Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network
ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY
View Publication Info| Field | Value | |
| Title |
Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network
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| Creator |
Mirza, Sami F.
Al-Talabani, Abdulbasit K. |
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| Subject |
Deep learning
Echo system network Long short-term memory Microsoft Kinect v2 Sensor Recurrent neural network Sign language |
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| 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.
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| Publisher |
Koya University
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| Date |
2021-10-13
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| Type |
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion Peer-reviewed article text |
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| Format |
application/pdf
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| Identifier |
http://aro.koyauniversity.org/index.php/aro/article/view/827
10.14500/aro.10827 |
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| Source |
ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY; Vol. 9 No. 2 (2021): Issue Seventeen; 1-9
2307-549X 2410-9355 |
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| Language |
eng
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| Relation |
http://aro.koyauniversity.org/index.php/aro/article/view/827/219
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| Rights |
Copyright (c) 2021 Sami F. Mirza, Abdulbasit K. Al-Talabani
https://creativecommons.org/licenses/by-nc-sa/4.0 |
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