Font Style that Fits an Image -- Font Generation Based on Image Context

TL;DR

This paper introduces an end-to-end neural network that generates stylized book title fonts fitting the context of a book cover, combining multiple neural components for effective style transfer.

Contribution

It presents a novel neural network architecture that generates contextually appropriate fonts for book covers, integrating style transfer and text skeleton prediction.

Findings

Effective font style generation for book covers

Quantitative and qualitative validation of results

Improved aesthetic fit of generated fonts

Abstract

When fonts are used on documents, they are intentionally selected by designers. For example, when designing a book cover, the typography of the text is an important factor in the overall feel of the book. In addition, it needs to be an appropriate font for the rest of the book cover. Thus, we propose a method of generating a book title image based on its context within a book cover. We propose an end-to-end neural network that inputs the book cover, a target location mask, and a desired book title and outputs stylized text suitable for the cover. The proposed network uses a combination of a multi-input encoder-decoder, a text skeleton prediction network, a perception network, and an adversarial discriminator. We demonstrate that the proposed method can effectively produce desirable and appropriate book cover text through quantitative and qualitative results.