Reading the Signs: A Video Based Sign Dictionary
This article presents a dictionary for Sign Language using visual sign recognition based on linguistic subcomponents. We demonstrate a system where the user makes a query, receiving in response a ranked selection of similar results. The approach uses concepts from linguistics to provide sign sub-unit features and classifiers based on motion, sign-location and handshape. These sub-units are combined using Markov Models for sign level recognition. Results are shown for a video dataset of 984 isolated signs performed by a native signer. Recognition rates reach 71.4\% for the first candidate and 85.9\% for retrieval within the top 10 ranked signs.