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18th International Conference on Pattern Recognition (ICPR'06) Volume 1
A Novel Vision based Finger-writing Character Recognition System
Hong Kong
August 20-August 24
ISBN: 0-7695-2521-0
ASCII Text
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Lianwen Jin, Duanduan Yang, Li-Xin Zhen, Jian-Cheng Huang, "A Novel Vision based Finger-writing Character Recognition System," Pattern Recognition, International Conference on, vol. 1, pp. 1104-1107, 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006.
 
 
BibTex
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@article{ 10.1109/ICPR.2006.145,
author = {Lianwen Jin and Duanduan Yang and Li-Xin Zhen and Jian-Cheng Huang},
title = {A Novel Vision based Finger-writing Character Recognition System},
journal ={Pattern Recognition, International Conference on},
volume = {1},
year = {2006},
issn = {1051-4651},
pages = {1104-1107},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.145},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - A Novel Vision based Finger-writing Character Recognition System
SN - 1051-4651
SP1104
EP1107
A1 - Lianwen Jin,
A1 - Duanduan Yang,
A1 - Li-Xin Zhen,
A1 - Jian-Cheng Huang,
PY - 2006
KW - null
VL - 1
JA - Pattern Recognition, International Conference on
ER -
 
Lianwen Jin, South China University of Technology,Guangzhou
Duanduan Yang, South China University of Technology,Guangzhou
Li-Xin Zhen, Motorola China Research Center, Shanghai, 210000, China
Jian-Cheng Huang, Motorola China Research Center, Shanghai, 210000, China
A new vision based finger writing character recognition system (FWCRS) is proposed in this paper. The FWCRS allows people to write characters virtually just using his finger-tip (we call this "fingerwriting"). The trajectories of the finger-tip are tracked and reconstructed as a kind of inkless character pattern and finally recognized by a classifier. In this paper, a simple but effective background model is built for the FWCRS to segment human finger from cluttered background. A robust fingertip detection algorithm based on feature matching is presented. The fingerwriting character is finally recognized by a DTW classifier. Experiments show that the FWCRS can recognize finger-writing uppercase & lowercase English characters with the accuracy of 95.6%, 98.5% respectively.
Citation:
Lianwen Jin, Duanduan Yang, Li-Xin Zhen, Jian-Cheng Huang, "A Novel Vision based Finger-writing Character Recognition System," icpr, vol. 1, pp.1104-1107, 18th International Conference on Pattern Recognition (ICPR'06) Volume 1, 2006
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