Attempts at machine sign language recognition have begun to appear in the literature over the past five years. However, these systems have generally concentrated on isolated signs and small training and test sets. Tamura and Kawasaki demonstrate an early image processing system which recognizes 20 Japanese signs based on matching cheremes [20]. Charayaphan and Marble [2] demonstrate a feature set that distinguishes between the 31 isolated ASL signs in their training set (which also acts as the test set). More recently, Cui and Weng [3] have shown an image-based system with 96% accuracy on 28 isolated gestures.
Takahashi and Kishino in [19] discuss a user dependent
Dataglove-based system that recognizes 34 of the 46 Japanese kana
alphabet gestures using a joint angle and hand orientation coding
technique. The test user makes each of the 46 gestures 10 times to
provide data for principle component and cluster analysis. A separate
test set is created from five iterations of the alphabet by the user,
with each gesture well separated in time. Murakami and Taguchi
[11] describe a similar Dataglove system using recurrent
neural networks. However, in this experiment a 42 static-pose finger
alphabet is used, and the system achieves up to 98% recognition for
trainers of the system and 77% for users not in the training set.
This study also demonstrates a separate 10 word gesture lexicon with
user dependent accuracies up to 96% in constrained situations.