An Improved Sign Language Translation approach using KNN in Deep Learning Environment

Abstract

The deaf and dumb community’s primary mode of communication is signs. It is the only source through which deaf and dumb people can communicate with others. The goal of this paper is to invent a model for translating signs into text format. With assistance of machine learning algorithms, we will scan the signs and then convert them to understandable text. KNN (k-nearest neighbour) algorithm will be used to do so. User will get an interface where it can train the system according to their signs and meanings with respect to it, which can later be used for interaction between deaf and dumb people and common people and vice versa. The assessment of this model is conducted with 3 students using various training examples. The accuracy obtained is approximately 97%.