File size: 2,943 Bytes
7bdb468 28e8c4f 86acd18 28e8c4f 5b51011 28e8c4f 4b34880 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
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}
}
``` |