The Realtime Sign Language Detection Using LSTM Model is a deep learning-based project that aims to recognize and interpret sign language gestures in real-time. It utilizes a Long Short-Term Memory (LSTM) neural network architecture to learn and classify sign language gestures captured from a video feed. The project provides a user-friendly interface where users can perform sign language gestures in front of a camera, and the system will instantly detect and interpret the gestures. This can be used as an assistive technology for individuals with hearing impairments to communicate effectively. Key features of the project include real-time gesture detection, high accuracy in recognition, and the ability to add and train new sign language gestures. The system is built using Python, TensorFlow, OpenCV, and Numpy, making it accessible and easy to customize. With the Realtime Sign Language Detection Using LSTM Model, we aim to bridge the communication gap and empower individuals with hearing impairments
This section showcases a demonstration of the Realtime Sign Language Detection Using LSTM Model project.
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The demo allows viewers to see how the system accurately interprets sign language gestures and provides real-time results.