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--- |
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annotations_creators: |
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- crowdsourced |
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license: other |
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pretty_name: DocLayNet |
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size_categories: |
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- 10K<n<100K |
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tags: |
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- layout-segmentation |
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- COCO |
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- document-understanding |
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- PDF |
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task_categories: |
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- object-detection |
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- image-segmentation |
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task_ids: |
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- instance-segmentation |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: bboxes |
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sequence: |
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sequence: float64 |
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- name: category_id |
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sequence: int64 |
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- name: segmentation |
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sequence: |
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sequence: |
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sequence: float64 |
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- name: area |
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sequence: float64 |
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- name: pdf_cells |
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list: |
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list: |
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- name: bbox |
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sequence: float64 |
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- name: font |
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struct: |
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- name: color |
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sequence: int64 |
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- name: name |
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dtype: string |
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- name: size |
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dtype: float64 |
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- name: text |
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dtype: string |
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- name: metadata |
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struct: |
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- name: coco_height |
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dtype: int64 |
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- name: coco_width |
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dtype: int64 |
|
- name: collection |
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dtype: string |
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- name: doc_category |
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dtype: string |
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- name: image_id |
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dtype: int64 |
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- name: num_pages |
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dtype: int64 |
|
- name: original_filename |
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dtype: string |
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- name: original_height |
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dtype: float64 |
|
- name: original_width |
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dtype: float64 |
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- name: page_hash |
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dtype: string |
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- name: page_no |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 28172005254.125 |
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num_examples: 69375 |
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- name: test |
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num_bytes: 1996179229.125 |
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num_examples: 4999 |
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- name: val |
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num_bytes: 2493896901.875 |
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num_examples: 6489 |
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download_size: 7766115331 |
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dataset_size: 32662081385.125 |
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--- |
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|
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# Dataset Card for DocLayNet v1.1 |
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|
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Annotations](#annotations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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|
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## Dataset Description |
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- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/ |
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- **Repository:** https://github.com/DS4SD/DocLayNet |
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- **Paper:** https://doi.org/10.1145/3534678.3539043 |
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|
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### Dataset Summary |
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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: |
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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 |
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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 |
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3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail. |
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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 |
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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. |
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## Dataset Structure |
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|
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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. |
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* `image`: page PIL image. |
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* `bboxes`: a list of layout bounding boxes. |
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* `category_id`: a list of class ids corresponding to the bounding boxes. |
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* `segmentation`: a list of layout segmentation polygons. |
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* `pdf_cells`: a list of lists corresponding to `bbox`. Each list contains the PDF cells (content) inside the bbox. |
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* `metadata`: page and document metadetails. |
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Bounding boxes classes / categories: |
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|
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``` |
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1: Caption |
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2: Footnote |
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3: Formula |
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4: List-item |
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5: Page-footer |
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6: Page-header |
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7: Picture |
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8: Section-header |
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9: Table |
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10: Text |
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11: Title |
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``` |
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The `["metadata"]["doc_category"]` field uses one of the following constants: |
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``` |
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* financial_reports, |
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* scientific_articles, |
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* laws_and_regulations, |
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* government_tenders, |
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* manuals, |
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* patents |
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``` |
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### Data Splits |
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The dataset provides three splits |
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- `train` |
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- `val` |
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- `test` |
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## Dataset Creation |
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### Annotations |
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#### Annotation process |
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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). |
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#### Who are the annotators? |
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Annotations are crowdsourced. |
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## Additional Information |
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### Dataset Curators |
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The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research. |
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You can contact us at [[email protected]](mailto:[email protected]). |
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Curators: |
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- Christoph Auer, [@cau-git](https://github.com/cau-git) |
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- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm) |
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- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial) |
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- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM) |
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### Licensing Information |
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License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/) |
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### Citation Information |
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|
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```bib |
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@article{doclaynet2022, |
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title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation}, |
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doi = {10.1145/3534678.353904}, |
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url = {https://doi.org/10.1145/3534678.3539043}, |
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author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J}, |
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year = {2022}, |
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isbn = {9781450393850}, |
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publisher = {Association for Computing Machinery}, |
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address = {New York, NY, USA}, |
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booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, |
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pages = {3743–3751}, |
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numpages = {9}, |
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location = {Washington DC, USA}, |
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series = {KDD '22} |
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} |
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``` |
