Improved American Sign Language Recognition and Correction Using Inception Network, MediaPipe and PyEnchant

Abstract

Sign language recognition through image processing presents challenges related to the requirement of real time applicability and high accuracy. Though previous work adopting methodologies from deep convolutional neural network architectures have shown to achieve good performance, they lack a consummate solution in terms of accuracy due to consideration of word based recognition. Recent development of Inception Network based architectures have shown promising classification accuracy with relatively less computational demand. Hence in this paper we propose a methodology that adopts Inception Network for the task of Sign Language Recognition. We considered the American Sign Language Recognition and Correction model. The correction and suggestion tools are implemented in the model to rectify any incorrect sign detection. The results from our approach achieves accuracy in the order of 99 percent.