Abstract
Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL) alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.
Highlights
Signing has always been part of human communications
We have compared the performance of our system to previously published work using adaptive neuro-fuzzy inference systems (ANFIS)-based classification
We have used the same actual data collected from deaf people, and the Misclassifications using the training data Misclassifications using the test data
Summary
Signing has always been part of human communications. The use of gestures is not tied to ethnicity, age, or gender. Existing HCI devices for hand gesture recognition fall into two categories: glove-based and vision-based systems. Vision-based systems basically suggest using a set of video cameras, image processing, and artificial intelligence to recognize and interpret hand gestures [1]. Several approaches have been used for hand gestures recognition including fuzzy logic, neural networks, neuro-fuzzy, and hidden Markov model. Lee et al have used fuzzy logic and fuzzy min-max neural networks techniques for Korean sign language recognition [10] They were able to achieve a recognition rate of 80.1% using gloved-based system. It has been shown that the polynomial technique can provide several advantages over other methods (e.g., neural network, hidden Markov models, etc.) These advantages include computational and storage requirements and recognition performance.
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