README.md

---
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](https://github.com/DS4SD/DocLayNet) 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

```js
    ...
    {
      "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

```bib
@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}
}
```