Automatic generation of HMM topology for sign language recognition

Sign language is used for communicating to people with hearing difficulties. Recognition of a sign language image sequence is challenging because of the variety of hand shapes and hand motions. We propose a method to automatically construct a transitional structure(topology) of a Hidden Markov Model(HMM) for recognizing sign language words. Unlike conventional HMM, the constructed topology has branches and junctions in order to represent a flexible structure. The proposed method consists of segmentation of a motion, and construction of the topology from segments. The topology is constructed from an initial topology by modifying it. With experiments, we show the effectiveness of the proposed method.