TY - JOUR
T1 - Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots
AU - Chao, Fei
AU - Huang, Yuxuan
AU - Lin, Chih-Min
AU - Yang, Longzhi
AU - Hu, Huosheng
AU - Zhou, Changle
N1 - Funding Information:
Manuscript received November 14, 2016; revised August 3, 2017, October 24, 2017, and June 27, 2018; accepted October 20, 2018. Date of publication December 10, 2018; date of current version January 15, 2019. This work was supported by the National Natural Science Foundation of China (No. 61673322, 61673326, and 91746103), by the Fundamental Research Funds for the Central Universities (No. 20720160126), by the Natural Science Foundation of Fujian Province of China (No. 2017J01128 and 2017J01129), and by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant 663830. This paper was recommended by Associate Editor R. Plamondon. (Corresponding author: Fei Chao.) F. Chao, Y. Huang, and C. Zhou are with the Cognitive Science Department, School of Information Science and Engineering, Xiamen University, Xiamen 361005, China (e-mail:, [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2013 IEEE.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Conventional Chinese calligraphy robots often suffer from the limited sizes of predefined font databases, which prevent the robots from writing new characters. This paper presents a robotic handwriting system to address such limitations, which extracts Chinese characters from textbooks and uses a robot's manipulator to write the characters in a different style. The key technologies of the proposed approach include the following: 1) automatically decomposing Chinese characters into strokes using Harris corner detection technology and 2) matching the decomposed strokes to robotic writing trajectories learned from human gestures. Briefly, the system first decomposes a given Chinese character into a set of strokes and obtains the stroke trajectory writing ability by following the gestures performed by a human demonstrator. Then, it applies a stroke classification method that recognizes the decomposed strokes as robotic writing trajectories. Finally, the robot arm is driven to follow the trajectories and thus write the Chinese character. Seven common Chinese characters have been used in an experiment for system validation and evaluation. The experimental results demonstrate the power of the proposed system, given that the robot successfully wrote all the testing characters in the given Chinese calligraphic style
AB - Conventional Chinese calligraphy robots often suffer from the limited sizes of predefined font databases, which prevent the robots from writing new characters. This paper presents a robotic handwriting system to address such limitations, which extracts Chinese characters from textbooks and uses a robot's manipulator to write the characters in a different style. The key technologies of the proposed approach include the following: 1) automatically decomposing Chinese characters into strokes using Harris corner detection technology and 2) matching the decomposed strokes to robotic writing trajectories learned from human gestures. Briefly, the system first decomposes a given Chinese character into a set of strokes and obtains the stroke trajectory writing ability by following the gestures performed by a human demonstrator. Then, it applies a stroke classification method that recognizes the decomposed strokes as robotic writing trajectories. Finally, the robot arm is driven to follow the trajectories and thus write the Chinese character. Seven common Chinese characters have been used in an experiment for system validation and evaluation. The experimental results demonstrate the power of the proposed system, given that the robot successfully wrote all the testing characters in the given Chinese calligraphic style
KW - Chinese character decomposition
KW - human-robot interactions
KW - robotic calligraphy
UR - http://www.scopus.com/inward/record.url?scp=85058177811&partnerID=8YFLogxK
U2 - 10.1109/THMS.2018.2882485
DO - 10.1109/THMS.2018.2882485
M3 - Article
SN - 2168-2305
VL - 49
SP - 47
EP - 58
JO - IEEE Transactions on Human-Machine Systems
JF - IEEE Transactions on Human-Machine Systems
IS - 1
M1 - 8570842
ER -