2017
DOI: 10.14569/ijacsa.2017.080538
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The Performance of Individual and Ensemble Classifiers for an Arabic Sign Language Recognition System

Abstract: Abstract-The objective of this paper is to compare different classifiers' recognition accuracy for the 28 Arabic alphabet letters gestured by participants as Sign Language and captured by two depth sensors. The accuracy results of three individual classifiers: (1) the support vector machine (SVM), (2) random forest (RF), and (3) nearest neighbour (kNN), using the original gestured dataset were compared with the accuracy results using an ensemble of the results of each classifier, as recommended by the literatu… Show more

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