Datasets:
Sub-tasks:
instance-segmentation
Size Categories:
10K<n<100K
Annotations Creators:
crowdsourced
License:
| annotations_creators: | |
| - crowdsourced | |
| license: other | |
| pretty_name: DocLayNet | |
| size_categories: | |
| - 10K<n<100K | |
| tags: | |
| - layout-segmentation | |
| - COCO | |
| - document-understanding | |
| task_categories: | |
| - object-detection | |
| - image-segmentation | |
| task_ids: | |
| - instance-segmentation | |
| # Dataset Card for DocLayNet | |
| ## Table of Contents | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Annotations](#annotations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## 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 | |
| - **Leaderboard:** | |
| - **Point of Contact:** | |
| ### 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 https://ds4sd.github.io/icdar23-doclaynet/. | |
| ## 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 | |
| ```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` | |
| ## Dataset Creation | |
| ### Annotations | |
| #### Annotation process | |
| The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/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](https://ds4sd.github.io/) at IBM Research. | |
| You can contact us at [deepsearch-core@zurich.ibm.com](mailto: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) | |
| ### Licensing Information | |
| License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/) | |
| ### Citation Information | |
| ```bib | |
| @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](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset. | |