Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Chao, Fei, Huang, Yuxuan, Lin, Chih-Min, Yang, Longzhi, Hu, Huosheng and Zhou, Changle (2019) Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots. IEEE Transactions on Human-Machine Systems, 49 (1). pp. 47-58. ISSN 2168-2291

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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.

Item Type: Article
Uncontrolled Keywords: Robotic calligraphy, human-robot interactions, Chinese character decomposition
Subjects: G400 Computer Science
H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 11 Dec 2018 11:08
Last Modified: 01 Aug 2021 07:31

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