Typeface Generation Through Style Descriptions With Generative Models

Abstract

Typeface design plays a vital role in graphic and communication design. Different typefaces are suitable for different contexts and can convey different emotions and messages. Typeface design still relies on skilled designers to create unique styles for specific needs. Recently, generative adversarial networks (GANs) have been applied to typeface generation, but these methods face challenges due to the high annotation requirements of typeface generation datasets, which are difficult to obtain. Furthermore, machine-generated typefaces often fail to meet designers’ specific requirements, as dataset annotations limit the diversity of the generated typefaces. In response to these limitations in current typeface generation models, we propose an alternative approach to the task. Instead of relying on dataset-provided annotations to define the typeface style vector, we introduce a transformer-based language model to learn the mapping between a typeface style description and the corresponding style vector. We evaluated the proposed model using both existing and newly created style descriptions. Results indicate that the model can generate high-quality, patent-free typefaces based on the input style descriptions provided by designers.