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Arabic Sign Language (ArSL) is similar to other sign languages in terms of the way it is gestured and interpreted and used as a medium of communication among the hearing-impaired and the communities in which they live in. Research investigating sensor utilization and natural user interfaces to facilitate ArSL recognition and interpretation, is lacking.Previous research has demonstrated that there is not a single classifier modeling approach that can be suitable for all hand gesture recognition tasks, therefore, this research investigated which combination of algorithms, set with different parameters used with a sensor device, produce higher ArSL recognition accuracy results in a gesture recognition system.This research proposed a dynamic prototype model (DPM) using Kinect as a sensor to recognize certain ArSL gestured dynamic words. The DPM used eleven predictive models of three algorithms (SVM, RF, KNN) based on different parameter settings. Research findings indicated that highest recognition accuracy rates for the dynamic words gestured were achieved by the SVM models, with linear kernel and cost parameter ¼ 0.035.

Abstract:The objective of this research was to recognize the hand gestures of Arabic Sign Language (ArSL) words using two depth sensors. The researchers developed a model to examine 143 signs gestured by 10 users for 5 ArSL words (the dataset). The sensors captured depth images of the upper human body, from which 235 angles (features) were extracted for each joint and between each pair of bones. The dataset was divided into a training set (109 observations) and a testing set (34 observations). The support vector machine (SVM) classifier was set using different parameters on the gestured words' dataset to produce four SVM models, with linear kernel (SVMLD and SVMLT) and radial kernel (SVMRD and SVMRT) functions. The overall identification accuracy for the corresponding words in the training set for the SVMLD, SVMLT, SVMRD, and SVMRT models was 88.92%, 88.92%, 90.88%, and 90.884%, respectively. The accuracy from the testing set for SVMLD, SVMLT, SVMRD, and SVMRT was 97.059%, 97.059%, 94.118%, and 97.059%, respectively. Therefore, since the two kernels in the models were close in performance, it is far more efficient to use the less complex model (linear kernel) set with a default parameter.

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 literature. SVM produced higher overall accuracy when running as an individual classifier regardless of the number of observations for each letter. However, for letters with fewer than 65 observations each, which created a far smaller dataset, RF had higher accuracy than SVM did when using the ensemble approach. Although RF produced higher accuracy results for classes with limited class observation data, the difference between the accuracy results of RF in phase 2 and SVM in phase 1 was negligible. The researchers conclude that such a difference does not warrant using the ensemble approach for this experiment, which adds more processing complexity without a significant increase in accuracy.

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