Abstract
People who are deaf or dumb in Arab communities face several challenges. The most important challenge is to communicate with people. In this study, a new approach for identifying the alphabet in the Iraqi Sign Language (IrSL) is proposed, which makes use of a suggested deep neural network called the Deep Recurrent Alphabet Sign Language (DRASL). It utilizes the Long Short-Term Memory (LSTM) technique for classifying the outputs and recognizing the alphabet in the SL. The dataset is constructed with the use of a glove that is coupled to flex sensors on each finger; each sensor gives a variable value based on the curvature ratio of the fingers. The sensors were connected to an Arduino which was then linked to a computer to transfer the data we collected. The data were divided into three groups, which had 29 different movements. All of these groups had a remarkably high accuracy equal to 100%.deep learning
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