Sign Language Gesture Recognition

This project is aimed at developing a system to help convert sign language gestures into text and voice, as well as converting text/voice back into sign language. It includes tools for collecting gesture data, processing it, and using machine learning for recognition and translation.

Features

Data Collection: Capture images of hand signs using a webcam. The images are stored in a structured format for training machine learning models. Each image is labeled and stored with its corresponding person ID and image count for easy tracking. Sign to Text/Voice: Recognize sign language gestures from images and convert them into text and speech using trained machine learning models. Text/Voice to Sign: Convert text or spoken language into sign language gestures by displaying the corresponding sign images.

Project Structure

/dataset
   /images              # Directory to store captured images
   /csv.csv             # CSV file to store image metadata (file name, label, and person ID)
README.md               # Project overview and setup instructions
image_capture.py        # Script to collect gesture data using a webcam

How It Works

1. Data Collection: Use the image_capture.py script to collect images of hand gestures for various labels (e.g., numbers, alphabets). Each image is automatically named and stored along with its label and person ID in the csv.csv file.
2. Model Training: Use the captured images to train a machine learning model for recognizing sign language gestures.
3. Recognition: The system can convert recognized gestures into text and voice.
4. Translation: The system also supports translating text or voice into sign language gestures by displaying corresponding images.