GANCCRobot: Generative adversarial nets based chinese calligraphy robot

Ruiqi Wu, Changle Zhou, Fei Chao*, Longzhi Yang, Chih Min Lin, Changjing Shang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
22 Downloads (Pure)


Robotic calligraphy, as a typical application of robot movement planning, is of great significance for the inheritance and education of calligraphy culture. The existing implementations of such robots often suffer from its limited ability for font generation and evaluation, leading to poor writing style diversity and writing quality. This paper proposes a calligraphic robotic framework based on the generative adversarial nets (GAN) to address such limitation. The robot implemented using such framework is able to learn to write fundamental Chinese character strokes with rich diversities and good quality that is close to the human level, without the requirement of specifically designed evaluation functions thanks to the employment of the revised GAN. In particular, the type information of the stroke is introduced as condition information, and the latent codes are applied to maximize the style quality of the generated strokes. Experimental results demonstrate that the proposed model enables a calligraphic robot to successfully write fundamental Chinese strokes based on a given type and style, with overall good quality. Although the proposed model was evaluated in this report using calligraphy writing, the underpinning research is readily applicable to many other applications, such as robotic graffiti and character style conversion.

Original languageEnglish
Pages (from-to)474-490
Number of pages17
JournalInformation Sciences
Early online date30 Dec 2019
Publication statusPublished - Apr 2020


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