There is some work that has been done in areas similar to sign language recognition, such as Fels' ([FH93,Fel94]) and Pausch's ([PW91]) work. Also, there is some of work in progress, such as Vamplew's ([Vam]) and Dorner & Hagen's ([DH94]). Some work has also been done on recognising handshapes ([ITK92,TK91,DS93]).
The work that has been published in the specific area of recognition of individual signs is still limited. Murakami and Taguchi considered ten signs using a recurrent neural net and obtained accuracy of 96 per cent ([MT91]). Charayaphan and Marble considered 31 ASL symbols, but only sampled each sign once and simulated the variation and consistently got 27 out of the 31 correct, with the remaining four sometimes correct using a Hough transform. Starner considered 40 signs used in brief sentences and ([Sta95,SP95]) obtained accuracies of 91.3 per cent on raw signs and 99.2 per cent by using a very strict grammar for sentences.