  The purpose of this paper is to reveal the ability that Convolutional Neural
Networks (CNN) have on the novel task of image-to-image language conversion. We
propose a new network to tackle this task by converting images of Korean Hangul
characters directly into images of the phonetic Latin character equivalent. The
conversion rules between Hangul and the phonetic symbols are not explicitly
provided. The results of the proposed network show that it is possible to
perform image-to-image language conversion. Moreover, it shows that it can
grasp the structural features of Hangul even from limited learning data. In
addition, it introduces a new network to use when the input and output have
significantly different features.
