Update README.md

README.md

@@ -1,3 +1,44 @@
+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: ner_tags
+    sequence: int64
+  - name: words
+    sequence: string
+  - name: bboxes
+    sequence:
+      sequence: int64
+  splits:
+  - name: train
+    num_bytes: 477503369
+    num_examples: 10000
+  download_size: 342662174
+  dataset_size: 477503369
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+license: cc-by-4.0
+task_categories:
+- text-classification
+- token-classification
+language:
+- en
+tags:
+- invoices
+- invoice
+- FATURA
+pretty_name: FATURA2 invoices dataset
+size_categories:
+- 1K<n<10K
+---
+The dataset consists of 10000 jpg images with white backgrounds, 10000 jpg images with colored backgrounds (the same colors used in the paper) as well as 3x10000 json annotation files. The images are generated from 50 different templates.
+
+https://zenodo.org/records/10371464
+
 ---
 dataset_info:
   features:
@@ -23,4 +64,4 @@ configs:
     path: data/train-*
 ---
 
-@misc{limam2023fatura, title={FATURA: A Multi-Layout Invoice Image Dataset for Document Analysis and Understanding}, author={Mahmoud Limam and Marwa Dhiaf and Yousri Kessentini}, year={2023}, eprint={2311.11856}, archivePrefix={arXiv}, primaryClass={cs.CV} }
+@misc{limam2023fatura, title={FATURA: A Multi-Layout Invoice Image Dataset for Document Analysis and Understanding}, author={Mahmoud Limam and Marwa Dhiaf and Yousri Kessentini}, year={2023}, eprint={2311.11856}, archivePrefix={arXiv}, primaryClass={cs.CV} }