1. A static hand gesture classification system for american sign language(asl) fingerspelling and digits
Authors : Sunanda Biradar, Ashwini Tuppad
Pages : 695-702
DOI : http://dx.doi.org/10.21172/1.71.099
Keywords : Sign languageStatic hand gesture recognitionCentral moments Abstract :Sign language recognition involves hand gesture classification. A method for static hand gesture classification has been proposed for American Sign Language(ASL) fingerspelling and digits. The system uses skin color based segmentation requiring little post processing. The averages of central moments of order 2 to 9 have been extracted as the features that characterize the hand gestures. The neural network classifier has been employed for recognition and it produced decent classification results of 73.68% with a small feature vector size containing 8 features.
Citing this Journal Article :Sunanda Biradar, Ashwini Tuppad, "A static hand gesture classification system for american sign language(asl) fingerspelling and digits", Volume 7 Issue 1 - May 2016, 695-702
Click here to Submit Copyright Takedown Notice for this article.