Sign Language Recognition Using Neural Network

This abstract explores the application of deep learning techniques in the interpretation of sign language, , addressing the imperative need for effective communication solutions for the deaf and hard of-hearing communities. Leveraging the advancements in deep learning, particularly convolutional neural networks (CNNs), our study delves into the development and evaluation of a robust sign language recognition system. The proposed model capitalizes on the spatial and temporal intricacies of sign language gestures, offering enhanced accuracy and efficiency. We investigate the dataset curation, model architecture, and training methodologies, showcasing the adaptability of deep learning in capturing the nuanced expressions inherent in sign language. Our experimental results demonstrate promising outcomes, indicating the potential for real-world deployment of sign language interpretation systems. This research contributes to the broader domain of assistive technologies, fostering inclusivity by providing a more accessible and seamless means of communication for individuals with hearing impairments.