A Moroccan Sign Language Recognition Algorithm Using a Convolution Neural Network

Abstract

Gesture recognition is an open phenomenon in computer vision, and one of the topics of current interest. Gesture recognition has many applications in the interpretation of sign language, one is in human-computer interaction, and the other is in immersive game technology. For this reason, we have developed a model of image processing recognition of gestures, based on artificial neural networks, starting from data collection, identification, tracking and classification of gestures, to the display of the obtained results. We propose an approach to contribute to the translation of sign language into voice/text format. In this paper, we present a Moroccan sign language recognition system using a convolutional neural network (CNN). This system includes an important data set of more than 20 files. Each file contains 1000 static images of each signal from several different angles that we collected with a camera. Different sign language models were evaluated and compared with the proposed CNN model. The proposed system achieved an accuracy of 99.33% and achieved best performance with an accuracy rate of 98.7%.