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Jurnal SINTA 2

LoVi App: Android Application-based Image Classification for Low Vision

Journal of ICT Research and Applications (JICTRA) · Vol. 18 No. 2 (2024) · pp. 108–129

DOI
10.5614/itbj.ict.res.appl.2023.18.2.3
ISSN
2338-5499, 2337-5787
Published
2024-09-30
Publisher
DRI - ITB
Language
eng

Abstract

In Indonesia, many people with visual impairments are drawing public attention to their rights as fellow humans. One of the limitations that individuals with low vision face is their ability to recognize objects and navigate their surroundings due to difficulties in visual perception. In this modern era, deep learning technologies, especially in image classification, can help people with low vision overcome these challenges. In this paper, we discuss a deep learning system that optimizes image classification on users' smartphones to enhance visual support for individuals with low vision. We present an Android-based app, LoVi, designed to assist users with low vision. Powered by core systems within Sherpa models (TrotoarNet, IndoorNet, and CurrencyNet), LoVi has three modes: outdoor, indoor, and currency. The LoVi application provides over 80% accuracy for navigation on sidewalks, indoor object recognition, and currency identification. TrotoarNet aids in sidewalk navigation, IndoorNet assists with indoor object identification, and CurrencyNet recognizes Rupiah banknotes. Additionally, low-vision users can receive voice feedback for further accessibility.

Keywords

Collaboration network of the authors

13 authors · 43 co-authorship links · 25 across different organisations

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

  • Bandung Institute of Technology
  • Universitas Sumatera Utara
  • Universitas Airlangga
  • Institut Teknologi Sepuluh Nopember
  • Institute of Industrial Science, The University of Tokyo
  • Andalas University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Fajri Anugerah Pramadhana KornelBandung Institute of Technology1
Naufal Rafi HibatullahBandung Institute of Technology1
Fahmi FahmiUniversitas Sumatera Utara1
Erwin SutantoUniversitas Airlangga1
Muhammad YazidInstitut Teknologi Sepuluh Nopember1
Muhammad AzizInstitute of Industrial Science, The University of Tokyo1
Muhammad Imran HamidAndalas University1

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 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">LoVi App: Android Application-based Image Classification for Low Vision</dc:title>
	<dc:creator>Sofiyati, Mitra</dc:creator>
	<dc:creator>Wiranata, Fandi Azam</dc:creator>
	<dc:creator>Shalannanda, Wervyan</dc:creator>
	<dc:creator>Mulyana, Eueung</dc:creator>
	<dc:creator>Anshori, Isa</dc:creator>
	<dc:creator>Satriawan, Ardianto</dc:creator>
	<dc:subject xml:lang="en-US">convolutional neural network</dc:subject>
	<dc:subject xml:lang="en-US">deep learning</dc:subject>
	<dc:subject xml:lang="en-US">image classification</dc:subject>
	<dc:subject xml:lang="en-US">low vision</dc:subject>
	<dc:subject xml:lang="en-US">smartphone</dc:subject>
	<dc:description xml:lang="en-US">In Indonesia, many people with visual impairments are drawing public attention to their rights as fellow humans. One of the limitations that individuals with low vision face is their ability to recognize objects and navigate their surroundings due to difficulties in visual perception. In this modern era, deep learning technologies, especially in image classification, can help people with low vision overcome these challenges. In this paper, we discuss a deep learning system that optimizes image classification on users' smartphones to enhance visual support for individuals with low vision. We present an Android-based app, LoVi, designed to assist users with low vision. Powered by core systems within Sherpa models (TrotoarNet, IndoorNet, and CurrencyNet), LoVi has three modes: outdoor, indoor, and currency. The LoVi application provides over 80% accuracy for navigation on sidewalks, indoor object recognition, and currency identification. TrotoarNet aids in sidewalk navigation, IndoorNet assists with indoor object identification, and CurrencyNet recognizes Rupiah banknotes. Additionally, low-vision users can receive voice feedback for further accessibility.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2024-09-30</dc:date>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/22132</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2023.18.2.3</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 18 No. 2 (2024); 108-129</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2023.18.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/22132/6770</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Journal of ICT Research and Applications</dc:rights>
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