Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters
According 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 interact...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , , , |
| التنسيق: | article |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://dx.doi.org/10.1016/j.eij.2023.100413 https://www.sciencedirect.com/science/article/pii/S1110866523000695 http://hdl.handle.net/10576/54042 |
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| _version_ | 1857415087519694848 |
|---|---|
| author | Kanchon Kanti, Podder |
| author2 | Emdad Khan, Ludmila Chakma, Jyoti Chowdhury, Muhammad E.H. Dutta, Proma Salam, Khan Md Anwarus Khandakar, Amith Ayari, Mohamed Arselene Bhawmick, Bikash Kumar Islam, S M Arafin Kiranyaz, Serkan |
| author2_role | author author author author author author author author author author |
| author_facet | Kanchon Kanti, Podder Emdad Khan, Ludmila Chakma, Jyoti Chowdhury, Muhammad E.H. Dutta, Proma Salam, Khan Md Anwarus Khandakar, Amith Ayari, Mohamed Arselene Bhawmick, Bikash Kumar Islam, S M Arafin Kiranyaz, Serkan |
| author_role | author |
| dc.creator.none.fl_str_mv | Kanchon Kanti, Podder Emdad Khan, Ludmila Chakma, Jyoti Chowdhury, Muhammad E.H. Dutta, Proma Salam, Khan Md Anwarus Khandakar, Amith Ayari, Mohamed Arselene Bhawmick, Bikash Kumar Islam, S M Arafin Kiranyaz, Serkan |
| dc.date.none.fl_str_mv | 2023-11-22 2024-04-22T08:39:50Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.eij.2023.100413 Podder, 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. 1110-8665 https://www.sciencedirect.com/science/article/pii/S1110866523000695 http://hdl.handle.net/10576/54042 4 24 2090-4754 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Chakma language Handwritten character recognition Deep learning Self-ONN |
| dc.title.none.fl_str_mv | Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | According 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_fccafc7bdde2961e0a5a02c263ef110a |
| identifier_str_mv | Podder, 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. 1110-8665 4 24 2090-4754 |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/54042 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| spelling | Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten charactersKanchon Kanti, PodderEmdad Khan, LudmilaChakma, JyotiChowdhury, Muhammad E.H.Dutta, PromaSalam, Khan Md AnwarusKhandakar, AmithAyari, Mohamed ArseleneBhawmick, Bikash KumarIslam, S M ArafinKiranyaz, SerkanChakma languageHandwritten character recognitionDeep learningSelf-ONNAccording 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.Elsevier2024-04-22T08:39:50Z2023-11-22Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.eij.2023.100413Podder, 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), 100413.1110-8665https://www.sciencedirect.com/science/article/pii/S1110866523000695http://hdl.handle.net/10576/540424242090-4754enhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/540422024-07-23T15:53:25Z |
| spellingShingle | Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters Kanchon Kanti, Podder Chakma language Handwritten character recognition Deep learning Self-ONN |
| status_str | publishedVersion |
| title | Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters |
| title_full | Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters |
| title_fullStr | Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters |
| title_full_unstemmed | Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters |
| title_short | Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters |
| title_sort | Self-ChakmaNet: A deep learning framework for indigenous language learning using handwritten characters |
| topic | Chakma language Handwritten character recognition Deep learning Self-ONN |
| url | http://dx.doi.org/10.1016/j.eij.2023.100413 https://www.sciencedirect.com/science/article/pii/S1110866523000695 http://hdl.handle.net/10576/54042 |