Publication Type : Conference Paper
Publisher : IEEE
Source : Proceedings - IEEE International Conference on Technology for Education, T4E 2011, Chennai, Tamil Nadu, p.241-245 (2011)
ISBN : 9780769545349
Keywords : American sign language, Approximation algorithms, ASL, chain code, Chain codes, Codes (symbols), Contour measurement, Data glove, Data-acquisition devices, Douglas, Existing systems, Feature vectors, Finger gestures, Freeman chain code, Gesture recognition, Image matching, Perfect matches, Polygon approximation, Substring
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2011
Abstract : We propose a novel method to recognize symbols of the American Sign Language alphabet (A-Z) that have static gestures. Many of the existing systems require the use of special data acquisition devices like data gloves which are expensive and difficult to handle. Some of the methods like finger tip detection do not recognize the alphabets which have closed fingers. We propose a method where the boundary of the gesture image is approximated into a polygon with Douglas - Peucker algorithm. Each edge of the polygon is assigned the difference Freeman Chain Code Direction. We use finger tips count along with difference chain code sequence as a feature vector. The matching is done by looking for either perfect match and in case there is no perfect match, substring matching is done. The method efficiently recognizes the open and closed finger gestures. © 2011 IEEE.
Cite this Research Publication :
M. Geetha, Menon, R., Jayan, S., James, R., and Janardhan, G. V. V., “Gesture recognition for American sign language with polygon approximation”, in Proceedings - IEEE International Conference on Technology for Education, T4E 2011, Chennai, Tamil Nadu, 2011, pp. 241-245