Normalization
      • Who are the source language producers?
    • Annotations
      • Annotation process
      • Who are the annotators?
    • Personal and Sensitive Information
  • Considerations for Using the Data
    • Social Impact of Dataset
    • Discussion of Biases
    • Other Known Limitations
  • Additional Information
    • Dataset Curators
    • Licensing Information
    • Citation Information
    • Contributions

Dataset Description

• Homepage: https://developer.ibm.com/exchanges/data/all/publaynet/
• Repository: https://github.com/shunk031/huggingface-datasets_PubLayNet
• Paper (Preprint): https://arxiv.org/abs/1908.07836
• Paper (ICDAR2019): https://ieeexplore.ieee.org/document/8977963

Dataset Summary

PubLayNet is a dataset for document layout analysis. It contains images of research papers and articles and annotations for various elements in a page such as "text", "list", "figure" etc in these research paper images. The dataset was obtained by automatically matching the XML representations and the content of over 1 million PDF articles that are publicly available on PubMed Central.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

import datasets as ds

dataset = ds.load_dataset(
    path="shunk031/PubLayNet",
    decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask.
)

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

[More Information Needed]

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

[More Information Needed]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

• CDLA-Permissive

Citation Information

@inproceedings{zhong2019publaynet,
  title={Publaynet: largest dataset ever for document layout analysis},
  author={Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno},
  booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
  pages={1015--1022},
  year={2019},
  organization={IEEE}
}

Contributions

Thanks to ibm-aur-nlp/PubLayNet for creating this dataset.