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End of preview. Expand
in Data Studio
Dataset Card for DocLayNet large without image
About this card (02/14/2024)
Property and license
All information from this page but the content of this paragraph "About this card (02/14/2025)" has been copied/pasted from Dataset Card for DocLayNet.
DocLayNet is a dataset created by Deep Search (IBM Research) published under license CDLA-Permissive-1.0.
I do not claim any rights to the data taken from this dataset and published on this page.
Dataset Card for DocLayNet
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:
- 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
- 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
- Detailed label set: DocLayNet defines 11 class labels to distinguish layout features in high detail.
- 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
- 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 https://ds4sd.github.io/icdar23-doclaynet/.
Dataset Structure
Data Fields
DocLayNet provides four types of data assets:
- Bounding-box annotations in COCO format for each PNG image
- Extra: Single-page PDF files matching each PNG image
- 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:
financial_reports,
scientific_articles,
laws_and_regulations,
government_tenders,
manuals,
patents
Data Splits
The dataset provides three splits
trainvaltest
Dataset Creation
Annotations
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 deepsearch-core@zurich.ibm.com.
Curators:
- Christoph Auer, @cau-git
- Michele Dolfi, @dolfim-ibm
- Ahmed Nassar, @nassarofficial
- Peter Staar, @PeterStaar-IBM
Licensing Information
License: CDLA-Permissive-1.0
Citation Information
@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}
}
Contributions
Thanks to @dolfim-ibm, @cau-git for adding this dataset.
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