@inproceedings{39ba8c629edf4ef7bcf65a3b334e351a,
title = "Detection of Clickbait Thumbnails on YouTube Using Tesseract-OCR, Face Recognition, and Text Alteration",
abstract = "The combination of thumbnails and titles on the video-sharing website is effective for providing an overview of the content of the videos. But there are also clickbait thumbnails purposely designed to lure people into watching the videos, sometimes even misleading with the agenda of building a particular opinion. Clickbait thumbnails usually consist of a composition of photos of people and narrative text. OCR is generally used to detect text in images of similar objects, while face recognition is applied to identify people in photos. In this research, we implement OCR and face recognition to detect clickbait thumbnails. We use SVM Model to process the implementation results. Using dataset consist of 250 thumbnails, resulting in an accuracy value of 0.968, a sensitivity value of 0.968, a precision value of 0.9698, and an F1-Score of 0.9678.",
keywords = "Face Recognition, OCR, Tesseract, Text Alteration, YouTube",
author = "Agastya Vitadhani and Kalamullah Ramli and {Dewi Purnamasari}, Prima",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Artificial Intelligence and Computer Science Technology, ICAICST 2021 ; Conference date: 29-06-2021",
year = "2021",
month = jun,
day = "29",
doi = "10.1109/ICAICST53116.2021.9497811",
language = "English",
series = "ICAICST 2021 - 2021 International Conference on Artificial Intelligence and Computer Science Technology",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "56--61",
booktitle = "ICAICST 2021 - 2021 International Conference on Artificial Intelligence and Computer Science Technology",
address = "United States",
}