Font Impression Estimation in the Wild
Kazuki Kitajima Daichi Haraguchi Seiichi Uchida
This paper addresses the challenging task of estimating font impressions from
real font images. We use a font dataset with annotation about font impressions
and a convolutional neural network (CNN) framework for this task. However,
impressions attached to individual fonts are often missing and noisy because of
the subjective characteristic of font impression annotation. To realize stable
impression estimation even with such a dataset, we propose an exemplar-based
impression estimation approach, which relies on a strategy of ensembling
impressions of exemplar fonts that are similar to the input image. In addition,
we train CNN with synthetic font images that mimic scanned word images so that
CNN estimates impressions of font images in the wild. We evaluate the basic
performance of the proposed estimation method quantitatively and qualitatively.
Then, we conduct a correlation analysis between book genres and font
impressions on real book cover images; it is important to note that this
analysis is only possible with our impression estimation method. The analysis
reveals various trends in the correlation between them - this fact supports a
hypothesis that book cover designers carefully choose a font for a book cover
considering the impression given by the font.