Human Sign Language Recognition System

Human Sign Language Recognition (HSLR) systems aim to bridge communication gaps between the deaf and hearing communities by accurately interpreting sign language gestures into written or spoken language. Such systems involve advanced computer vision, machine learning, and natural language processing techniques to capture, process, and recognize sign language gestures from human users in real time. The development of HSLR systems presents unique challenges, such as differentiating similar gestures, recognizing subtle finger and hand movements, and managing variations in sign language across regions. This paper proposes a comprehensive approach to HSLR by integrating deep learning techniques, including convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) for sequence processing. The proposed HSLR system could significantly impact accessibility for the deaf community, enhancing inclusivity in various settings such as education, customer service, and public communications