TY - JOUR
T1 - Automatic stroke generation for style-oriented robotic Chinese calligraphy
AU - Lin, Gan
AU - Guo, Zhihua
AU - Chao, Fei
AU - Yang, Longzhi
AU - Chang, Xiang
AU - Lin, Chih Min
AU - Zhou, Changle
AU - Vijayakumar, V.
AU - Shang, Changjing
N1 - Funding Information:
The authors are very grateful to the anonymous reviewers for their constructive comments which have helped significantly in revising this work. This work was supported by the National Natural Science Foundation of China (No. 61673322, 61673326, and 91746103), the Key Project of National Key R & D Project, China (No. 2017YFC1703303), the Fundamental Research Funds for the Central Universities, China (No. 20720190142), and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement (No. 663830).
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/6/30
Y1 - 2021/6/30
N2 - 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.
AB - 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.
KW - Deep learning
KW - Intelligent robots
KW - Robotic calligraphy
KW - Robotic motion planning
KW - Smart cyber–physical systems
UR - http://www.scopus.com/inward/record.url?scp=85100485206&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85100485206
SN - 0167-739X
VL - 119
SP - 20
EP - 30
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -