A Neural Network Approach to Derive the Horizontal Spaces in Typefaces

Typeface spacing is a hard problem. It takes countless hours of manual labour to achieve an aesthetically pleasing font one frequently encounters in digital media. Inter-letter spacing defines the texture and the feel of a typeface and when done accurately yields an aesthetically balanced and an appealing typeface. Nevertheless, setting spacing in a typeface is a tedious and a time consuming task. Hence this paper presents an exploratory study investigating the potential of Neural Networks (NN) to fully automate the typeface spacing process. Even though the NN models investigated in this study yielded up to an accuracy of 47\% when compared with typefaces spaced by the type designers, the visual differences were subtle. Thus, we conclude that neural models can indeed be used to model the typeface spacing problem. As one of the first attempts to apply neural models in this particular problem domain, this study lays the foundation to future research and studies.