Using Robust Regression to Find Font Usage Trends
This work addresses font trend analysis for designers and historians, but it is incremental as it applies existing regression techniques to a new dataset.
The paper tackled the problem of identifying font usage trends over time by using robust regression on a large collection of movie poster text images, achieving a method that estimates release years with improved accuracy through a hybrid loss function.
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.