  In this paper, the task of recognizing signs made by hearing impaired people
at sentence level has been addressed. A novel method of extracting spatial
features to capture hand movements of a signer has been proposed. Frames of a
given video of a sign are preprocessed to extract face and hand components of a
signer. The local centroids of the extracted components along with the global
centroid are exploited to extract spatial features. The concept of interval
valued type symbolic data has been explored to capture variations in the same
sign made by the different signers at different instances of time. A suitable
symbolic similarity measure is studied to establish matching between test and
reference signs and a simple nearest neighbour classifier is used to recognize
an unknown sign as one among the known signs by specifying a desired level of
threshold. An extensive experimentation is conducted on a considerably large
database of signs created by us during the course of research work in order to
evaluate the performance of the proposed system
