  Widely used in news, business, and educational media, infographics are
handcrafted to effectively communicate messages about complex and often
abstract topics including `ways to conserve the environment' and `understanding
the financial crisis'. Composed of stylistically and semantically diverse
visual and textual elements, infographics pose new challenges for computer
vision. While automatic text extraction works well on infographics, computer
vision approaches trained on natural images fail to identify the stand-alone
visual elements in infographics, or `icons'. To bridge this representation gap,
we propose a synthetic data generation strategy: we augment background patches
in infographics from our Visually29K dataset with Internet-scraped icons which
we use as training data for an icon proposal mechanism. On a test set of 1K
annotated infographics, icons are located with 38% precision and 34% recall
(the best model trained with natural images achieves 14% precision and 7%
recall). Combining our icon proposals with icon classification and text
extraction, we present a multi-modal summarization application. Our application
takes an infographic as input and automatically produces text tags and visual
hashtags that are textually and visually representative of the infographic's
topics respectively.
