AI Driven Deep Neural Network forGesture and Sign Language Recognition-A Survey Approach
Abstract
Sign language recognition plays a key role in bridging communication gaps between hearing-impaired individuals and society. This survey paper offers a comprehensive examination of AI-driven deep neural network (DNN) architectures developed for the purpose of gesture recognition and sign language identification. Human communication relies profoundly on non-verbal channels, and for individuals with hearing or speech impairments, sign language constitutes the central mode of expressing thought, need, and emotion. Bridging the communication divide between the Deaf community and the hearing world through intelligent automated systems represents one of the most socially meaningful directions in contemporary artificial intelligence research. This paper consolidates findings from a wide spectrum of studies spanning classical machine learning, convolutional neural networks, recurrent sequence models, attention-based transformers, graph convolutional networks, and multimodal fusion architectures.
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