Using Robust Regression to Find Font Usage Trends
- URL: http://arxiv.org/abs/2106.15232v2
- Date: Wed, 30 Jun 2021 09:21:50 GMT
- Title: Using Robust Regression to Find Font Usage Trends
- Authors: Kaigen Tsuji, Seiichi Uchida, Brian Kenji Iwana
- Abstract summary: We use movie posters as the source of fonts for this task because movie posters can represent time periods by using their release date.
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.
Due to the difficulty of the task, we propose to use a hybrid training regimen that uses a combination of Mean Squared Error (MSE) and Tukey's biweight loss.
- Score: 8.5941401672901
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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