Grey Wolf Optimization-based Neural Network for Deaf and Mute Sign Language Recognition: Survey

¹ College of Computer science & information technology, University of AL-Qadisiyah, Iraq
² College of Computer science & information technology, University of AL-Qadisiyah, Iraq
³ College of Computer science & information technology, University of AL-Qadisiyah, Iraq

^* Corresponding author: it.mast.23.7@qu.edu.iq

Abstract

Recognizing sign language is one of the most challenging tasks of our time. Researchers in this field have focused on different types of signaling applications to get to know typically, the goal of sign language recognition is to classify sign language recognition into specific classes of expression labels. This paper surveys sign language recognition classification based on machine learning (ML), deep learning (DL), and optimization algorithms. A technique called sign language recognition uses a computer as an assistant with specific algorithms to evaluate basic sign language recognition. The letters of the alphabet were represented through sign language, relying on hand movement to communicate between deaf people and normal people. This paper presents a literature survey of the most important techniques used in sign language recognition models