---
dataset_info:
  features:
  - name: image
    dtype:
      image:
        decode: false
  - name: annotation_json
    dtype: string
  splits:
  - name: test
    num_bytes: 231043093.0
    num_examples: 481
  download_size: 191751258
  dataset_size: 231043093.0
configs:
- config_name: default
  data_files:
  - 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("Tex-TAR/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 Data 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}},
  year    = {2025}
}
```