Update README.md

README.md

@@ -1,52 +1,95 @@
 ---
-dataset_info:
-  features:
-  - name: filename
-    dtype: string
-  - name: split
-    dtype: string
-  - name: imgid
-    dtype: int64
-  - name: dataset
-    dtype: string
-  - name: table_bbox
-    sequence: int64
-  - name: cells
-    list:
-      list:
-      - name: tokens
-        sequence: string
-      - name: bbox
-        sequence: int64
-  - name: otsl
-    sequence: string
-  - name: html
-    sequence: string
-  - name: html_restored
-    sequence: string
-  - name: cols
-    dtype: int64
-  - name: rows
-    dtype: int64
-  - name: html_len
-    dtype: int64
-  - name: otsl_len
-    dtype: int64
-  - name: image
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 64476011817.784
-    num_examples: 1502054
-  - name: val
-    num_bytes: 8063269734.066
-    num_examples: 187978
-  - name: test
-    num_bytes: 7953703702.636
-    num_examples: 185682
-  download_size: 62335182023
-  dataset_size: 80492985254.486
+license: other
+pretty_name: PubTabNet-OTSL
+size_categories:
+- 10K<n<100K
+tags:
+- table-structure-recognition
+- table-understanding
+- PDF
+task_categories:
+- object-detection
+- table-to-text
 ---
-# Dataset Card for "PubTables-1M_OTSL"
+# Dataset Card for PubTables-1M_OTSL
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## Dataset Description
+
+- **Homepage:** https://ds4sd.github.io
+- **Paper:** https://arxiv.org/pdf/2305.03393
+
+### Dataset Summary
+
+This dataset is a conversion of the original [PubTables-1M](https://github.com/microsoft/table-transformer) into the OTSL format presented in our paper "Optimized Table Tokenization for Table Structure Recognition". The dataset includes the original annotations amongst new additions.
+
+### Dataset Structure
+
+* cells: origunal dataset cell groundtruth (content).
+* table_bbox: origunal dataset table detection groundtruth.
+* otsl: new reduced table structure token format
+* html: Generated HTML for PubTables-1M to match PubTabNet, FinTabNet, and SynthTabNet format.
+* html_restored: generated HTML from OTSL.
+* cols: grid column length.
+* rows: grid row length.
+* image: PIL image
+
+### OTSL Vocabulary:
+
+**OTSL**: new reduced table structure token format
+More information on the OTSL table structure format and its concepts can be read from our paper.
+Format of this dataset extends work presented in a paper, and introduces slight modifications:
+
+* "fcel" - cell that has content in it
+* "ecel" - cell that is empty
+* "lcel" - left-looking cell (to handle horizontally merged cells)
+* "ucel" - up-looking cell (to handle vertically merged cells)
+* "xcel" - 2d span cells, in this dataset - covers entire area of a merged cell
+* "nl" - new line token
+
+### Data Splits
+
+The dataset provides three splits
+- `train`
+- `val`
+- `test`
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset is converted by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
+You can contact us at [[email protected]](mailto:[email protected]).
+
+Curators:
+- Maksym Lysak, [@maxmnemonic](https://github.com/maxmnemonic)
+- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
+- Christoph Auer, [@cau-git](https://github.com/cau-git)
+- Nikos Livathinos, [@nikos-livathinos](https://github.com/nikos-livathinos)
+- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
+
+
+### Citation Information
+
+**Citation to OTSL Paper:
+**
+
+	@article{lysak2023optimized,
+	      title={Optimized Table Tokenization for Table Structure Recognition}, 
+	      author={Maksym Lysak and Ahmed Nassar and Nikolaos Livathinos and Christoph Auer and Peter Staar},
+	      year={2023},
+	      eprint={2305.03393},
+	      archivePrefix={arXiv},
+	      primaryClass={cs.CV}
+	}
+
+**Citation to PubTables-1M creators:
+**
+
+	@inproceedings{smock2022pubtables,
+	  title={Pub{T}ables-1{M}: Towards comprehensive table extraction from unstructured documents},
+	  author={Smock, Brandon and Pesala, Rohith and Abraham, Robin},
+	  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+	  pages={4634-4642},
+	  year={2022},
+	  month={June}
+	}