add content to the README

README.md

@@ -24,20 +24,11 @@ task_ids:
 - [Dataset Description](#dataset-description)
   - [Dataset Summary](#dataset-summary)
   - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
 - [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
   - [Data Fields](#data-fields)
   - [Data Splits](#data-splits)
 - [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
   - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
 - [Additional Information](#additional-information)
   - [Dataset Curators](#dataset-curators)
   - [Licensing Information](#licensing-information)
@@ -46,98 +37,129 @@ task_ids:
 
 ## Dataset Description
 
-- **Homepage:**
-- **Repository:**
-- **Paper:**
+- **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
 
-[More Information Needed]
+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:
 
-### Supported Tasks and Leaderboards
-
-[More Information Needed]
+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.
 
-### Languages
+### Supported Tasks and Leaderboards
 
-[More Information Needed]
+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 Instances
-
-[More Information Needed]
-
 ### Data Fields
 
-[More Information Needed]
+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
 
-[More Information Needed]
+The dataset provides three splits
+- `train`
+- `val`
+- `test`
 
 ## Dataset Creation
 
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
 ### Annotations
 
 #### Annotation process
 
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
+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).
 
-## Considerations for Using the Data
 
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
+#### Who are the annotators?
 
-### Other Known Limitations
+Annotations are crowdsourced.
 
-[More Information Needed]
 
 ## Additional Information
 
 ### Dataset Curators
 
-[More Information Needed]
+The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
+You can contact us at [[email protected]](mailto:[email protected]).
+
+Curators:
+- Christoph Auer, [@cau-git](https://github.com/dolfim-ibm)
+- 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
+License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
 
-[More Information Needed]
 
 ### Citation Information
 
-[More Information Needed]
+
+```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 [@github-username](https://github.com/<github-username>) for adding this dataset.
+Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/dolfim-ibm) for adding this dataset.