Name
Title | ||
|---|---|---|
A reduced classifier ensemble approach to human gesture classification for robotic Chinese handwriting |
Abstract | ||
|---|---|---|
The paper presents an approach to applying a classifier ensemble to identify human body gestures, so as to control a robot to write Chinese characters. Robotic handwriting ability requires complicated robotic control algorithms. In particular, the Chinese handwriting needs to consider the relative positions of a character's strokes. This approach derives the font information from human gestures by using a motion sensing input device. Five elementary strokes are used to form Chinese characters, and each elementary stroke is assigned to a type of human gestures. Then, a classifier ensemble is applied to identify each gesture so as to recognize the characters that gestured by the human demonstrator. The classier ensemble's size is reduced by feature selection techniques and harmony search algorithm, thereby achieving higher accuracy and smaller ensemble size. The inverse kinematics algorithm converts each stroke's trajectory to the robot's motor values that are executed by a robotic arm to draw the entire character. Experimental analysis shows that the proposed approach can allow a human to naturally and conveniently control the robot in order to write many Chinese characters. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/FUZZ-IEEE.2014.6891656 | FUZZ-IEEE |
Keywords | Field | DocType |
reduced classifier ensemble approach,motion sensing input device,harmony search algorithm,robotic control algorithms,font information,robotic arm,robotic chinese handwriting,image classification,inverse kinematics algorithm,manipulator kinematics,gesture recognition,feature selection,chinese characters,human gesture classification,handwriting recognition,feature selection techniques,character sets,robot vision,trajectory,writing | Character recognition,Handwriting,Intelligent character recognition,Computer science,Speech recognition,Artificial intelligence,Fuzzy control system,Classifier (linguistics),Robot,Machine learning,Beijing,Gesture classification | Conference |
ISSN | Citations | PageRank |
1544-5615 | 4 | 0.43 |
References | Authors | |
21 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Chao | 1 | 90 | 17.24 |
| Yan Lindsay Sun | 2 | 75 | 10.41 |
| Zhengshuai Wang | 3 | 18 | 1.72 |
| Gang Yao | 4 | 276 | 21.08 |
| Zuyuan Zhu | 5 | 4 | 0.43 |
| Changle Zhou | 6 | 233 | 50.24 |
| Qinggang Meng | 7 | 273 | 23.54 |
| Min Jiang | 8 | 106 | 14.77 |