Famous Companies Use More Letters in Logo: A Large-Scale Analysis of Text Area in Logo

Shintaro Nishi, Takeaki Kadota, Seiichi Uchida

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

This paper analyzes a large number of logo images from the LLD-logo dataset, by recent deep learning-based techniques, to understand not only design trends of logo images and but also the correlation to their owner company. Especially, we focus on three correlations between logo images and their text areas, between the text areas and the number of followers on Twitter, and between the logo images and the number of followers. Various findings include the weak positive correlation between the text area ratio and the number of followers of the company. In addition, deep regression and deep ranking methods can catch correlations between the logo images and the number of followers.

本文言語英語
ホスト出版物のタイトルDocument Analysis and Recognition – ICDAR 2021 Workshops - Proceedings
編集者Elisa H. Barney Smith, Umapada Pal
出版社Springer Science and Business Media Deutschland GmbH
ページ97-111
ページ数15
ISBN(印刷版)9783030861971
DOI
出版ステータス出版済み - 2021
イベントInternational Workshops co-located with the 16th International Conference on Document Analysis and Recognition, ICDAR 2021 - Lausanne, スイス
継続期間: 9 5 20219 10 2021

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12916 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議International Workshops co-located with the 16th International Conference on Document Analysis and Recognition, ICDAR 2021
国/地域スイス
CityLausanne
Period9/5/219/10/21

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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