| --- |
| annotations_creators: |
| - crowdsourced |
| license: other |
| pretty_name: DocLayNet |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - layout-segmentation |
| - COCO |
| - document-understanding |
| - PDF |
| task_categories: |
| - object-detection |
| - image-segmentation |
| task_ids: |
| - instance-segmentation |
| --- |
| |
| # Dataset Card for DocLayNet v1.1 |
|
|
| ## 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 |
|
|
| ### 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. |
|
|
|
|
| ## Dataset Structure |
|
|
| This dataset is structured differently from the other repository [ds4sd/DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet), as this one includes the content (PDF cells) of the detections, and abandons the COCO format. |
|
|
| * `image`: page PIL image. |
| * `bboxes`: a list of layout bounding boxes. |
| * `category_id`: a list of class ids corresponding to the bounding boxes. |
| * `segmentation`: a list of layout segmentation polygons. |
| * `pdf_cells`: a list of lists corresponding to `bbox`. Each list contains the PDF cells (content) inside the bbox. |
| * `metadata`: page and document metadetails. |
|
|
| Bounding boxes classes / categories: |
|
|
| ``` |
| 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 `["metadata"]["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} |
| } |
| ``` |
|
|
