  This paper is motivated from a young boy's capability to recognize an
illustrator's style in a totally different context. In the book "We are All
Born Free" [1], composed of selected rights from the Universal Declaration of
Human Rights interpreted by different illustrators, the boy was surprised to
see a picture similar to the ones in the "Winnie the Witch" series drawn by
Korky Paul (Figure 1). The style was noticeable in other characters of the same
illustrator in different books as well. The capability of a child to easily
spot the style was shown to be valid for other illustrators such as Axel
Scheffler and Debi Gliori. The boy's enthusiasm let us to start the journey to
explore the capabilities of machines to recognize the style of illustrators.
  We collected pages from children's books to construct a new illustrations
dataset consisting of about 6500 pages from 24 artists. We exploited deep
networks for categorizing illustrators and with around 94% classification
performance our method over-performed the traditional methods by more than 10%.
Going beyond categorization we explored transferring style. The classification
performance on the transferred images has shown the ability of our system to
capture the style. Furthermore, we discovered representative illustrations and
discriminative stylistic elements.
