  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.
