TexTAR – Textual Attribute Recognition

Abstract

Recognising textual attributes such as bold, italic, underline and ~~strikeout~~ is essential for understanding text semantics, structure and visual presentation. Existing methods struggle with computational efficiency or adaptability in noisy, multilingual settings. To address this, we introduce TexTAR, a multi-task, context-aware Transformer for Textual Attribute Recognition (TAR). Our data-selection pipeline enhances context awareness and our architecture employs a 2-D RoPE mechanism to incorporate spatial context for more accurate predictions. We also present MMTAD, a diverse multilingual dataset annotated with text attributes across real-world documents. TexTAR achieves state-of-the-art performance in extensive evaluations.

Textual Attributes in the Dataset

Image	T₁		T₂
Image	bold	italic	underline	strikeout
	✗	✗	✗	✗
	✗	✗	✓	✗
	✓	✗	✓	✗
	✗	✗	✓	✓
	✓	✓	✗	✗
	✗	✓	✓	✗
	✓	✗	✗	✓

Chart – distribution of annotated attributes in our dataset.

Data-selection Pipeline

Model Architecture

Comparison with State-of-the-Art Approaches

Methods	normal	T₁ group			T₂ group			Average
Methods	normal	bold	italic	b & i	underline	strikeout	u & s	Average
Baselines
ResNet-18 [5]	0.97	0.75	0.88	0.77	0.68	0.97	0.99	0.86
ResNet-50 [5]	0.97	0.74	0.89	0.69	0.73	0.98	0.99	0.86
ResNeXt-101 [18]	0.97	0.77	0.91	0.74	0.78	0.99	0.99	0.88
EfficientNet-b4 [15]	0.97	0.75	0.90	0.62	0.75	0.98	0.99	0.85
Variants
DeepFont [17]	0.97	0.72	0.80	0.44	0.64	0.93	0.98	0.78
DropRegion† [21]	0.98	0.77	0.90	0.61	0.75	0.97	0.99	0.85
MTL [9]	0.97	0.75	0.89	0.64	0.70	0.97	0.99	0.84
TaCo† [10]	0.97	0.79	0.90	0.60	0.78	0.86	0.89	0.83
CONSENT† [12]	0.98	0.86	0.93	0.84	0.81	0.96	0.98	0.91
TexTAR (Ours)	0.99	0.92	0.95	0.90	0.87	0.99	0.99	0.94

All scores are F₁. u & s = underline & strikeout, b & i = bold & italic. † = our re-implementation.

Citation

@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},
  journal = {arXiv},
  year    = {2025}
}

Acknowledgements

International Institute of Information Technology Hyderabad, India.

Contact

rohan.kumar@students.iiit.ac.in
jinka.swaroopa@research.iiit.ac.in
ravi.kiran@iiit.ac.in

TexTAR – Textual Attribute Recognition in Multi-domain and Multi-lingual Document Images

Accepted at ICDAR 2025