cs.HCMar 24, 2026

Shu Dao: A Calligraphy Score Framework Linking Calligraphy, Music, and Performance

Authors: Lican Huang

Organizations: Hangzhou Domain Zones Technology Co., Ltd., Hangzhou, China.

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Abstract

This paper introduces Calligraphy Writing Score Representation (CWSR) and proposes Shu Dao as a framework that interprets East Asian calligraphy as a performative art rather than a static visual artifact. Inspired by traditions such as Japanese Shodō and embodied cultural practices such as Chadao , the framework models calligraphy as a structured performance analogous to musical notation. Instead of representing characters as fixed images, the proposed approach encodes each brush stroke as an ordered and executable action, forming a calligraphy score. Characters are organized within a structured spatial grid, and strokes are annotated with attributes including stroke type, execution order, spatial coordinates, trajectory, compositional role, and dynamic properties such as brush pressure and pacing. This representation captures temporal and expressive aspects of calligraphic writing that are typically absent from image-based representations. The paper makes three main contributions. First, it introduces CWSR as a structured notation system for representing calligraphy across multiple levels, including strokes, character structures, and compositional organization (e.g., layout and zhangfa), together with their rhythmic and performative dynamics. Second, it conceptualizes Shu Dao as a score-mediated framework that models calligraphy as structured performance. Third, it establishes a computational foundation for the analysis, visualization, and executable generation of calligraphic works by AI-based calligraphic agents. Together, these contributions bridge calligraphy, musical notation, and performative cultural practices, supporting human--AI co-creation in computational calligraphy and digital humanities research.

Figures & tables

Figure 1: CWSR calligraphy score pipeline. A score template is instantiated as a machine-readable Score JSON. From this representation, two parallel interpretation paths emerge. Human artists interpret the symbolic score to produce diverse calligraphic performances, while different AI artists (models or algorithms) generate executable score variants that lead to computational calligraphy generation.
Figure 2: Example calligraphy rendering of “永和” and “九年” generated from one AI agent’s executable Score JSON . The figure also shows the experiment directory on the left, containing the three executable Score JSON files and the three corresponding calligraphy renderings produced by independent AI agents , demonstrating multiple stylistic outputs from the same Score JSON.
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