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AuthorKanchon Kanti, Podder
AuthorEmdad Khan, Ludmila
AuthorChakma, Jyoti
AuthorChowdhury, Muhammad E.H.
AuthorDutta, Proma
AuthorSalam, Khan Md Anwarus
AuthorKhandakar, Amith
AuthorAyari, Mohamed Arselene
AuthorBhawmick, Bikash Kumar
AuthorIslam, S M Arafin
AuthorKiranyaz, Serkan
Available date2024-04-22T08:39:50Z
Publication Date2023-11-22
Publication NameEgyptian Informatics Journal
Identifierhttp://dx.doi.org/10.1016/j.eij.2023.100413
CitationPodder, K. K., Khan, L. E., Chakma, J., Chowdhury, M. E., Dutta, P., Salam, K. M. A., ... & Kiranyaz, S. (2023). Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters. Egyptian Informatics Journal, 24(4), 100413.
ISSN1110-8665
URIhttps://www.sciencedirect.com/science/article/pii/S1110866523000695
URIhttp://hdl.handle.net/10576/54042
AbstractAccording to UNESCO's Atlas of the World's Languages in Danger, 40% of the languages today are counted as endangered in the future. Indigenous languages are endangered because of the less availability of interactive learning mediums for those languages. Thus this paper proposes an interactive deep learning method for Handwritten Character Recognition of the indigenous language “Chakma.” The method comprises dataset creation using a mobile app named “EthnicData.” It reports the first “Handwriting Character Dataset” of Chakma containing 47,000 images of 47 characters of Chakma language using the app. A novel SelfONN-based deep learning model, Self-ChakmaNet, is proposed in this research for Chakma Handwritten character recognition. The Self-ChakmaNet achieved 99.84% for overall accuracy, precision, recall, F1 score, and sensitivity. The proposed model with high accuracy can be implemented in mobile devices for handwritten character recognition as the model has less number of parameters and a faster processing speed.
Languageen
PublisherElsevier
SubjectChakma language
Handwritten character recognition
Deep learning
Self-ONN
TitleSelf-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters
TypeArticle
Issue Number4
Volume Number24
Open Access user License http://creativecommons.org/licenses/by/4.0/
ESSN2090-4754
dc.accessType Open Access


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