Rohan Kumar, B.Tech, alumni and Jyothi Swaroopa Jinka, MS student  supervised by Dr. Ravi Kiran Sarvadevabhatla received the Best Student Paper Award at ICDAR 2025 for their research on TexTAR – Textual Attribute Recognition in Multi-domain and Multi-lingual Document Images. ICDAR, the flagship document understanding conference, was held in Wuhan, Hubei, China, from 16 – 21 September. Here is the summary of the research work as explained by the authors:

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.

Full paper: https://tex-tar.github.io/

September 2025