Character-independent font identification

Daichi Haraguchi, Shota Harada, Brian Kenji Iwana, Yuto Shinahara, Seiichi Uchida

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

抄録

There are a countless number of fonts with various shapes and styles. In addition, there are many fonts that only have subtle differences in features. Due to this, font identification is a difficult task. In this paper, we propose a method of determining if any two characters are from the same font or not. This is difficult due to the difference between fonts typically being smaller than the difference between alphabet classes. Additionally, the proposed method can be used with fonts regardless of whether they exist in the training or not. In order to accomplish this, we use a Convolutional Neural Network (CNN) trained with various font image pairs. In the experiment, the network is trained on image pairs of various fonts. We then evaluate the model on a different set of fonts that are unseen by the network. The evaluation is performed with an accuracy of 92.27%. Moreover, we analyzed the relationship between character classes and font identification accuracy.

本文言語英語
ホスト出版物のタイトルDocument Analysis Systems - 14th IAPR International Workshop, DAS 2020, Proceedings
編集者Xiang Bai, Dimosthenis Karatzas, Daniel Lopresti
出版社Springer
ページ497-511
ページ数15
ISBN(印刷版)9783030570576
DOI
出版ステータス出版済み - 2020
イベント14th IAPR International Workshop on Document Analysis Systems, DAS 2020 - Wuhan, 中国
継続期間: 7 26 20207 29 2020

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)
12116 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議14th IAPR International Workshop on Document Analysis Systems, DAS 2020
国/地域中国
CityWuhan
Period7/26/207/29/20

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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