Sign Language Detection Project
This project aims to detect and recognize sign language gestures using computer vision and machine learning techniques. It utilizes the MediaPipe library for hand detection and tracking, as well as a trained machine learning model to recognize gestures.
Requirements
- Python 3.10
- OpenCV
- MediaPipe
- TensorFlow/Keras
- NumPy
- pickle
- gTTS (Google Text-to-Speech) - for macOS users
Installation
- Clone the repository to your local machine:
git clone https://github.com/nrrpatel/SignLanguageDetection
- Navigate to the project directory:
cd sign-language-detection
- Install the required Python dependencies:
pip install -r requirements.txt
- Usage:
Ensure your webcam is connected and properly configured. Run the main script inference_classifier.py:
When a gesture is detected, the corresponding character or word will be displayed on the screen and announced (if supported by your system).
Supported Gestures The model currently supports recognition of the following gestures:
A-Z letters
Model Training
The machine learning model used for gesture recognition was trained on a dataset of hand gesture images. The training process involved preprocessing the images, extracting relevant features using MediaPipe, and training a convolutional neural network (CNN) using TensorFlow/Keras.
Credits This project was developed by Nikunj Patel and should not be recreated without the permission of the user.