Video-based signer-independent Arabic sign language recognition using hidden Markov models
Sign language in Arab World has been recently recognized and documented. There have been no serious attempts to develop a recognition system that can be used as a communication means between hearing-impaired and other people. This paper introduces the first automatic Arabic sign language (ArSL) recognition system based on hidden Markov models (HMMs). A large set of samples has been used to recognize 30 isolated words from the Standard Arabic sign language. The system operates in different modes including offline, online, signer-dependent, and signer-independent modes. Experimental results on using real ArSL data collected from deaf people demonstrate that the proposed system has high recognition rate for all modes. For signer-dependent case, the system obtains a word recognition rate of 98.13%, 96.74%, and 93.8%, on the training data in offline mode, on the test data in offline mode, and on the test data in online mode respectively. On the other hand, for signer-independent case the system obtains a word recognition rate of 94.2% and 90.6% for offline and online modes respectively. The system does not rely on the use of data gloves or other means as input devices, and it allows the deaf signers to perform gestures freely and naturally.
arabic sign language (arsl) hmm deaf people hand gestures feature extraction dct