Sign Language Recognition: an Application of the Theory of Size Functions

This paper discusses the use of certain integer valued functions of two real variables, named size functions, for shape representation and recognition. The recognition of the signing alphabet is described as a study case. A number of size functions are computed from the edge map of the viewed sign and a feature vector based on the obtained size functions is formed. A training set of feature vectors built from real images and the ^-nearest-neighbor rule are employed for the classification of unpreviously seen signs. The proposed system performs recognition at about 2Hz with feature vectors of small dimension. The reported experiments indicate that size functions can be effectively used for the recognition of nonrigid shapes.