Font Impression Estimation in the Wild
This work addresses the challenge of subjective font impression annotation for designers and researchers, but it is incremental as it builds on existing CNN frameworks with specific adaptations.
The paper tackles the problem of estimating font impressions from real-world font images by proposing an exemplar-based approach and training a CNN on synthetic images to handle missing and noisy annotations. The method enables a correlation analysis between book genres and font impressions, revealing trends that support the hypothesis that designers choose fonts based on their impressions.
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.