Visual Sign Language Interpretation Using Spatial Temporal Neural Processing

An approach to recognizing human hand gestures from a monocular temporal sequence of color images is presented in this paper. The focus is on the representation and recognition of hand movements that are used in single handed (right hand is the dominant hand) American sign language (ASL). This work has been done with the help of markov chains while we have carried it out with STANN. The first level of decomposition is in terms of three sets of primitives, hand shape, location and movement. Further levels of decomposition involve the lexical and sentence levels and are part of future work. The approach has been trained and tested with an overall recognition of 100%