Sign Language Recognition System
This project proposes an optimal recognition engine whose main objective is to translate static American Sign Language alphabets, numbers, and words into human and machine understandable English script and the other way around.
Abstract
Abstract: A large number of deaf and mute people are present around the world and communicating with them is a bit difficult at times; because not everyone can understand Sign language(a system of communication using visual gestures and signs). In addition, there is a lack of official sign language interpreters. In India, the official number of approved sign language interpreters is only 250[1] . This makes communication with deaf and mute people very difficult. The majority of deaf and dumb teaching methods involve accommodating them to people who do not have disabilities - while discouraging the use of sign language. There is a need to encourage the use of sign language. People communicate with each other in sign language by using hand and finger gestures. The language serves its purpose by bridging the gap between the deaf-mute and speaking communities. With recent technological developments, sign language identification is a hard subject in the field of computer vision that has room for further progress. In this project, we propose an optimal recognition engine whose main objective is to translate static American Sign Language alphabets, numbers, and words into human and machine understandable English script and the other way around. Using Neural Networks, we offer a machine learning-based technique for identifying American Sign Language. Keywords: deep learning; convolutional neural network; recognition; comparison; sign language;