Implementation of real-time static hand gesture recognition using artificial neural network

Abstract

Sign language is a language that requires the combination of hand gesture, orientation, movement of the hands, arms, body, and facial to simultaneously express the thoughts of the speaker. This paper implements static hand gesture recognition in recognizing the alphabetical sign from “A” to “Z”, number from “0” to “9”, and additional punctuation mark such as “Period”, “Question Mark”, and “Space”in Sistem Isyarat Bahasa Indonesia (SIBI). Hand gestures are obtained by evaluating the contour representation from image segmentation of the glove wore by user and then is classified using Artificial Neural Network based on the training model previously built from 100 images for each gesture. The accuracy rate of hand gesture translation is calculated to be 90%. Speech translation recognized NATO phonetic letter as the speech input for translation.