HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understanding
Automated fine-grained perception of calligraphy styles--a task vital to cultural heritage preservation--remains a critical challenge for Large Vision-Language Models (LVLMs), largely constrained by existing datasets that suffer from modal mixture and flattened labels. To bridge this gap, we introduce HCSU, the first comprehensive dataset tailored for fine-grained Historical Calligraphy Style Understanding. HCSU comprises 39,307 meticulously curated character images from 49 historically prominent calligraphers across 10 dynasties, systematically decoupling authentic ink manuscripts (Tie) from stone rubbings (Bei) to resolve the long-standing modal mixture problem. Moving beyond convention...
| Authors | Yinsheng Yao, Yan Liu, Chen Ye |
|---|---|
| Primary category | cs.CV |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
Models and datasets referencing this paper
- Tongji209/HCSUDataset
- YAN-LIU05/HCSUDataset
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