Real-Time Hand Gesture Recognition and Sentence Generation for Deaf and Non-Verbal Communication

To bridge the communication gap between deaf people and non-sign language users, the research project Real-Time Hand Gesture Recognition and Sentence Generation for Deaf and Non-Verbal Communication was created. The proposed system makes use of computer vision technology for real-time hand gesture identification and natural language processing to produce coherent and understandable words. In order to recognize hand gestures in real-time, the study employs deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These models are trained on a dataset of hand gestures and are able to accurately classify different hand gestures in real-time. The system can recognize a wide range of hand motions, from simple ones like pointing and waving to complex ones involving several fingers. The system creates sentences that are consistent with the hand gesture's intended meaning once it has been identified. Natural language processing techniques are utilized to convert the recognized hand signal into understandable speech. Based on established rules and grammar suggestions, the program generates appropriate sentences while taking the context of the conversation into account. The proposed device may change communication for nonverbal and deaf individuals who have trouble expressing themselves. It gives these populations and the rest of society a quick, efficient way to communicate.