{"id":24374097,"url":"https://github.com/monzerdev/real-time-sign-language-recognition","last_synced_at":"2026-05-15T21:35:45.734Z","repository":{"id":275487374,"uuid":"926217445","full_name":"MonzerDev/Real-Time-Sign-Language-Recognition","owner":"MonzerDev","description":"A real-time sign language recognition system utilizing MediaPipe and CNNs to classify static gestures (A-Z, 1-9). 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The system captures live video input, processes hand \ngestures, and classifies them into corresponding sign language alphabets or numbers.\n\n---\n\nProject Structure\n\n- CNNModel.py: Defines the Convolutional Neural Network (CNN) architecture used for classifying hand gestures.\n- training.py: Script used for training the CNN model on datasets of hand gestures or numbers.\n- testCNN.py: Script for testing the performance of the trained CNN model on a test dataset.\n- mediapipeHandDetection.py: Integrates MediaPipe to perform real-time hand detection and display landmarks through the webcam.\n- realTime.py: Main script that integrates the CNN model and MediaPipe for real-time hand gesture recognition and classification.\n- handLandMarks.py: Processes MediaPipe's hand landmarks for generating datasets suitable for training the CNN model.\n- numbers_testing_data.xlsx: Example processed dataset for testing numeric gesture recognition.\n- CNN_model_alphabet_SIBI.pth: Pre-trained CNN model weights for sign language alphabets classification.\n- CNN_model_number_SIBI.pth: Pre-trained CNN model weights for numeric gesture classification.\n\n---\n\nHow to Run the Project\n\n1. Install Dependencies\n\nEnsure Python is installed. Install the required Python packages using:\n\n\npip install opencv-python mediapipe torch numpy pandas\n\n2. Running Real-Time Recognition\n\nFor real-time sign language or numeric gesture recognition, run:\n\npython realTime.py\n\nThis will activate your webcam and start detecting and classifying hand gestures in real-time.\n\n3. Training the Model (Optional)\n\nTo train the CNN model from scratch using a dataset of hand gestures, run:\n\npython training.py\n\n4. Testing the Model (Optional)\n\nTo evaluate the trained CNN model's performance on a test dataset, run:\n\npython testCNN.py\n\n---\n\nHow It Works\n\n1. Hand Landmark Detection: \n   - MediaPipe detects and tracks hand landmarks in real-time from the webcam feed.\n\n2. Feature Extraction:\n   - Hand landmarks are processed and normalized to be used as input features for the CNN model.\n\n3. Gesture Classification:\n   - The CNN model classifies the input features into one of the predefined sign language alphabets (A-Z) or numeric gestures (1-9).\n\n4. Real-Time Feedback:\n   - The classified gesture is displayed in real-time, providing immediate feedback to the user.\n\n---\n\nRequirements\n\n- Python 3.x\n- OpenCV\n- MediaPipe\n- PyTorch\n- Pandas\n- NumPy\n\n---\n\nNotes\n\n- The system supports both alphabetic and numeric gestures based on the pre-trained model loaded \n  (CNN_model_alphabet_SIBI.pth or CNN_model_number_SIBI.pth).\n- Ensure the training and test datasets are preprocessed and structured correctly as required by the CNN model.\n\n---\n\nContributing\n\nContributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.\n\n---\n\nContact\n\nFor any questions or suggestions, feel free to contact me at (monzerkoukou@gmail.com).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmonzerdev%2Freal-time-sign-language-recognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmonzerdev%2Freal-time-sign-language-recognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmonzerdev%2Freal-time-sign-language-recognition/lists"}