  Designers need to consider not only perceptual effectiveness but also visual
styles when creating an infographic. This process can be difficult and time
consuming for professional designers, not to mention non-expert users, leading
to the demand for automated infographics design. As a first step, we focus on
timeline infographics, which have been widely used for centuries. We contribute
an end-to-end approach that automatically extracts an extensible timeline
template from a bitmap image. Our approach adopts a deconstruction and
reconstruction paradigm. At the deconstruction stage, we propose a multi-task
deep neural network that simultaneously parses two kinds of information from a
bitmap timeline: 1) the global information, i.e., the representation, scale,
layout, and orientation of the timeline, and 2) the local information, i.e.,
the location, category, and pixels of each visual element on the timeline. At
the reconstruction stage, we propose a pipeline with three techniques, i.e.,
Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an
extensible template from the infographic, by utilizing the deconstruction
results. To evaluate the effectiveness of our approach, we synthesize a
timeline dataset (4296 images) and collect a real-world timeline dataset (393
images) from the Internet. We first report quantitative evaluation results of
our approach over the two datasets. Then, we present examples of automatically
extracted templates and timelines automatically generated based on these
templates to qualitatively demonstrate the performance. The results confirm
that our approach can effectively extract extensible templates from real-world
timeline infographics.

Human-Computer Interaction;Graphics