Update README.md

README.md

@@ -79,3 +79,117 @@ configs:
   - split: test
     path: data/test-*
 ---
+# Dataset Card for Docling-DocLayNet dataset
+
+## 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
+
+This dataset is an extention of the [original DocLayNet dataset](https://github.com/DS4SD/DocLayNet) which embeds the PDF files of the document images inside a binary column.
+
+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
+
+### Data Fields
+
+* image: PIL image of all pages, resized to square 1025 x 1025px.
+* bboxes: Bounding-box annotations in COCO format for each PNG image.
+* category_id: integer representations of the segmentation labels (see below).
+* segmentation:
+* area:
+* pdf_cells:
+* metadata:
+* pdf: Binary blob with the original PDF image.
+
+
+This is the mapping between the labels and the `category_id`:
+
+```
+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 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`
+
+
+## Additional Information
+
+### Citation Information
+
+"DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis" (KDD 2022).
+
+    Birgit Pfitzmann ([email protected])
+    Christoph Auer ([email protected])
+    Michele Dolfi ([email protected])
+    Ahmed Nassar ([email protected])
+    Peter Staar ([email protected])
+
+ArXiv link: https://arxiv.org/abs/2206.01062
+
+```bib
+@article{doclaynet2022,
+  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},  
+  doi = {10.1145/3534678.353904},
+  url = {https://arxiv.org/abs/2206.01062},
+  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
+  year = {2022}
+}
+```