HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understanding

2026-07-05

AI Summary

Existing vision-language models struggle to achieve fine-grained perception of calligraphic styles due to modality entanglement and flattened labeling in current datasets. To address this, this work introduces HCSU—the first dataset dedicated to historical Chinese calligraphy style understanding—comprising 39,307 character images from 49 calligraphers across 10 dynasties. HCSU systematically disentangles the two primary modalities of ink-on-paper (tie) and stone inscriptions (bei) and incorporates expert-authored hierarchical aesthetic descriptions. This dataset enables fine-grained style discrimination and interpretable aesthetic reasoning, establishing a new benchmark for the field. Evaluations reveal that while contemporary large models exhibit preliminary style awareness, they remain susceptible to interference from font type, textual content, and source provenance, hindering accurate aesthetic judgments grounded in nuanced brushstroke details.

Abstract

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 conventional flattened labels, HCSU provides hierarchical expert-written aesthetic descriptions, enabling two rigorous evaluation protocols: fine-grained style discrimination and interpretable aesthetic reasoning. Extensive evaluations reveal a persistent gap between calligraphy-related knowledge and visually grounded style perception: state-of-the-art LVLMs show non-trivial performance but remain sensitive to script-level, textual, and source-specific cues, and often struggle to ground aesthetic judgments in fine-grained brushwork evidence. Ultimately, the HCSU benchmark exposes fundamental limitations in current multimodal architectures, aiming to inspire the evolution of expert-level visual reasoning for cultural heritage preservation. The dataset is available at https://huggingface.co/datasets/Tongji209/HCSU.

Problem

• fine-grained calligraphy style understanding
• modal mixture
• flattened labels
• cultural heritage preservation
• aesthetic reasoning

Innovation

• fine-grained style understanding
• historical calligraphy
• modal decoupling
• hierarchical aesthetic descriptions
• multimodal benchmark