A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images
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 us...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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ITB Journal Publisher
2019-12-01
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| Series: | Journal of ICT Research and Applications |
| Subjects: | |
| Online Access: | http://journals.itb.ac.id/index.php/jictra/article/view/11896 |
| _version_ | 1818394520365039616 |
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| author | Bilal Mohammed Bataineh Mohd Khaled Yousef Shambour |
| author_facet | Bilal Mohammed Bataineh Mohd Khaled Yousef Shambour |
| author_sort | Bilal Mohammed Bataineh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T06:02:31Z |
| format | Article |
| id | doaj.art-85d8d8f5d0c64a6ab50eab0fbbcf51b7 |
| institution | Directory Open Access Journal |
| issn | 2337-5787 2338-5499 |
| language | English |
| last_indexed | 2024-12-14T06:02:31Z |
| publishDate | 2019-12-01 |
| publisher | ITB Journal Publisher |
| record_format | Article |
| series | Journal of ICT Research and Applications |
| spelling | doaj.art-85d8d8f5d0c64a6ab50eab0fbbcf51b72022-12-21T23:14:23ZengITB Journal PublisherJournal of ICT Research and Applications2337-57872338-54992019-12-0113310.5614/itbj.ict.res.appl.2019.13.3.2A Robust Algorithm for Emoji Detection in Smartphone Screenshot ImagesBilal Mohammed Bataineh0Mohd Khaled Yousef Shambour1Umm al-Qura UniversityUmm al-Qura UniversityThe 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.http://journals.itb.ac.id/index.php/jictra/article/view/11896digital imagesemojirecognitionscreenshotstext |
| spellingShingle | Bilal Mohammed Bataineh Mohd Khaled Yousef Shambour A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images Journal of ICT Research and Applications digital images emoji recognition screenshots text |
| title | A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images |
| title_full | A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images |
| title_fullStr | A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images |
| title_full_unstemmed | A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images |
| title_short | A Robust Algorithm for Emoji Detection in Smartphone Screenshot Images |
| title_sort | robust algorithm for emoji detection in smartphone screenshot images |
| topic | digital images emoji recognition screenshots text |
| url | http://journals.itb.ac.id/index.php/jictra/article/view/11896 |
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