init

README.md

@@ -27,7 +27,7 @@ configs:
 ---
 
 # TextAnchor-Bench (TABench)
-**Links:** [📄 Paper: Q-Mask: Query-driven Causal Masks for Text Anchoring in OCR-Oriented Vision-Language Models](https://arxiv.org/abs/2604.00161)
+**📄 Paper Link:** [Q-Mask: Query-driven Causal Masks for Text Anchoring in OCR-Oriented Vision-Language Models](https://arxiv.org/abs/2604.00161)
 
 TABench evaluates whether a vision-language model can (i) accurately read the text within a specified region (**Region-to-Text, R2T**) and (ii) localize the region(s) corresponding to a given text query (**Text-to-Region, T2R**). It contains 5,450 queries in total with an exact 1:1 balance between the two tasks, defined over the same set of 973 core images. The benchmark is curated from four public datasets (**HierText, SVRD, CDLA, ICDAR2015**) and covers 12 representative scenarios spanning both scene text and document-centric settings.