Volume 7 Issue 1 - May 2016

  • 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", https://www.ijltet.org/journal_details.php?id=901&j_id=3118, Volume 7 Issue 1 - May 2016, 695-702, #ijltetorg