Real-Time Gesture Based Sign Language Recognition System

Sign language is a vital mode of communication for the deaf and dumb community. This research presents a robust and real-time Gesture-Based Sign Language Detection System that leverages computer vision and deep learning techniques. The system is designed to recognize and interpret American Sign Language (ASL) gestures, enabling efficient communication between individuals who are proficient in ASL and those who are not. The core of the system utilizes Python, OpenCV (Open-Source Computer Vision Library), and MediaPipe Holistic for real-time hand and body pose estimation. By accurately tracking the movements of hands and key body parts, the system captures the nuances of sign language gestures. The captured data is then fed into a Long Short-Term Memory (LSTM) neural network, which excels in sequence modeling tasks. The LSTM model is trained on a comprehensive dataset of ASL gestures, encompassing a wide range of signs and expressions. Transfer learning techniques are also employed to fine-tune the model for improved performance in sign language recognition. The model's architecture allows it to learn the temporal dependencies and context inherent in sign language, making it capable of recognizing gestures within sentences or phrases. The system's evaluation demonstrates its effectiveness in real-world scenarios, achieving high accuracy and low latency in sign language recognition. It opens new avenues for accessible and inclusive communication, aiding both deaf and hearing individuals in bridging the communication gap. Future work may explore the integration of natural language processing (NLP) to facilitate two-way communication between sign and spoken language. In conclusion, this Gesture-Based Sign Language Detection System represents a significant step towards harnessing the power of computer vision and deep learning to make sign language more accessible and inclusive in various domains, including education, accessibility, and social interaction.