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

Rohan Kumar, Jyothi Swaroopa Jinka, Ravi Kiran Sarvadevabhatla

International Institute of Information Technology Hyderabad

[Paper]  [Code & Dataset] 

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

Data-selection Pipeline

Model Architecture

Comparison with State-of-the-Art Approaches

Visualization of results for a subset of baselines and variants in comparison with TexTAR

Download the Dataset and Weights

 Model weights and the MMTAD testset can be downloaded from the link. To get access to the full dataset, please contact This email address is being protected from spambots. You need JavaScript enabled to view it.. .

@article{Kumar2025TexTAR, 

     author = {Rohan Kumar and Jyothi Swaroopa Jinka and Ravi Kiran Sarvadevabhatla}, 
     title = {TexTAR: Textual Attribute Recognition in Multi-domain and Multi-lingual Document Images}, 

     booktitle = {International Conference on Document Analysis and Recognition, ICDAR}, 

     year = {2025}, 
}