DRAW: Deep networks for Recognizing styles of Artists Who illustrate children's books

TL;DR

This paper develops deep learning methods to recognize and transfer illustrators' styles in children's book images, achieving high accuracy and identifying key stylistic features.

Contribution

It introduces a new dataset of children's book illustrations and demonstrates deep networks' effectiveness in style classification and transfer tasks.

Findings

94% classification accuracy on illustrators

Deep networks outperform traditional methods by over 10%

Successful style transfer and identification of stylistic elements

Abstract

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.