Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language
Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language
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Publisher
Henderson: Tech Science Press
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Language
English
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Publisher
Henderson: Tech Science Press
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Contents
The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based Attention Module (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two di...
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Full title
Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language
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Record Identifier
TN_cdi_proquest_journals_2615684333
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2615684333
Other Identifiers
ISSN
1546-2226,1546-2218
E-ISSN
1546-2226
DOI
10.32604/cmc.2022.022471
