MediaPipe with LSTM Architecture for Real-Time Hand Gesture Recognization

Abstract

Gesture recognition plays a vital role in the area of research for human-computer interaction (HCI). The integration of MediaPipe with Long Short Term Memory (LSTM) architecture holds tremendous potential for real-time hand gesture recognition. MediaPipe provides a robust and versatile framework for capturing and processing multimedia input, such as video streams from cameras or pre-recorded video files. The temporal modeling capabilities of LSTM captures the temporal dynamics of hand gestures. This research paper aims to present a novel method utilizing the MediaPipe with LSTM architecture for real-time hand gesture recognition. A test on real-time gesture recognition is performed to evaluate the performance of the suggested model. Our results demonstrate that the suggested method outperforms other state-of-theart approaches on our custom made dataset with an accuracy of 98.99%.