Title :
Hand gesture recognition for Indian Sign Language
Author :
Ghotkar, Archana S. ; Khatal, Rucha ; Khupase, Sanjana ; Asati, Surbhi ; Hadap, Mithila
Author_Institution :
Dept. of Comput. Eng., Pune Inst. of Comput. Technol., Pune, India
Abstract :
In this paper, we introduce a hand gesture recognition system to recognize the alphabets of Indian Sign Language. In our proposed system there are 4 modules: real time hand tracking, hand segmentation, feature extraction and gesture recognition. Camshift method and Hue, Saturation, Intensity (HSV) color model are used for hand tracking and segmentation. For gesture recognition, Genetic Algorithm is used. We propose an easy-to-use and inexpensive approach to recognize single handed as well as double handed gestures accurately. This system can definitely help millions of deaf people to communicate with other normal people.
Keywords :
feature extraction; genetic algorithms; gesture recognition; image colour analysis; image segmentation; object tracking; Indian sign language alphabet; camshift method; double handed gesture; feature extraction; genetic algorithm; hand gesture recognition; hand segmentation; hue-saturation-intensity color model; real time hand tracking; single handed gesture; Feature extraction; Genetic algorithms; Gesture recognition; Handicapped aids; Hidden Markov models; Image color analysis; Image segmentation; Double handed Gestures; Feature Extraction; Gesture Recognition; Hand Tracking; Indian Sign Language; Segmentation; Single handed Gestures;
Conference_Titel :
Computer Communication and Informatics (ICCCI), 2012 International Conference on
Conference_Location :
Coimbatore
Print_ISBN :
978-1-4577-1580-8
DOI :
10.1109/ICCCI.2012.6158807
