Typeface network and the principle of font pairing.

ABSTRACT: In a field traditionally driven by intuition and subjective judgment, this study presents a data-driven approach to typography, the art of arranging text. Leveraging a comprehensive dataset of font-use cases across diverse mediums, we employed Non-negative Matrix Factorization to extract three fundamental morphological characteristics of fonts: Serif vs. Sans-Serif, Basic vs. Decorative letterforms, and Light vs. Bold. This analysis demonstrated that different mediums preferentially utilize fonts with distinct morphological features. We also predicted variations between single and paired fonts, contrasting these findings with random pairings from several null models, to identify unique font-pairing trends across various mediums. Furthermore, we utilized a network analysis approach to ident

SUBMITTER: Choi J 

PROVIDER: S-EPMC11681221 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature