README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: bboxes
      sequence:
        sequence: float64
    - name: category_id
      sequence: int64
    - name: segmentation
      sequence:
        sequence:
          sequence: float64
    - name: area
      sequence: float64
    - name: pdf_cells
      list:
        list:
          - name: bbox
            sequence: float64
          - name: font
            struct:
              - name: color
                sequence: int64
              - name: name
                dtype: string
              - name: size
                dtype: float64
          - name: text
            dtype: string
    - name: metadata
      struct:
        - name: coco_height
          dtype: int64
        - name: coco_width
          dtype: int64
        - name: collection
          dtype: string
        - name: doc_category
          dtype: string
        - name: image_id
          dtype: int64
        - name: num_pages
          dtype: int64
        - name: original_filename
          dtype: string
        - name: original_height
          dtype: float64
        - name: original_width
          dtype: float64
        - name: page_hash
          dtype: string
        - name: page_no
          dtype: int64
    - name: pdf
      dtype: binary
    - name: modalities
      sequence: string
  splits:
    - name: train
      num_bytes: 35626146180.25
      num_examples: 69375
    - name: validation
      num_bytes: 3090589267.941
      num_examples: 6489
    - name: test
      num_bytes: 2529339432.131
      num_examples: 4999
  download_size: 39770621829
  dataset_size: 41246074880.322
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for Docling-DocLayNet dataset

Dataset Description

• Homepage: https://developer.ibm.com/exchanges/data/all/doclaynet/
• Repository: https://github.com/DS4SD/DocLayNet
• Paper: https://doi.org/10.1145/3534678.3539043

Dataset Summary

This dataset is an extention of the original DocLayNet dataset which embeds the PDF files of the document images inside a binary column.

DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:

1. Human Annotation: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
2. Large layout variability: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
3. Detailed label set: DocLayNet defines 11 class labels to distinguish layout features in high detail.
4. Redundant annotations: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
5. Pre-defined train- test- and validation-sets: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.

Dataset Structure

Data Fields

• image: PIL image of all pages, resized to square 1025 x 1025px.
• bboxes: Bounding-box annotations in COCO format for each PNG image.
• category_id: integer representations of the segmentation labels (see below).
• segmentation:
• area:
• pdf_cells:
• metadata:
• pdf: Binary blob with the original PDF image.

This is the mapping between the labels and the category_id:

1: "caption"
2: "footnote"
3: "formula"
4: "list_item"
5: "page_footer"
6: "page_header"
7: "picture"
8: "section_header"
9: "table"
10: "text"
11: "title"

The COCO image record are defined like this example

    ...
    {
      "id": 1,
      "width": 1025,
      "height": 1025,
      "file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",

      // Custom fields:
      "doc_category": "financial_reports" // high-level document category
      "collection": "ann_reports_00_04_fancy", // sub-collection name
      "doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
      "page_no": 9, // page number in original document
      "precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
    },
    ...

The doc_category field uses one of the following constants:

financial_reports,
scientific_articles,
laws_and_regulations,
government_tenders,
manuals,
patents

Data Splits

The dataset provides three splits

• train
• val
• test

Additional Information

Citation Information

"DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis" (KDD 2022).

Birgit Pfitzmann ([email protected])
Christoph Auer ([email protected])
Michele Dolfi ([email protected])
Ahmed Nassar ([email protected])
Peter Staar ([email protected])

ArXiv link: https://arxiv.org/abs/2206.01062

@article{doclaynet2022,
  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},  
  doi = {10.1145/3534678.353904},
  url = {https://arxiv.org/abs/2206.01062},
  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
  year = {2022}
}