On the Ability of a CNN to Realize ImagetoImage Language Conversion The purpose of this paper is to reveal the ability that Convolutional Neural Networks (CNN) have on the novel task of imagetoimage 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 imagetoimage 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.

Selective SuperResolution for Scene Text Images In this paper, we realize the enhancement of superresolution using images with scene text. Specifically, this paper proposes the use of SuperResolution Convolutional Neural Networks (SRCNN) which are constructed to tackle issues associated with characters and text. We demonstrate that standard SRCNNs trained for general object superresolution is not sufficient and that the proposed method is a viable method in creating a robust model for text. To do so, we analyze the characteristics of SRCNNs through quantitative and qualitative evaluations with scene text data. In addition, analysis using the correlation between layers by Singular Vector Canonical Correlation Analysis (SVCCA) and comparison of filters of each SRCNN using tSNE is performed. Furthermore, in order to create a unified superresolution model specialized for both text and objects, a model using SRCNNs trained with the different data types and Contentwise Network Fusion (CNF) is used. We integrate the SRCNN trained for character images and then SRCNN trained for general object images, and verify the accuracy improvement of scene images which include text. We also examine how each SRCNN affects superresolution images after fusion.