GANCCRobot: Generative adversarial nets based chinese calligraphy robot

Wu, Ruiqi, Zhou, Changle, Chao, Fei, Yang, Longzhi, Lin, Chih-Min and Shang, Changjing (2020) GANCCRobot: Generative adversarial nets based chinese calligraphy robot. Information Sciences, 516. pp. 474-490. ISSN 0020-0255

RuiqiIS_20191028_YL.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (2MB) | Preview
Official URL:


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.

Item Type: Article
Uncontrolled Keywords: Calligraphy robot, Generative adversarial nets, Motion planning
Subjects: G400 Computer Science
G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 29 Jun 2020 11:13
Last Modified: 31 Jul 2021 14:17

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics