---
sdk: static
title: TexTAR
emoji: "📚"
license: mit
---

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)