Abstract
Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained.
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