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ArSL-CNN: a convolutional neural network for Arabic sign language gesture recognition
journal contribution
posted on 2021-10-27, 09:46 authored by Ali A. Alani, Georgina CosmaGeorgina CosmaSign language (SL) is a visual language means of communication for people with deafness or hearing impairments. In Arabic-speaking countries, there are many arabic sign languages (ArSL) and these use the same alphabets. This study proposes ArSLCNN, a deep learning model that is based on a convolutional neural network (CNN) for translating Arabic SL (ArSL). Experiments were performed using a large ArSL dataset (ArSL2018) that contains 54,049 images of 32 sign language gestures, collected from forty participants. The results of the first experiments with the ArSL-CNN model returned a train and test accuracy of 98.80% and 96.59%, respectively. The results also revealed the impact of imbalanced data on model accuracy. For the second set of experiments, various re-sampling methods were applied to the dataset. Results revealed that applying the synthetic minority oversampling technique (SMOTE) improved the overall test accuracy from 96.59% to 97.29%, yielding a statistically significant improvement in test accuracy (p=0.016, 0:05). The proposed ArSL-CNN model can be trained on a variety of Arabic sign languages and reduce the communication barriers encountered by deaf communities in Arabic-speaking countries.
History
School
- Science
Department
- Computer Science
Published in
Indonesian Journal of Electrical Engineering and Computer ScienceVolume
22Issue
2Pages
1096-1107Publisher
Institute of Advanced Engineering and Science (IAES)Version
- VoR (Version of Record)
Rights holder
© The AuthorsPublisher statement
This is an Open Access Article. It is published by the Institute of Advanced Engineering and Science (IAES) under the Creative Commons Attribution-ShareAlike 4.0 International Licence (CC BY-SA 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by-sa/4.0/Acceptance date
2021-03-20Publication date
2021-05-01Copyright date
2021ISSN
2502-4752eISSN
2502-4760Publisher version
Language
- en
Depositor
Dr Georgina Cosma. Deposit date: 26 October 2021Usage metrics
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