Abstract:

Sign language detection has gained significant attention in recent years due to its potential to bridge the communication gap between deaf and hearing communities. This paper presents a comprehensive review of sign language detection techniques using machine learning. We first introduce the challenges of sign language recognition, including variations in sign gestures, lighting conditions, and occlusion. Then, we survey the stateof- the-art approaches in sign language recognition, including feature extraction, feature selection, and classification. We compare and analyze various machine learning algorithms, including deep learning models, support vector machines, and decision trees, in terms of their performance, complexity, and scalability. We also discuss datasets commonly used in sign language recognition, such as American Sign Language (ASL) and British Sign Language (BSL), and the evaluation metrics used to assess the performance of the models. Finally, we discuss future directions in sign language detection, including the integration of multimodal data and the use of explainable AI for more transparent and interpretable models.

Description:

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