Interpreting deaf-mute people has always been a problem for people as they primarily rely on sign language for communicating. Active participation of the deaf-mute community still remains at an elementary stage, despite multiple nations providing resources for the same, like a sign language interpreter and communicator of news in the country of New Zealand. Perturbing situations such as kidnapping, deception, fire breakout or any other situations of general agony could further exacerbate this barrier of communication, as the mute people try their best to communicate, but the majority remains oblivious to their language. Therefore, bridging the gap between these two worlds is of utmost necessity. This paper aims to briefly acquaint the reader with how sign language communication works and puts forward research conducted in this field that explains how to capture and recognize sign language and also attempts to suggest a systemized solution

Keywords : Hilbert Curve, Support Vector Machines, Random Forests, Artificial Neural Network, Feed-forward Backpropagation, Hough Transform, Convolutional Neural Networks, Stacked Deionized Decoders, Multilayer Perceptron Neural Network, Adaline Neural Networ