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AvishakeAdhikary / repository
Realtime-Sign-Language-Detection-Using-LSTM-Model
Realtime Sign Language Detection: Deep learning model for accurate, real-time recognition of sign language gestures using Python and TensorFlow.
#artificial-intelligence#computer-vision#dataanalysis#datavisualization#deep-learning#gesture-recognition
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README preview
Realtime Sign Language Detection Using LSTM Model
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
Table of Contents
About the Project
This section provides an overview of the Realtime Sign Language Detection Using LSTM Model project. It describes the project's purpose, which is to develop a system that can accurately detect and interpret sign language gestures in real time. It also highlights the use of LSTM (Long Short-Term Memory) models for this task and emphasizes the project's significance in improving communication accessibility for the deaf and hard of hearing community.
Demo
This section showcases a demonstration of the Realtime Sign Language Detection Using LSTM Model project.