An Adaptive Learning Method for Sign Language Detection
A. V. Sriharsha, K Anusha, Bramhanapudhuru Naveen, Kacheri Sadak, Hima Harshitha Reddy G · 2024
Advancements in hand gesture recognition technology are transforming the way we interact with computers, manipulate objects in virtual worlds, and communicate using hand gestures. In this paper, we present a system that leverages the combined power of Media Pipe's holistic model and machine learning to achieve real-time hand gesture recognition with remarkable accuracy and efficiency. The pre-trained machine learning model is employed to classify detected hand gestures, providing a robust framework for gesture recognition. The system operates live, capturing video frames from your camera. Using Media Pipe's magic, it pinpoints key points on your hands, creating a digital map of their position and configuration. Detected hand landmarks are visually overlaid on the video frames, enhancing user understanding and system transparency. This information is then fed into a pre-trained machine learning model, a master of decoding hand movements, which instantly deciphers the gesture you're making. Like a secret code translator, it converts this gesture into a corresponding label, transforming your hand movements into meaningful commands or words.