MMTAD

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.