Automatic stroke generation for style-oriented robotic Chinese calligraphy

Gan Lin, Zhihua Guo, Fei Chao*, Longzhi Yang, Xiang Chang, Chih Min Lin, Changle Zhou, V. Vijayakumar, Changjing Shang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Intelligent robots, as an important type of Cyber–Physical systems, have promising potential to take the central stage in the development of the next-generation of efficient smart systems. Robotic calligraphy is such an attempt, and the current research focuses on the control algorithms of the robotic arms, which usually suffers from significant human inputs and limited writing styles. This paper presents an autonomous robotic writing system for Chinese calligraphy empowered by the proposed automatic stroke matching and generation mechanisms. Thanks to these mechanisms, the robot is able to effectively learn to write any Chinese characters in a style that is sampled by a small amount of handwritten Chinese characters with a certain target writing style. This is achieved by firstly disassembling each given Chinese character into individual strokes using the proposed character disassemble method; then, the writing style of the dissembled strokes is learned by a stroke generation module, which is built upon a generative adversarial learning model. From this, the robot can apply the learned writing style to any Chinese character from a given database, by dissembling the character and then generating the stroke trajectories based on the learned writing style. The experiments confirm the effectiveness of the proposed system in learning writing a certain style of characters based on a small style dataset, as evidenced by the high similarity between the robotic writing results and the handwritten ones according to the Fréchet Inception Distance.

Original languageEnglish
Pages (from-to)20-30
Number of pages11
JournalFuture Generation Computer Systems
Early online date22 Jan 2021
Publication statusPublished - 1 Jun 2021


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