MediaPipe and Deep Learning for Robust Real-Time Hand Gesture Recognition in Sign Language

Abstract - This extend centers on creating an AI-based framework for real-time sign dialect discovery utilizing computer vision and profound learning methods. The essential objective is to bridge the communication hole between the hard of hearing and hearing communities by precisely recognizing hand motions and changing over them into content or discourse. The examination includes utilizing MediaPipe Hands, OpenCV, and a profound learning show prepared on a dataset of sign dialect signals. Strategies such as convolutional neural systems (CNNs) and repetitive neural systems (RNNs) are utilized to make strides motion acknowledgment precision.