Character-independent font identification

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationDocument Analysis Systems - 14th IAPR International Workshop, DAS 2020, Proceedings
EditorsXiang Bai, Dimosthenis Karatzas, Daniel Lopresti
Number of pages15
ISBN (Print)9783030570576
Publication statusPublished - 2020
Event14th IAPR International Workshop on Document Analysis Systems, DAS 2020 - Wuhan, China
Duration: Jul 26 2020Jul 29 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12116 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th IAPR International Workshop on Document Analysis Systems, DAS 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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