Deep Learning Techniques for Spanish Sign Language Interpretation
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http://hdl.handle.net/10045/115802
Title: | Deep Learning Techniques for Spanish Sign Language Interpretation |
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Authors: | Martinez-Martin, Ester | Morillas-Espejo, Francisco |
Research Group/s: | Robótica y Visión Tridimensional (RoViT) |
Center, Department or Service: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Keywords: | Deep learning | Spanish sign language | Interpretation | Neural networks |
Knowledge Area: | Ciencia de la Computación e Inteligencia Artificial |
Issue Date: | 15-Jun-2021 |
Publisher: | Hindawi |
Citation: | Computational Intelligence and Neuroscience. 2021, Volume 2021: Article ID 5532580. https://doi.org/10.1155/2021/5532580 |
Abstract: | Around 5% of the world population suffers from hearing impairment. One of its main barriers is communication with others since it could lead to their social exclusion and frustration. To overcome this issue, this paper presents a system to interpret the Spanish sign language alphabet which makes the communication possible in those cases, where it is necessary to sign proper nouns such as names, streets, or trademarks. For this, firstly, we have generated an image dataset of the signed 30 letters composing the Spanish alphabet. Then, given that there are static and in-motion letters, two different kinds of neural networks have been tested and compared: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). A comparative analysis of the experimental results highlights the importance of the spatial dimension with respect to the temporal dimension in sign interpretation. So, CNNs obtain a much better accuracy, with 96.42% being the maximum value. |
Sponsor: | This work was partly supported by Generalitat Valenciana (GV/2020/051). |
URI: | http://hdl.handle.net/10045/115802 |
ISSN: | 1687-5265 (Print) | 1687-5273 (Online) |
DOI: | 10.1155/2021/5532580 |
Language: | eng |
Type: | info:eu-repo/semantics/article |
Rights: | © 2021 Ester Martinez-Martin and Francisco Morillas-Espejo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Peer Review: | si |
Publisher version: | https://doi.org/10.1155/2021/5532580 |
Appears in Collections: | INV - RoViT - Artículos de Revistas |
Files in This Item:
File | Description | Size | Format | |
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Martinez-Martin_Morillas-Espejo_2021_ComputIntellNeurosci.pdf | 3,34 MB | Adobe PDF | Open Preview | |
This item is licensed under a Creative Commons License