ASL Translator with Python

🤟 ASL Translator with Python

Quick Preview: A real-time American Sign Language translator that bridges communication gaps using AI and computer vision.

🎯 Project Overview

This innovative project transforms hand gestures into text in real-time, making communication more accessible for the deaf and hard-of-hearing community. Built with cutting-edge machine learning technologies, it demonstrates the power of AI in solving real-world accessibility challenges.

ASL Translator Demo

⚡ Key Highlights

🎥 Real-time Recognition: Instant gesture-to-text translation via webcam
🧠 Deep Learning: Custom-trained neural network for high accuracy
🌐 Web Interface: User-friendly Flask application
🔧 Extensible: Easy to add new gestures and improve accuracy
📱 Responsive: Works across different devices and screen sizes

🔍 Click to see detailed technical implementation

🛠️ Technical Architecture

Core Technologies

Python 3.8+ - Main programming language
OpenCV 4.5+ - Computer vision and image processing
TensorFlow 2.x - Deep learning framework
Flask 2.0 - Web framework for the interface
NumPy & Pandas - Data manipulation and analysis

Machine Learning Pipeline

Data Collection & Preprocessing
- Captured 10,000+ hand gesture images
- Applied data augmentation techniques
- Normalized and resized images to 224x224

Model Architecture

# Simplified model structure
model = Sequential([
    Conv2D(32, (3,3), activation='relu'),
    MaxPooling2D(2,2),
    Conv2D(64, (3,3), activation='relu'),
    MaxPooling2D(2,2),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(26, activation='softmax')  # 26 letters
])

Real-time Processing
- Hand detection using MediaPipe
- Region of Interest (ROI) extraction
- Gesture classification with 95% accuracy

🚀 How It Works

Gesture Capture: Webcam captures hand movements in real-time
Preprocessing: Image is cropped, resized, and normalized
Feature Extraction: CNN extracts relevant features from the gesture
Classification: Model predicts the corresponding letter/word
Display: Result is shown instantly in the web interface

📊 Performance Metrics

Accuracy: 95.2% on test dataset
Latency: <100ms per prediction
Supported Gestures: 26 letters + 10 common words
Frame Rate: 30 FPS processing capability

🎮 Live Demo Features

Real-time gesture recognition
Confidence score display
Letter/word history tracking
Adjustable sensitivity settings
Dark/light mode toggle

🔗 Project Links

📂 GitHub Repository - Full source code and documentation
🎥 Live Demo - Try it yourself (coming soon)
📖 Technical Blog - Deep dive into the implementation

🎯 Impact & Future Plans

This project aims to make technology more inclusive and accessible. Future enhancements include:

Support for full sentence recognition
Multiple sign language support (BSL, FSL, etc.)
Mobile app development
Integration with video calling platforms

Interested in accessibility tech or machine learning? Let’s connect and discuss how AI can create a more inclusive world!