Language is the method of communication between individuals and communities through either sounds, written symbols, or visual gestures. There is a variety of languages around the world. One significant language that is made up of hand gestures is sign language and it is the main method of communication between and with deaf people. Translation between languages is a practice that has been automated with the advancement of artificial intelligence, this has also allowed for the translation of sign language into written language. However, there remains a certain gap in translating between regional variants of sign language. The purpose of this study is to propose a method of implementing a machine learning model on multiple sets of data to automate the process of translating between regional variants of hand sign language, through a series of interconnected deep learning algorithms including Convolution Neural Networks & sequence Transformers. This study will produce a model that can translate between linguistic variants of hand sign language.

Publication Date


Document Type

Master's Project

Student Type


Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research (Dubai)


Sanjay Modak

Advisor/Committee Member

Khalil Al Hussaeni


RIT Dubai