Large vocabulary sign language recognition based on hierarchical decision trees

The major difficulty for large vocabulary sign language or gesture recognition lies in the huge search space due to a variety of recognized classes. How to reduce the recognition time without loss of accuracy is a challenge issue. In this paper, a hierarchical decision tree is first presented for large vocabulary sign language recognition based on the divide-and-conquer principle. As each sign feature has the different importance to gestures, the corresponding classifiers are proposed for the hierarchical decision to gesture attributes. One- or two- handed classifier with little computational cost is first used to eliminate many impossible candidates. The subsequent hand shape classifier is performed on the possible candidate space. SOFM/HMM classifier is employed to get the final results at the last non-leaf nodes that only include few candidates. Experimental results on a large vocabulary of 5113-signs show that the proposed method drastically reduces the recognition time by 11 times and also improves the recognition rate about 0.95% over single SOFM/HMM.