Abstract:
Sign language, a gesture based nonverbal technique of communication, is used by a
significant number of populations throughout the world. Sign language recognition
emerges as one of the most challenging exercises when the person interpreting it lacks
the previous knowledge of signs. This paper proposes a method to recognize sign
language using computer vision. Five features from a binary image of a hand shape,
namely the area of the closed contour, the area of the convex hull, number of convexity
defects, maximum depth of the defects, and the sum of the depths of the defects are
extracted after preprocessing the image. In this study, for recognizing sign languages,
the recognition task was accomplished by employing two classification algorithms: knearest neighbor (k-NN) and Support Vector Machine (SVM) individually. Then the
system will decide the class of the gesture acquired from the result of a Logical AND
operation from both of the classifiers.
