DocLayNet /
dolfim-ibm's picture
fix github url
  - crowdsourced
license: other
pretty_name: DocLayNet
  - 10K<n<100K
  - layout-segmentation
  - COCO
  - document-understanding
  - PDF
  - object-detection
  - image-segmentation
  - instance-segmentation

Dataset Card for DocLayNet

Table of Contents

Dataset Description

Dataset Summary

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.

Supported Tasks and Leaderboards

We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see

Dataset Structure

Data Fields

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

The doc_category field uses one of the following constants:


Data Splits

The dataset provides three splits

  • train
  • val
  • test

Dataset Creation


Annotation process

The labeling guideline used for training of the annotation experts are available at DocLayNet_Labeling_Guide_Public.pdf.

Who are the annotators?

Annotations are crowdsourced.

Additional Information

Dataset Curators

The dataset is curated by the Deep Search team at IBM Research. You can contact us at


Licensing Information

License: CDLA-Permissive-1.0

Citation Information

  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
  doi = {10.1145/3534678.353904},
  url = {},
  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}


Thanks to @dolfim-ibm, @cau-git for adding this dataset.