Kelsey Atemie-Hart
Sign Language is the method of communication used by people with hearing and speaking disabilities. The sign languages used in a certain country will be different from that used in another country. The individuals without this disability will find it difficult to communicate with the deaf and dumb, leading to a communication gap between these people. There are technologies that are developed to aid the learning of sign language through the implementation of deep learning techniques in machine learning to develop a Sign Language Recognition (SLR) system. A SLR system is a system that is used with the aid of machine learning to convert sign gestures performed by individuals to readable human text. There are two approaches in deep learning that used; sensor-based and vision-based approach. These two approaches differ in computation and structure. This researcher paper will analyze these two approaches, how they are implemented, the limitations in these approaches and from the analysis determine the best approach to adopt when developing a SLR system. The best approach to adopt will be determined by its output recognition accuracy, ease to use by end-users and the computational complexity when building it.
Keywords: communication, sign language recognition, analyze, accuracy.