Smarttext: Learning To Generate Harmonious Textual Layout Over Natural Image

Abstract

Automatic typography is important because it helps designers avoid highly repetitive tasks and amateur users achieve high-quality textual layout designs. However, there are often many parameters that need to be adjusted in automatic typography work. In this paper, we propose an efficient content-aware learning-based framework to generate harmonious textual layout over natural image. Our method incorporates both semantic features and visual perception principles. First, we combine a semantic visual saliency detection network with diffusion equations and a text-region proposal algorithm to generate candidate text anchors with various positions and sizes. Second, we develop a deep scoring network to assess the aesthetic quality of the candidate results. We design multiple evaluations to compare our method with several baselines and a commercial poster design tool. The results demonstrate that our method can generate harmonious textual layout in various actual scenarios with better performance.