Intelligent Malaysian Sign Language Translation System Using Convolutional-Based Attention Module with Residual Network
(e deaf-mutes population always feels helpless when they are not understood by others and vice versa. (is is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to re...
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Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
Hindawi
2021
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Subjects: | |
Online Access: | http://ir.unimas.my/id/eprint/37106/1/residual1.pdf http://ir.unimas.my/id/eprint/37106/ https://www.hindawi.com/journals/cin/2021/9023010/ https://doi.org/10.1155/2021/9023010 |
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Summary: | (e deaf-mutes population always feels helpless when they are not understood by others and vice versa. (is is a big humanitarian
problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN),
convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images. Two different experiments
were conducted for MSL signs, using CBAM-2DResNet (2-Dimensional Residual Network) implementing “Within
Blocks” and “Before Classifier” methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix,
and training time are recorded to evaluate the models’ efficiency. (e experimental results showed that CBAM-ResNet models
achieved a good performance in MSL signs recognition tasks, with accuracy rates of over 90% through a little of variations. (e
CBAM-ResNet “Before Classifier” models are more efficient than “Within Blocks” CBAM-ResNet models. (us, the best trained
model of CBAM-2DResNet is chosen to develop a real-time sign recognition system for translating from sign language to text and
from text to sign language in an easy way of communication between deaf-mutes and other people. All experiment results
indicated that the “Before Classifier” of CBAMResNet models is more efficient in recognising MSL and it is worth for
future research. |
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