SignDecode-Sign-Language-Recognition-

SignDecode: Real-Time Sign Language Recognition System

Overview

SignDecode is a computer vision application that performs real-time American Sign Language (ASL) recognition using skeletal hand tracking and neural network classification. The system processes webcam input to detect hand landmarks and classify gestures into alphanumeric characters (A-Z, 0-9).

Key Technical Achievement: By using skeletal coordinate data instead of raw pixel analysis, the system achieves 99% reduction in input dimensionality (63 features vs 4,096 pixels for 64x64 images) while maintaining 92% classification accuracy.

Architecture

System Pipeline

Camera Input → MediaPipe Hand Detection → Landmark Extraction (21 points × 3 coords) 
→ MLP Classifier → Temporal Smoothing → Output Display

Technology Stack

• Computer Vision: OpenCV, Google MediaPipe
• Machine Learning: TensorFlow/Keras (Multi-Layer Perceptron)
• Backend: Flask (REST API)
• Frontend: Vanilla JavaScript, HTML5, CSS3
• Data Processing: NumPy, Pandas

Model Architecture

Input Layer:    63 features (21 hand landmarks × xyz coordinates)
Hidden Layer 1: 128 neurons, ReLU activation, 30% dropout
Hidden Layer 2: 64 neurons, ReLU activation, 30% dropout  
Hidden Layer 3: 64 neurons, ReLU activation
Output Layer:   36 neurons (A-Z, 0-9), Softmax activation

Training Configuration:

• Optimizer: Adam
• Loss Function: Sparse Categorical Crossentropy
• Epochs: 50
• Batch Size: 16
• Train/Test Split: 80/20

Performance Metrics:

• Training Accuracy: 92%
• Validation Accuracy: 88%
• Inference Latency: <50ms per frame
• Model Size: ~500KB

Project Structure

SignDecode/
├── src/                        # Application source code
│   ├── app.py                 # Flask server, API endpoints
│   ├── model.py               # Model wrapper class
│   ├── utils.py               # Keypoint extraction utilities
│   ├── labels.py              # Class label mappings
│   ├── text_to_speech.py      # Audio output module
│   ├── static/                # Frontend assets (CSS, JS)
│   └── templates/             # HTML templates
├── training/                   # Model training pipeline
│   ├── collect_data.py        # Data collection utility
│   ├── train_model.py         # Model training script
│   └── dataset/               # Training data storage
├── models/                     # Trained model artifacts
│   └── sign_language_model.h5
├── run.py                      # Application entry point
├── requirements.txt            # Python dependencies
└── README.md

Installation

Prerequisites

• Python 3.8+
• Webcam
• Modern web browser (Chrome/Firefox recommended)

Setup

# Clone repository
git clone https://github.com/thesakshidigg/SignDecode-Sign-Language-Recognition-.git
cd SignDecode-Sign-Language-Recognition-

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run application
python run.py

Navigate to http://localhost:5000 in your browser.

Usage

Running the Application

python run.py

The Flask server starts on port 5000. The web interface provides:

• Real-time hand tracking visualization
• ASL character recognition
• Text-to-speech output
• Interactive sign language game

Training a Custom Model

Step 1: Collect Training Data

cd training
python collect_data.py

Follow prompts to record hand gestures for each character. Data is saved to training/dataset/sign_data.csv.

Step 2: Train Model

python train_model.py

Trains the neural network and saves the model to models/sign_language_model.h5.

Technical Implementation Details

Hand Landmark Detection

MediaPipe detects 21 anatomical landmarks per hand:

• Wrist (1 point)
• Thumb (4 points)
• Index, Middle, Ring, Pinky fingers (4 points each)

Each landmark provides (x, y, z) coordinates, normalized to [0, 1] range.

Classification Approach

The system uses a Multi-Layer Perceptron (MLP) rather than a Convolutional Neural Network (CNN) because:

1. Input Type: Structured coordinate data (not spatial image data)
2. Efficiency: 63 input features vs 4,096+ for image-based approaches
3. Invariance: Skeletal data is inherently invariant to lighting, background, and skin tone

Temporal Smoothing

A 15-frame consistency filter prevents prediction flickering:

if predicted_label == last_predicted_label:
    frame_count += 1
    if frame_count >= THRESHOLD_FRAMES:
        output_text += predicted_character

API Endpoints

POST /process_frame

Processes a single video frame for hand detection and classification.

Request:

{
  "image": "data:image/jpeg;base64,..."
}

Response:

{
  "prediction": "A",
  "image": "data:image/jpeg;base64,..."
}

GET /status

Returns current recognized text.

POST /clear_text

Clears the output text buffer.

Performance Considerations

• Latency: Sub-50ms inference time enables real-time processing at 30 FPS
• Accuracy Trade-off: Temporal smoothing reduces false positives at the cost of ~500ms recognition delay
• Scalability: Lightweight model enables deployment on edge devices without GPU

Future Enhancements

• Support for dynamic gestures (word-level recognition using LSTM/GRU)
• Multi-language sign language support (BSL, ISL, etc.)
• Mobile deployment using TensorFlow Lite
• Data augmentation for improved robustness
• REST API for third-party integration

Contributing

Contributions are welcome. Please follow standard Git workflow:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Commit changes (git commit -m 'Add feature')
4. Push to branch (git push origin feature/improvement)
5. Open a Pull Request

License

MIT License - see LICENSE file for details.

Contact

Sakshi Diggikar
GitHub: @thesakshidigg

Acknowledgments

• Google MediaPipe for hand tracking framework
• TensorFlow team for ML infrastructure
• ASL dataset contributors