Similar to natural language, sign language recognition using machine learning methods has gained momen- tum in recent years. This research project investigates the detailed application of machine learning in sign language recognition (SLR) and classification. It highlights the shortcomings of traditional sign language recognition methods and then proceeds to brief upon the current advances in SLR using machine learn- ing techniques. Using some of the current existing works as a baseline, this project implements modern approaches as well as develop novel architectures to improve the performance of these existing methods. Finally a comparative evaluation is done in the concluding section.
