We have established a framework for sign recognition that relies on combining 3D modeling of start and end handshapes as distributions based on a few initial and final frames (enhanced by statistical information about their linguistic dependencies) and of 3D movement patterns of the hands, arms, and upper body during sign production.
we have developed a statistical approach that combines distributions of the initial and final hand shapes and pose coupled with the spatiotemporal patterning of the arm and upper torso.