Samir, A., Aboulela, M., Tolba, M. (2014). A Proposed Model for Standard Arabic Sign Language Recognition Based on Multiplicative Neural Network. The Egyptian Journal of Language Engineering, 1(2), 1-10. doi: 10.21608/ejle.2014.59919
Ahmed Samir; Magdi Aboulela; Mohamed Tolba. "A Proposed Model for Standard Arabic Sign Language Recognition Based on Multiplicative Neural Network". The Egyptian Journal of Language Engineering, 1, 2, 2014, 1-10. doi: 10.21608/ejle.2014.59919
Samir, A., Aboulela, M., Tolba, M. (2014). 'A Proposed Model for Standard Arabic Sign Language Recognition Based on Multiplicative Neural Network', The Egyptian Journal of Language Engineering, 1(2), pp. 1-10. doi: 10.21608/ejle.2014.59919
Samir, A., Aboulela, M., Tolba, M. A Proposed Model for Standard Arabic Sign Language Recognition Based on Multiplicative Neural Network. The Egyptian Journal of Language Engineering, 2014; 1(2): 1-10. doi: 10.21608/ejle.2014.59919
A Proposed Model for Standard Arabic Sign Language Recognition Based on Multiplicative Neural Network
1Faculty of Computer and Information Technology, Ain Shams University, Cairo, Egypt
2Sadat Academy for Management Sciences, Cairo, Egypt
3Faculty of Computers and Information Technology, Ain Shams University Cairo, Egypt
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.
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