TY - GEN
T1 - Using Robust Regression to Find Font Usage Trends
AU - Tsuji, Kaigen
AU - Uchida, Seiichi
AU - Iwana, Brian Kenji
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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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U2 - 10.1007/978-3-030-86159-9_9
DO - 10.1007/978-3-030-86159-9_9
M3 - Conference contribution
AN - SCOPUS:85115317580
SN - 9783030861582
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 126
EP - 141
BT - Document Analysis and Recognition – ICDAR 2021 Workshops - Proceedings
A2 - Barney Smith, Elisa H.
A2 - Pal, Umapada
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Workshops co-located with the 16th International Conference on Document Analysis and Recognition, ICDAR 2021
Y2 - 5 September 2021 through 10 September 2021
ER -