Convolutional Neural Networks for Handwritten Javanese Character Recognition

Dewa, Chandra Kusuma; Fadhilah, Amanda Lailatul; Afiahayati, A

doi:10.22146/ijccs.31144

Open access Jurnal SINTA 2

Convolutional Neural Networks for Handwritten Javanese Character Recognition

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 12 No. 1 (2018) · pp. 83–94

DOI: 10.22146/ijccs.31144
ISSN: 2460-7258, 1978-1520
Published: 2018-01-31
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

Convolutional neural network (CNN) is state-of-the-art method in object recognition task. Specialized for spatial input data type, CNN has special convolutional and pooling layers which enable hierarchical feature learning from the input space. For offline handwritten character recognition problem such as classifying character in MNIST database, CNN shows better classification result than any other methods. By leveraging the advantages of CNN over character recognition task, in this paper we developed a software which utilizes digital image processing methods and CNN module for offline handwritten Javanese character recognition. The software performs image segmentation process using contour and Canny edge detection with OpenCV library over captured handwritten Javanese character image. CNN will classify the segmented image into 20 classes of Javanese letters. For evaluation purposes, we compared CNN to multilayer perceptron (MLP) on classification accuracy and training time. Experiment results show that CNN model testing accuracy outperforms MLP accuracy although CNN needs more training time than MLP.

Keywords

Collaboration network of the authors

9 authors · 12 co-authorship links · 4 across different organisations

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Islamic University of Indonesia
Universitas Gadjah Mada
Udayana University
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
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Co-author	Organisation	Joint articles
Reza Pulungan	Universitas Gadjah Mada	1
Muhammad Ihsan	Affiliation unknown	1
I Nyoman Prayana Trisna	Udayana University	1
Muhammad Auzan	Universitas Gadjah Mada	1
Novendri Isra Asriny	Islamic University of Indonesia	1
Ahmat Luthfi	Affiliation unknown	1

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 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Convolutional Neural Networks for Handwritten Javanese Character Recognition</dc:title>
	<dc:creator>Dewa, Chandra Kusuma</dc:creator>
	<dc:creator>Fadhilah, Amanda Lailatul</dc:creator>
	<dc:creator>Afiahayati, A</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science; Neural Networks</dc:subject>
	<dc:subject xml:lang="en-US">convolutional neural network; handwritten character recognition; Javanese character recognition</dc:subject>
	<dc:description xml:lang="en-US">Convolutional neural network (CNN) is state-of-the-art method in object recognition task. Specialized for spatial input data type, CNN has special convolutional and pooling layers which enable hierarchical feature learning from the input space. For offline handwritten character recognition problem such as classifying character in MNIST database, CNN shows better classification result than any other methods. By leveraging the advantages of CNN over character recognition task, in this paper we developed a software which utilizes digital image processing methods and CNN module for offline handwritten Javanese character recognition. The software performs image segmentation process using contour and Canny edge detection with OpenCV library over captured handwritten Javanese character image. CNN will classify the segmented image into 20 classes of Javanese letters. For evaluation purposes, we compared CNN to multilayer perceptron (MLP) on classification accuracy and training time. Experiment results show that CNN model testing accuracy outperforms MLP accuracy although CNN needs more training time than MLP.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Directorate of Research and Community Service, Universitas Islam Indonesia (DPPM-UII)</dc:contributor>
	<dc:date>2018-01-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/31144</dc:identifier>
	<dc:identifier>10.22146/ijccs.31144</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 12, No 1 (2018): January; 83-94</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/31144/19951</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 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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