Abstract: In this paper, an algorithm for Arabic sign language recognition is proposed. The proposed algorithm facilitates the communication between deaf and non-deaf people. A possible way to achieve this goal is to enable computer systems to visually recognize hand gestures from images. In this context, a proposed criterion which is called Enhancement Motion Chain Code (EMCC) that uses Hidden Markov Model (HMM) on word level for Arabic sign language recognition (ArSLR) is introduced. This paper focuses on recognizing Arabic sign language at word level used by the community of deaf people. Experiments on real-world datasets showed that the reliability and suitability of the proposed algorithm for Arabic sign language recognition. The experiment results introduce the gesture recognition error rate for a different sign is 1.2% compared to that of the competitive method.

Mahmoud Zaki Abdo, Alaa Mahmoud Hamdy, Sameh Abd El-Rahman Salem and Elsayed Mostafa Saad. “EMCC: Enhancement of Motion Chain Code for Arabic Sign Language Recognition”. International Journal of Advanced Computer Science and Applications (IJACSA) 6.12 (2015). http://dx.doi.org/10.14569/IJACSA.2015.061215

@article{Abdo2015,
title = {EMCC: Enhancement of Motion Chain Code for Arabic Sign Language Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.061215},
url = {http://dx.doi.org/10.14569/IJACSA.2015.061215},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {12},
author = {Mahmoud Zaki Abdo and Alaa Mahmoud Hamdy and Sameh Abd El-Rahman Salem and Elsayed Mostafa Saad}
}