Evolving Stencils for Typefaces: Combining Machine Learning, User’s Preferences and Novelty

Abstract

Typefaces have become an essential resource used by graphic designs to communicate. Some designers opt to create their own typefaces or custom lettering that better suits each design project. This increases the demand for novelty in type design, and consequently the need for good technological means to explore new thinking and approaches in the design of typefaces. In this work, we continue our research on the automatic evolution of glyphs (letterforms or designs of characters). We present an evolutionary framework for the automatic generation of type stencils based on fitness functions designed by the user. The proposed framework comprises two modules: the evolutionary system, and the fitness function design interface. The first module, the evolutionary system, operates a Genetic Algorithm, with a novelty search mechanism, and the fitness assignment scheme. The second module, the fitness function design interface, enables the users to create fitness functions through a responsive graphical interface, by indicating the desired values and weights of a set of behavioural features, based on machine learning approaches, and morphological features. The experimental results reveal the wide variety of type stencils and glyphs that can be evolved with the presented framework and show how the design of fitness functions influences the outcomes, which are able to convey the preferences expressed by the user. The creative possibilities created with the outcomes of the presented framework are explored by using one evolved stencil in a design project. This research demonstrates how Evolutionary Computation and Machine Learning may address challenges in type design and expand the tools for the creation of typefaces.