Effective communication is essential for all individuals to live a fulfilling life, but it can be particularly challenging for those who are deaf and mute. While sign language interpreters are available to help bridge this communication gap, the high cost and scarcity of qualified interpreters make it difficult for deaf and mute individuals to rely on them for everyday interactions. To address this issue, we present a real-time sign language interpretation model that converts signs into text that is easily understandable by anyone. Our approach involves analyzing the overall body movement of sign language users and using the Arabic Sign Language (ArSL) dataset requirements to train a hybrid deep learning model with a convolutional long short-term memory (LSTM) network for gesture classification. The results of our model are promising, with an average classification accuracy of 100% on the three proposed signs from the complete ArSL word dataset. Our proposed system has the potential to significantly improve the quality of life for deaf and mute individuals by removing communication barriers and increasing accessibility in everyday situations. By providing a low-cost, reliable, and efficient solution for sign language interpretation, we aim to enhance communication between deaf and mute individuals and the hearing population, fostering greater inclusivity and understanding in society.
Alhumaid et al. (Fri,) studied this question.