Using Robust Regression to Find Font Usage Trends

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

Abstract

Fonts have had trends throughout their history, not only in when they were invented but also in their usage and popularity. In this paper, we attempt to specifically find the trends in font usage using robust regression on a large collection of text images. We utilize movie posters as the source of fonts for this task because movie posters can represent time periods by using their release date. In addition, movie posters are documents that are carefully designed and represent a wide range of fonts. To understand the relationship between the fonts of movie posters and time, we use a regression Convolutional Neural Network (CNN) to estimate the release year of a movie using an isolated title text image. Due to the difficulty of the task, we propose to use of a hybrid training regimen that uses a combination of Mean Squared Error (MSE) and Tukey’s biweight loss. Furthermore, we perform a thorough analysis on the trends of fonts through time.

Original languageEnglish
Title of host publicationDocument Analysis and Recognition – ICDAR 2021 Workshops - Proceedings
EditorsElisa H. Barney Smith, Umapada Pal
PublisherSpringer Science and Business Media Deutschland GmbH
Pages126-141
Number of pages16
ISBN (Print)9783030861582
DOIs
Publication statusPublished - 2021
EventInternational Workshops co-located with the 16th International Conference on Document Analysis and Recognition, ICDAR 2021 - Lausanne, Switzerland
Duration: Sep 5 2021Sep 10 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12917 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshops co-located with the 16th International Conference on Document Analysis and Recognition, ICDAR 2021
Country/TerritorySwitzerland
CityLausanne
Period9/5/219/10/21

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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