Exploring the world of fonts for discovering the most standard fonts and the missing fonts

Seiichi Uchida, Yuji Egashira, Kota Sato

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

9 Citations (Scopus)

Abstract

This paper has two contributions toward understanding the principles in font design. The first contribution of this paper is to discover the most standard font shape of each letter class by analyzing thousands of different fonts. For this analysis, two different methods are used. The first method is congealing for aligning multiple images based on a nonlinear geometric transformation model. The average of the aligned image is considered as a standard font shape. The second method is network analysis for representing font variations as a large-scale relative neighborhood graph (RNG) and then finding its center. The font corresponding to the center is considered as the standard font shape. Both of the standard font shapes given by the two methods are plain without decoration, serif, or slant, and thus give an objective reason why we consider the plain font as the typical font shape. The second contribution is to utilize the RNG and the pairwise congealing technique for discovering unexplored font designs and then generating totally new fonts automatically.

Original languageEnglish
Title of host publication13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
PublisherIEEE Computer Society
Pages441-445
Number of pages5
ISBN (Electronic)9781479918058
DOIs
Publication statusPublished - Nov 20 2015
Event13th International Conference on Document Analysis and Recognition, ICDAR 2015 - Nancy, France
Duration: Aug 23 2015Aug 26 2015

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2015-November
ISSN (Print)1520-5363

Other

Other13th International Conference on Document Analysis and Recognition, ICDAR 2015
CountryFrance
CityNancy
Period8/23/158/26/15

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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