A Proposed Model for Standard Arabic Sign Language Recognition Based on Multiplicative Neural Network

Abstract

Sign language recognition is one of the most challenging fields in Human-Computer Interface (HCI) applications. Although there are many obstacles that could dramatically limit the spread of sign language translators in our daily life, the community needs for these translators are no longer a luxury and increase day after day, other than the problems of sign languages all over the world, Arabic sign language enjoys its own difficulties and issues. This paper discusses Arabic sign language problems and proposes a recognition model for standard Arabic sign language. A model is proposed and developed for real-time hand signs recognition. The experiment was conducted on 100signs and the result was 94% recognition accuracy confirming words offline extendibility. Although the scientific understanding for the sign language is an essential step to build up a realistic recognition system, the proposed model can be used in other sign languages. The model exploits multi-stage Multiplicative Neural Networks for posture classification.