Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network

ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY

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Field Value
 
Title Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network
 
Creator Mirza, Sami F.
Al-Talabani, Abdulbasit K.
 
Subject Deep learning
Echo system network
Long short-term memory
Microsoft Kinect v2 Sensor
Recurrent neural network
Sign language
 
Description Sign language assists in building communication and bridging gaps in understanding. Automatic sign language recognition (ASLR) is a field that has recently been studied for various sign languages. However, Kurdish sign language (KuSL) is relatively new and therefore researches and designed datasets on it are limited. This paper has proposed a model to translate KuSL into text and has designed a dataset using Kinect V2 sensor. The computation complexity of feature extraction and classification steps, which are serious problems for ASLR, has been investigated in this paper. The paper proposed a feature engineering approach on the skeleton position alone to provide a better representation of the features and avoid the use of all of the image information. In addition, the paper proposed model makes use of recurrent neural networks (RNNs)-based models. Training RNNs is inherently difficult, and consequently, motivates to investigate alternatives. Besides the trainable long short-term memory (LSTM), this study has proposed the untrained low complexity echo system network (ESN) classifier. The accuracy of both LSTM and ESN indicates they can outperform those in state-of-the-art studies. In addition, ESN which has not been proposed thus far for ASLT exhibits comparable accuracy to the LSTM with a significantly lower training time.
 
Publisher Koya University
 
Date 2021-10-13
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed article
text
 
Format application/pdf
 
Identifier http://aro.koyauniversity.org/index.php/aro/article/view/827
10.14500/aro.10827
 
Source ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY; Vol. 9 No. 2 (2021): Issue Seventeen; 1-9
2307-549X
2410-9355
 
Language eng
 
Relation http://aro.koyauniversity.org/index.php/aro/article/view/827/219
 
Rights Copyright (c) 2021 Sami F. Mirza, Abdulbasit K. Al-Talabani
https://creativecommons.org/licenses/by-nc-sa/4.0
 

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