Enhancing American Sign Language Alphabet Recognition: A Fusion of Media Pipe and LSTM Technologies

Abstract

With the advancement of today’s technologies in artificial intelligence, humans tend to use hand gestures in their communication to convey their ideas. Gesture recognition is an active area of research in the human-computer interface (HCI). Gesture recognition is important for communication between deaf-mute people, HCI, robot control, home automation, and medical applications. In this article, a simple and efficient vision-based approach for American Sign Language (ASL) alphabets recognition has been discussed to recognize both static and dynamic gestures. Media pipe introduced by Google had been used to get hand landmarks and a custom data set has been created and used for the experimental study. Hand gesture recognition has been done by using Long short-term memory (LSTM). The proposed system has been investigated with 26 alphabets and an accuracy of 99% has been achieved. This work can be used to convert hand gestures into text.