Arabic Sign Language Recognition an Image-Based Approach

Authors:
M. Mohandes;S. I. Quadri;M. Deriche
Affiliations:
King Fahd University of Petroleum and Minerals, Saudi Arabia;King Fahd University of Petroleum and Minerals, Saudi Arabia;King Fahd University of Petroleum and Minerals, Saudi Arabia
Venue:
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Year:
2007

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A signer-independent Arabic Sign Language recognition system using face detection, geometric features, and a Hidden Markov Model

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Abstract

In this paper we propose an image based system for Arabic Sign Language recognition. A Gaussian skin color model is used to detect the signer's face. The centroid of the detected face is then used as a reference to track the hands movement using region growing from the sequence of images comprising the signs. A number of features are then selected from the detected hand regions across the sequence of images. The recognition stage is performed using a Hidden Markov Model. The proposed system achieved a recognition accuracy of about 93% for a data set of 300 signs with leave one out method.