https://github.com/rahulkumar7189/american-sign-language-detection
Real-time American Sign Language detection system using CNN, OpenCV, MediaPipe, and PyTorch for computer vision-based sign language recognition
https://github.com/rahulkumar7189/american-sign-language-detection
ai american-sign-language asl cnn computer-vision deep-learning education kaggle-dataset machine-learning mediapipe opencv pytorch real-time-detection sign-language
Last synced: 2 months ago
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Real-time American Sign Language detection system using CNN, OpenCV, MediaPipe, and PyTorch for computer vision-based sign language recognition
- Host: GitHub
- URL: https://github.com/rahulkumar7189/american-sign-language-detection
- Owner: rahulkumar7189
- Created: 2025-10-15T10:47:56.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-20T17:26:02.000Z (8 months ago)
- Last Synced: 2025-10-27T13:02:19.981Z (8 months ago)
- Topics: ai, american-sign-language, asl, cnn, computer-vision, deep-learning, education, kaggle-dataset, machine-learning, mediapipe, opencv, pytorch, real-time-detection, sign-language
- Language: Python
- Homepage:
- Size: 3.99 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# American Sign Language Detection
## 📖 Project Overview
This project provides a comprehensive American Sign Language (ASL) detection system using computer vision and deep learning. The pipeline, training, and hand gesture recognition work accurately for all genders, including men, women, and non-binary individuals, ensuring inclusive and unbiased performance across diverse users.
## ✨ Features
- Real-time ASL gesture detection
- CNN-based deep learning model
- Hand landmark detection using MediaPipe
- Support for multiple ASL signs
- Training pipeline for custom datasets
- Gender-inclusive recognition optimized for all users
## 🚀 Usage
### Training the Model
```bash
python train.py
```
### Creating Custom Dataset
When collecting data using `create_dataset.py`, ensure diverse participant representation:
- Include hand gestures from male, female, and non-binary participants
- Capture various hand sizes, skin tones, and lighting conditions
- Maintain consistent gesture form across all demographics
```bash
python create_dataset.py
```
### Running Sign Detection
```bash
python sign_detector.py
```
### Evaluating the Model
```bash
python Evaluation.py
```
## 🧠 Model Architecture
The CNN model consists of:
- Convolutional layers for feature extraction
- Pooling layers for dimensionality reduction
- Fully connected layers for classification
- Dropout for regularization
## 📊 Dataset
The project supports various ASL datasets including:
- Custom datasets created using `create_dataset.py` (ensure gender-diverse data collection with male, female, and non-binary participants for robust model performance)
- Kaggle ASL datasets
- Hand landmark data from MediaPipe
**Note on Dataset Inclusivity**: For optimal performance across all users, training data should include hand gestures from participants of all genders, with particular attention to capturing male hand gestures alongside female and non-binary participants to prevent gender bias in recognition accuracy.
## 🎓 Applications
- **Education**: Teaching ASL to students
- **Accessibility**: Bridging communication gaps
- **Research**: Sign language recognition studies
- **Real-time Translation**: Converting ASL to text/speech
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## 📝 License
This project is open source and available under the MIT License.
## 🙏 Acknowledgments
- MediaPipe for hand landmark detection
- PyTorch community for deep learning framework
- Kaggle for ASL datasets
- OpenCV for computer vision tools
## 📧 Contact
For questions or suggestions, please open an issue in this repository.
---
**Made with ❤️ for the ASL community**