Paper Info
Title
Using Robust Regression to Find Font Usage Trends
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.
Year
DOI
Venue
2021
10.1007/978-3-030-86159-9_9
DOCUMENT ANALYSIS AND RECOGNITION, ICDAR 2021, PT II
Keywords
DocType
Volume
Font analysis, Year estimation, Movie poster, Regression neural network, Tukey's biweight loss
Conference
12917
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Kaigen Tsuji100.34
Seiichi Uchida2790105.59
Brian Kenji Iwana3104.24