Text logo design heavily relies on the creativity and expertise of
professional designers, in which arranging element layouts is one of the most
important procedures. However, few attention has been paid to this task which
needs to take many factors (e.g., fonts, linguistics, topics, etc.) into
consideration. In this paper, we propose a content-aware layout generation
network which takes glyph images and their corresponding text as input and
synthesizes aesthetic layouts for them automatically. Specifically, we develop
a dual-discriminator module, including a sequence discriminator and an image
discriminator, to evaluate both the character placing trajectories and rendered
shapes of synthesized text logos, respectively. Furthermore, we fuse the
information of linguistics from texts and visual semantics from glyphs to guide
layout prediction, which both play important roles in professional layout
design. To train and evaluate our approach, we construct a dataset named as
TextLogo3K, consisting of about 3,500 text logo images and their pixel-level
annotations. Experimental studies on this dataset demonstrate the effectiveness
of our approach for synthesizing visually-pleasing text logos and verify its
superiority against the state of the art.

本文介绍了一种基于内容感知布局生成网络的方法，将字形图像和对应文本作为输入，自动生成美观的排版布局。在设计中融合了语言学和视觉语义信息，同时训练和评估了文本 Logo3K 数据集，验证了其在合成视觉上令人愉悦的文本 Logo 方面的优越性。