Mendeley
TY  _  JOUR
ID  -  1394090411589529
TI  -  A Simple and Effective Scheme for Hand Gesture Recognition in Finger Spelling of Farsi Alphabet
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Barzegar Sakhvidi  M. J.
AU  -  Sharafat A. R.
AD  -  دانشگاه تربیت مدرس
AD  -  دانشگاه تربیت مدرس
Y1  -  2009
PY  -  2009
VL  -  4
IS  -  
SP  -  323
EP  -  328
KW  -  Farsi finger spelling
nearest neighbor
neural networks
sign language recognition
skin detection
DO  -  
N2  -  In recent years, automated recognition of gestures in the finger spelling paradigm has become an active research area. Gesture is a combination of hand postures, hand movements, and face gestures; and finger spelling is a way of presenting alphabets of a word that does not exist in the sign language dictionary. In this paper, we present a scheme for hand gesture recognition in finger spelling of Farsi alphabets, where a different shape for hand and fingers denote a different letter in the alphabet. Our scheme has five stages, namely, visual data gathering, preprocessing of the image, detection and extraction of hand’s features, feature reduction and consolidation, and finally, hand gesture recognition. For the last stage (hand gesture recognition), we employ three techniques, namely, the nearest neighbor using the Euclidian distance, the nearest neighbor using the normalized Euclidian distance, and neural networks. For reducing the feature space, we use the discrete cosine transform (DCT), which yields better results as compared to the discrete Fourier transform and Fourier coefficients. We achieved 99.1% correct recognition using neural networks, which is superior to existing schemes.
UR  - rimag.ir/en/Article/27964
L1  - rimag.ir/en/Article/Download/27964
ER  -  
