---
# System prepended metadata

title: DocLayNet

---


# DocLayNet

## Summary
* [Introduction](#introduction)
* [Dataset Structure](#dataset-structure)
* [Reference](#reference)
* [License](#license)
* [Citation](#citation)

## Introduction
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 Instances
DocLayNet provides four types of data assets:

1. PNG images of all pages, resized to square 1025 x 1025px
2. Bounding-box annotations in COCO format for each PNG image
3. Extra: Single-page PDF files matching each PNG image
4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
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
    },
```

### Data Fields
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

### Dataset Curators
The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research. You can contact us at deepsearch-core@zurich.ibm.com.

Curators:

* Christoph Auer, [@cau-git](https://github.com/cau-git)
* Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
* Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
* Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)

## Reference

We would like to acknowledge Pfitzmann, Birgit and et al. for creating and maintaining the DocLayNet dataset as a valuable resource for the computer vision and machine learning research community. For more information about the DocLayNet dataset and its creator, please visit [the DocLayNet website](https://ds4sd.github.io/icdar23-doclaynet/).


## License 

The dataset has been released under the Community Data License Agreement - Permissive 1.0.
## Citation
```
@article{doclaynet2022,
  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
  doi = {10.1145/3534678.353904},
  url = {https://doi.org/10.1145/3534678.3539043},
  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
  year = {2022},
  isbn = {9781450393850},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages = {3743–3751},
  numpages = {9},
  location = {Washington DC, USA},
  series = {KDD '22}
}
```