Open CV Based Hand Gesture Recognition for Virtual Keyboard Control System

In the evolving landscape of computer technology, the interaction between humans and machines has undergone significant transformation, particularly emphasizing inclusivity and accessibility for individuals with disabilities. This project introduces a hand gesture recognition system designed to enable disabled individuals to interact with computers effortlessly, replacing traditional hardware like keyboards . Leveraging advanced technologies such as OpenCV, MediaPipe, and Python, the system allows users to control a virtual keyboard through intuitive hand gestures. By using a camera to track and interpret gestures, the system eliminates the need for physical input devices, offering a natural and efficient human-computer interface. The camera feed is displayed on the system screen for calibration, ensuring accurate gesture detection and personalization to the user’s needs. MediaPipe's robust AI capabilities enhance the system’s accuracy and responsiveness, making it a practical and empowering tool for individuals with mobility or dexterity impairments. This innovation aims to bridge the accessibility gap, fostering independence and improving quality of life for users with disabilities. KEYWORD: Hand Gesture Recognition – Accessibility – Inclusivity - Disabled Individuals - Human-Computer Interaction – OpenCV – MediaPipe – Python - Virtual Keyboard - Gesture Detection -Camera Feed – Calibration - AI Capabilities - Mobility Impairments - Dexterity Impairments - Physical Input Devices - Natural Interface - Independent Living - Quality of Life - Assistive Technology