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A Scheme Towards Automatic Word Indexation System for Balinese Palm Leaf Manuscripts

Journal of ICT Research and Applications (JICTRA) · Vol. 15 No. 2 (2021) · pp. 105–119

DOI
10.5614/itbj.ict.res.appl.2021.15.2.1
ISSN
2338-5499, 2337-5787
Published
2021-10-07
Publisher
DRI - ITB
Language
eng

Abstract

This paper proposes an initial scheme towards the development of an automatic word indexation system for Balinese lontar (palm leaf manuscript) collections. The word indexation system scheme consists of a sub module for patch image extraction of text areas in lontars and a sub module for word image transliteration. This is the first word indexation system for lontar collections to be proposed. To detect parts of a lontar image that contain text, a Gabor filter is used to provide initial information about the presence of text texture in the image. An adaptive sliding patch algorithm for the extraction of patch images in lontars is also proposed. The word image transliteration sub module was built using the long short-term memory (LSTM) model. The results showed that the image patch extraction of text areas process succeeded in optimally detecting text areas in lontars and extracting the patch image in a suitable position. The proposed scheme successfully extracted between 20% to 40% of the keywords in lontars and thus can at least provide an initial description for prospective lontar readers of the content contained in a lontar collection or to find in which lontar collection certain keywords can be found.

Keywords

Collaboration network of the authors

12 authors · 20 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.

  • Universitas Pendidikan Ganesha
  • Udayana University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
I Made Gede SunaryaUniversitas Pendidikan Ganesha2
Putu Hendra SuputraUniversitas Pendidikan Ganesha2
I Made Dendi MaysanjayaUniversitas Pendidikan Ganesha1
I Made Ardwi PradnyanaUniversitas Pendidikan Ganesha1
I Putu Gede Hendra SuputraUdayana University1
Agus Aan Jiwa PermanaUniversitas Pendidikan Ganesha1
Komang SetemenUniversitas Pendidikan Ganesha1
Ayu Nyoman Waisantini WinayaUniversitas Pendidikan Ganesha1
Ni Made Rai MaryatiUniversitas Pendidikan Ganesha1
I Wayan GunayaAffiliation 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://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A Scheme Towards Automatic Word Indexation System for Balinese Palm Leaf Manuscripts</dc:title>
	<dc:creator>Kesiman, Made Windu Antara</dc:creator>
	<dc:creator>Pradnyana, Gede Aditra</dc:creator>
	<dc:subject xml:lang="en-US">Balinese palm leaf manuscript</dc:subject>
	<dc:subject xml:lang="en-US">patch extraction</dc:subject>
	<dc:subject xml:lang="en-US">text detection</dc:subject>
	<dc:subject xml:lang="en-US">transliteration</dc:subject>
	<dc:subject xml:lang="en-US">word indexing</dc:subject>
	<dc:description xml:lang="en-US">This paper proposes an initial scheme towards the development of an automatic word indexation system for Balinese lontar (palm leaf manuscript) collections. The word indexation system scheme consists of a sub module for patch image extraction of text areas in lontars and a sub module for word image transliteration. This is the first word indexation system for lontar collections to be proposed. To detect parts of a lontar image that contain text, a Gabor filter is used to provide initial information about the presence of text texture in the image. An adaptive sliding patch algorithm for the extraction of patch images in lontars is also proposed. The word image transliteration sub module was built using the long short-term memory (LSTM) model. The results showed that the image patch extraction of text areas process succeeded in optimally detecting text areas in lontars and extracting the patch image in a suitable position. The proposed scheme successfully extracted between 20% to 40% of the keywords in lontars and thus can at least provide an initial description for prospective lontar readers of the content contained in a lontar collection or to find in which lontar collection certain keywords can be found.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2021-10-07</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/15263</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2021.15.2.1</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 15 No. 2 (2021); 105-119</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/15263/5410</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Journal of ICT Research and Applications</dc:rights>
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