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Deep Learning Approaches for Nusantara Scripts Optical Character Recognition

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 3 (2023) · pp. 325–336

DOI
10.22146/ijccs.86302
ISSN
2460-7258, 1978-1520
Published
2023-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The number of speakers of regional languages who are able to read and to write traditional scripts in Indonesia is decreasing. If left unaddressed, this will lead to the extinction of Nusantara scripts and it is not impossible that their reading methods will be forgotten in the future. To anticipate this, this study aims to preserve the knowledge of reading ancient scripts by developing a Deep Learning model that can read document images written using one of the 10 Nusantara scripts we have collected: Bali, Batak, Bugis, Javanese, Kawi, Kerinci, Lampung, Pallava, Rejang, and Sundanese. While previous studies have made efforts to read traditional Nusantara scripts using various Machine Learning and Convolutional Neural Network algorithms, they have primarily focused on specific scripts and lacked an integrated approach from script type recognition to character recognition. This study is the first to comprehensively address the entire range of Nusantara scripts, encompassing script type detection and character recognition. Convolutional Neural Network, ConvMixer, and Visual Transformer models were utilized and their respective performances were compared. The results demonstrate that our models achieved 96% accuracy in classifying Nusantara script types, with character recognition accuracy ranging from 93% to approximately 100% across the ten scripts.

Keywords

Collaboration network of the authors

9 authors · 18 co-authorship links · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Telkom Purwokerto
  • Jenderal Soedirman University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Julian SaputraAffiliation unknown3
Haris Budi WidodoJenderal Soedirman University1
Andre Citro Febriliyan LanyakAffiliation unknown1
Muhammad Hisyam GhaniAffiliation unknown1
Abiyan AthallahAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Deep Learning Approaches for Nusantara Scripts Optical Character Recognition</dc:title>
	<dc:creator>Prasetiadi, Agi</dc:creator>
	<dc:creator>Saputra, Julian</dc:creator>
	<dc:creator>Kresna, Iqsyahiro</dc:creator>
	<dc:creator>Ramadhanti, Imada</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Nusantara Script; Optical Character Recognition; Convolutional Neural Network, ConvMixer; Visual Transformer</dc:subject>
	<dc:description xml:lang="en-US">The number of speakers of regional languages who are able to read and to write traditional scripts in Indonesia is decreasing. If left unaddressed, this will lead to the extinction of Nusantara scripts and it is not impossible that their reading methods will be forgotten in the future. To anticipate this, this study aims to preserve the knowledge of reading ancient scripts by developing a Deep Learning model that can read document images written using one of the 10 Nusantara scripts we have collected: Bali, Batak, Bugis, Javanese, Kawi, Kerinci, Lampung, Pallava, Rejang, and Sundanese. While previous studies have made efforts to read traditional Nusantara scripts using various Machine Learning and Convolutional Neural Network algorithms, they have primarily focused on specific scripts and lacked an integrated approach from script type recognition to character recognition. This study is the first to comprehensively address the entire range of Nusantara scripts, encompassing script type detection and character recognition. Convolutional Neural Network, ConvMixer, and Visual Transformer models were utilized and their respective performances were compared. The results demonstrate that our models achieved 96% accuracy in classifying Nusantara script types, with character recognition accuracy ranging from 93% to approximately 100% across the ten scripts.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/86302</dc:identifier>
	<dc:identifier>10.22146/ijccs.86302</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 3 (2023): July; 325-336</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/86302/36989</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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