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

arXiv: https://arxiv.org/abs/2509.13151 · HTML: https://mlsanity.com/paper/2509.13151/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation](https://mlsanity.com/paper/2401.17904/) (2024)
- [DeepFont: Identify Your Font from An Image](https://mlsanity.com/paper/1507.03196/) (2015)
- [DropRegion Training of Inception Font Network for High-Performance Chinese Font Recognition](https://mlsanity.com/paper/1703.05870/) (2017)
- [EmpLite: A Lightweight Sequence Labeling Model for Emphasis Selection of Short Texts](https://mlsanity.com/paper/2101.03025/) (2020)
- [CONSENT: Context Sensitive Transformer for Bold Words Classification](https://mlsanity.com/paper/2205.07683/) (2022)
- [Exploring Multi-Tasking Learning in Document Attribute Classification](https://mlsanity.com/paper/2108.13382/) (2021)
- [TaCo: Textual Attribute Recognition via Contrastive Learning](https://mlsanity.com/paper/2208.10180/) (2022)

## Abstract (arXiv)

Recognizing textual attributes such as bold, italic, underline and strikeout is essential for understanding text semantics, structure, and visual presentation. These attributes highlight key information, making them crucial for document analysis. 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 novel data selection pipeline enhances context awareness, and our architecture employs a 2D RoPE (Rotary Positional Embedding)-style mechanism to incorporate input context for more accurate attribute predictions. We also introduce MMTAD, a diverse, multilingual, multi-domain dataset annotated with text attributes across real-world documents such as legal records, notices, and textbooks. Extensive evaluations show TexTAR outperforms existing methods, demonstrating that contextual awareness contributes to state-of-the-art TAR performance.