A Comprehensive Study on Sign Languages Recognition Systems Using (SVM, KNN, CNN and ANN)

Agha, Rawan A. Al Rashid, Muhammed N. Sefer, and Polla Fattah


The needs of communities and the new emerging technologies aspire researchers to come up with new and innovative ways to fulfil these needs. Sign languages are said to be a visual language that is used by the deaf community. Undoubtedly, there is a communication difficulty between the hearing-impaired people and the hearing community. To overcome this impediment between the two communities, various approaches were conducted to develop sign language recognition systems. An evaluation between some of these recent technologies is crucial to compare their methodologies and accuracy of their results. Therefore, this paper provides a comprehensive study on the different approaches and techniques used to develop a sign language study. On systems that were developed based on support vector machine (SVM), K-nearest neighbours (KNN) classifier, deep convolutional neural networks (CNN) and artificial neural networks (ANN).


Agha, Rawan A. Al Rashid, Muhammed N. Sefer, and Polla Fattah. ‘A comprehensive study on sign languages recognition systems using (SVM, KNN, CNN and ANN).’ In Proceedings of the First International Conference on Data Science, E-learning and Information Systems, pp. 1-6. 2018.

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