Add README to dataset repo

README.md

@@ -19,3 +19,57 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+We introduce **TexTAR**, a multi-task, context-aware Transformer for Textual Attribute Recognition (TAR),
+capable of handling both positional cues (bold, italic) and visual cues (underline, strikeout) in
+noisy, multilingual document images. 
+## MMTAD Dataset
+
+**MMTAD** (Multilingual Multi-domain Textual Attribute Dataset) comprises **1,623** real-world document images—from legislative records and notices to textbooks and notary documents—captured under diverse lighting, layout, and noise conditions. It delivers **1,117,716** word-level annotations for two attribute groups:
+
+- **T1**: Bold,Italic,Bold & Italic  
+
+- **T2**: Underline,Strikeout,Underline & Strikeout 
+
+**Language & Domain Coverage**  
+- English, Spanish, and six South Asian languages  
+- Distribution: 67.4 % Hindi, 8.2 % Telugu, 8.0 % Marathi, 5.9 % Punjabi, 5.4 % Bengali, 5.2 % Gujarati/Tamil/others  
+- 300–500 annotated words per image on average  
+
+To address class imbalance (e.g., fewer italic or strikeout samples), we apply **context-aware augmentations**:  
+- Shear transforms to generate additional italics  
+- Realistic, noisy underline and strikeout overlays  
+
+These augmentations preserve document context and mimic real-world distortions, ensuring a rich, balanced benchmark for textual attribute recognition.  
+
+**More Information**  
+For detailed documentation and resources, visit our website: [TexTAR](https://tex-tar.github.io/) 
+
+**Downloading the Dataset**
+ ```
+from datasets import load_dataset
+
+ds = load_dataset("textar/MMTAD")
+print(ds)
+```
+Dataset contains 
+- `textar-testset`: document images
+- `testset_labels.json`: a JSON array or dict where each key/entry is an image filename and the value is its annotated attribute labels (bold, italic, underline, strikeout, etc. for each word)  
+
+**Viewer Format**
+
+To power the Hugging-Face Dat Studio we convert the original
+testset_labels.json into a line-delimited JSONL (hierarchical.jsonl) of the form:
+```
+{"image":"textar-testset/ncert-page_25.png",
+ "annotation_json":"[{"bb_dim":[73,176,157,213],"bb_ids":[{"id":71120,"ocrv":"huge","attb":{"bold":false,"italic":false,"b_i":false,"no_bi":true,…}}]},…]"}
+```
+**Citation**
+
+Please use the following BibTeX entry for citation .
+```bibtex
+@article{Kumar2025TexTAR,
+  title   = {TexTAR: Textual Attribute Recognition in Multi-domain and Multi-lingual Document Images},
+  author  = {Rohan Kumar and Jyothi Swaroopa Jinka and Ravi Kiran Sarvadevabhatla},
+  booktitle = {International Conference on Document Analysis and Recognition,
+            {ICDAR}},