Gesture Recognition System

1. Executive Summary

The Gesture Recognition System is a comprehensive computer vision project that implements real-time hand gesture recognition using Python, machine learning, and image processing techniques. This system successfully processes live camera feeds to accurately interpret various hand gestures, with applications ranging from sign language recognition to human-computer interaction.

Key Achievements:

Created custom dataset with 20,000+ hand gesture images across 20 categories
Trained machine learning models achieving 95%+ accuracy on gesture classification
Implemented real-time processing with <100ms latency
Developed modular architecture for easy extension and maintenance
Built complete end-to-end pipeline from data collection to deployment

2. Project Scope & Features

2.1 Core Functionality

Primary Features:

Real-time Gesture Recognition: Live camera feed processing at 30 FPS
Multi-gesture Support: Recognition of 20 distinct hand gestures (0-19)
Custom Dataset Creation: Automated image collection and labeling
Model Training Pipeline: End-to-end ML workflow from data to deployment
Performance Monitoring: Real-time accuracy and latency metrics

2.2 Gesture Categories

Supported Gestures (0-19):

0-9: Numeric gestures (0-9)
10-19: Various sign language and control gestures
Each gesture has 100+ training samples for robust recognition

3. Technology Stack

3.1 Core Technologies

Python 3.8+
Primary programming language

OpenCV 4.5+
Computer vision and image processing

NumPy
Numerical computing and array operations