• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Sign Language Translation Using Deep Convolutional Neural Networks

Vol. 14, No. 2, February 29, 2020
10.3837/tiis.2020.02.009, Download Paper (Free):

Abstract

Sign language is a natural, visually oriented and non-verbal communication channel between people that facilitates communication through facial/bodily expressions, postures and a set of gestures. It is basically used for communication with people who are deaf or hard of hearing. In order to understand such communication quickly and accurately, the design of a successful sign language translation system is considered in this paper. The proposed system includes object detection and classification stages. Firstly, Single Shot Multi Box Detection (SSD) architecture is utilized for hand detection, then a deep learning structure based on the Inception v3 plus Support Vector Machine (SVM) that combines feature extraction and classification stages is proposed to constructively translate the detected hand gestures. A sign language fingerspelling dataset is used for the design of the proposed model. The obtained results and comparative analysis demonstrate the efficiency of using the proposed hybrid structure in sign language translation.


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Cite this article

[IEEE Style]
R. H.Abiyev, M. Arslan and J. B. Idoko, "Sign Language Translation Using Deep Convolutional Neural Networks," KSII Transactions on Internet and Information Systems, vol. 14, no. 2, pp. 631-653, 2020. DOI: 10.3837/tiis.2020.02.009.

[ACM Style]
Rahib H.Abiyev, Murat Arslan, and John Bush Idoko. 2020. Sign Language Translation Using Deep Convolutional Neural Networks. KSII Transactions on Internet and Information Systems, 14, 2, (2020), 631-653. DOI: 10.3837/tiis.2020.02.009.