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A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images

Journal of ICT Research and Applications (JICTRA) · Vol. 13 No. 3 (2019) · pp. 192–212

DOI
10.5614/itbj.ict.res.appl.2019.13.3.2
ISSN
2338-5499, 2337-5787
Published
2019-12-31
Publisher
DRI - ITB
Language
eng

Abstract

The increasing use of smartphones and social media apps for communication results in a massive number of screenshot images. These images enrich the written language through text and emojis. In this regard, several studies in the image analysis field have considered text. However, they ignored the use of emojis. In this study, a robust two-stage algorithm for detecting emojis in screenshot images is proposed. The first stage localizes the regions of candidate emojis by using the proposed RGB-channel analysis method followed by a connected component method with a set of proposed rules. In the second verification stage, each of the emojis and non-emojis are classified by using proposed features with a decision tree classifier. Experiments were conducted to evaluate each stage independently and assess the performance of the proposed algorithm completely by using a self-collected dataset. The results showed that the proposed RGB-channel analysis method achieved better performance than the Niblack and Sauvola methods. Moreover, the proposed feature extraction method with decision tree classifier achieved more satisfactory performance than the LBP feature extraction method with all Bayesian network, perceptron neural network, and decision table rules. Overall, the proposed algorithm exhibited high efficiency in detecting emojis in screenshot images.

Keywords

Collaboration network of the authors

7 authors · 16 co-authorship links · 1 across different organisations

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  • National University of Malaysia
  • Umm al-Qura University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Tarik Abu-AinNational University of Malaysia2
Siti Norul Huda Sheikh AbdullahNational University of Malaysia2
Khairuddin OmarNational University of Malaysia2
Ashraf Abu-EinAffiliation unknown1
Waleed Abdel karim Abu-ainNational University of Malaysia1

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">A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images</dc:title>
	<dc:creator>Bataineh, Bilal Mohammed</dc:creator>
	<dc:creator>Shambour, Mohd Khaled Yousef</dc:creator>
	<dc:subject xml:lang="en-US">digital images</dc:subject>
	<dc:subject xml:lang="en-US">emoji</dc:subject>
	<dc:subject xml:lang="en-US">recognition</dc:subject>
	<dc:subject xml:lang="en-US">screenshots</dc:subject>
	<dc:subject xml:lang="en-US">text</dc:subject>
	<dc:description xml:lang="en-US">The increasing use of smartphones and social media apps for communication results in a massive number of screenshot images. These images enrich the written language through text and emojis. In this regard, several studies in the image analysis field have considered text. However, they ignored the use of emojis. In this study, a robust two-stage algorithm for detecting emojis in screenshot images is proposed. The first stage localizes the regions of candidate emojis by using the proposed RGB-channel analysis method followed by a connected component method with a set of proposed rules. In the second verification stage, each of the emojis and non-emojis are classified by using proposed features with a decision tree classifier. Experiments were conducted to evaluate each stage independently and assess the performance of the proposed algorithm completely by using a self-collected dataset. The results showed that the proposed RGB-channel analysis method achieved better performance than the Niblack and Sauvola methods. Moreover, the proposed feature extraction method with decision tree classifier achieved more satisfactory performance than the LBP feature extraction method with all Bayesian network, perceptron neural network, and decision table rules. Overall, the proposed algorithm exhibited high efficiency in detecting emojis in screenshot images.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2019-12-31</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/11896</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2019.13.3.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 13 No. 3 (2019); 192-212</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/11896/4526</dc:relation>
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