Abstract
For those who are deaf or hard of hearing, sign language continues to be the preferred form of communication. As technology has advanced, systems that can automatically distinguish between spoken language and vision-based sign language have been created. This paper examines and identifies Arabic alphabet sign language (ArSLR). The Convolutional Neural Network (CNN) model is used by the system to visually recognize motions from the input sequence of hand photographs. We employed two datasets: the isolated words dataset for dynamic gestures given across multiple frames and the alphabet dataset for static gestures presented throughout a single frame. The suggested systems combine model training, feature extraction, and prediction. To boost performance, we concentrated on hyperparameter validation. The system’s accuracy has exceeded 98% after testing and comparing various metrics, which is comparative to other works utilizing the similar dataset.
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Bouhanou, I., Aboutabit, N. (2023). Arabic Sign Language Analysis and Recognition. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_16
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DOI: https://doi.org/10.1007/978-3-031-29313-9_16
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