Gesture Recognition for American Sign Language with Polygon Approximation

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.