Abstract: The Hand Gesture Controller project presents an innovative solution in the field of Human-Computer Interaction (HCI) by enabling users to interact with computers using hand gestures. This project eliminates the need for traditional input devices like keyboards and mice, instead relying on gesture recognition technologies to perform various computing tasks. By leveraging OpenCV and MediaPipe, the system achieves accurate and real-time hand gesture recognition, allowing users to control the mouse pointer, perform clicks, scroll, and even execute complex commands like drag-and-drop or multiple item selection. This approach offers a more natural and intuitive interface, particularly beneficial in environments where traditional devices are impractical or for users with mobility impairments. The project emphasizes the use of a gesture recognition system that does not rely on Convolutional Neural Networks (CNNs), opting instead for simpler and more efficient methods suitable for real-time application. Throughout extensive testing, the system demonstrated high accuracy in gesture detection and responsiveness, offering a seamless user experience. This paper explores the development and implementation of the Hand Gesture Controller, its applications, and potential for further development, making a significant contribution to the field of HCI and assistive technology.
Keywords: Machine learning , deep learning, NLP, GenAi,syntax library,C,java,javascripts.
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DOI:
10.17148/IJARCCE.2024.13856
