The Arabic Sign Language Recognition Research aims to develop a real-time system that accurately recognizes Arabic Sign Language (ArSL) gestures and translates them into both text and speech. This Research leverages the KArSL-502 Dataset, which contains 502 unique Arabic signs, to train a deep learning model using Bidirectional Long Short-Term Memory (LSTM) networks. LSTMs are particularly suited for capturing the temporal patterns of sign language gestures, which often involve sequential hand movements. The system integrates advanced image processing techniques such as Mediapipe and Handtrack for detecting and extracting hand landmarks, followed by key point adjustments to ensure consistency across gestures. The model's performance was evaluated using categorical accuracy, achieving a training accuracy of 98% and a testing accuracy of 96%, demonstrating the model’s ability to generalize well to unseen data. Additionally, the proposed system includes text-to-speech functionality via Google Text-to-Speech (Gtts), enabling real-time vocalization of recognized gestures, thus facilitating communication between sign language users and non-sign language speakers. The system’s high accuracy and fast processing time (measured in milliseconds per gesture) make it suitable for real-time applications.
Sorial et al. (Thu,) studied this question.