Sign-Language-Detection

Real-Time Sign Language Detection

This project implements a real-time sign language detection system using a Convolutional Neural Network (CNN) and MediaPipe for hand landmark detection. The system captures live video input, processes hand gestures, and classifies them into corresponding sign language alphabets.

Project Structure:

• CNNModel.py: Defines the Convolutional Neural Network (CNN) architecture used for classifying hand gestures.
• handLandMarks.py: Handles the detection of hand landmarks using MediaPipe and processes them for use by the CNN model.
• mediapipeHandDetection.py: Integrates MediaPipe to perform real-time hand detection through the webcam.
• realTime.py: The main script that ties everything together, using the CNN model and MediaPipe for real-time sign language detection.
• training.py: Script used for training the CNN model on a dataset of hand gestures.
• testCNN.py: Script for testing the performance of the trained CNN model on a test dataset.
• CNN_model_alphabet_SIBI.pth: Pre-trained CNN model weights used for classification.

How to Run the Project:

1. Install Dependencies

Make sure you have Python installed on your system. You can install the required Python packages using pip:

pip install -r requirements.txt

If you don't have a requirements.txt file, you can manually install the necessary packages:

pip install opencv-python mediapipe torch numpy pandas

2. Running Real-Time Detection

To start the real-time sign language detection, run the following command:

python realTime.py

This will activate your webcam and start detecting and classifying hand gestures in real-time.

3. Training the Model (Optional)

If you want to train the CNN model from scratch, you can run:

python training.py

This script will use a dataset of hand gestures to train the model.

4. Testing the Model (Optional)

To test the performance of the trained CNN model on a test dataset, you can run:

python testCNN.py

How It Works:

1. Hand Landmark Detection:

   • The system uses MediaPipe to detect and track hand landmarks in real-time from the webcam feed.

2. Feature Extraction:

   • The detected hand landmarks are processed and used as input features for the CNN model.

3. Gesture Classification:

   • The CNN model classifies the input features into one of the predefined sign language alphabets (A-Z).

4. Real-Time Feedback:

   • The classified gesture is displayed in real-time, providing immediate feedback to the user.

Requirements:

• Python 3.x
• OpenCV
• MediaPipe
• PyTorch
• Pandas

Contributing:

Contributions are welcome! If you have any ideas, suggestions, or improvements, feel free to open an issue or submit a pull request.

Contact:

For any questions or suggestions, please feel free to contact me at [[email protected]].