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.

Cascaded DetailPreserving Networks for SuperResolution of Document Images The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the lowresolution image is a common and challenging case. In this paper, we propose the cascaded networks for document image superresolution. Our model is composed by the DetailPreserving Networks with small magnification. The loss function with perceptual terms is designed to simultaneously preserve the original patterns and enhance the edge of the characters. These networks are trained with the same architecture and different parameters and then assembled into a pipeline model with a larger magnification. The lowresolution images can upscale gradually by passing through each DetailPreserving Network until the final highresolution images. Through extensive experiments on two scanning document image datasets, we demonstrate that the proposed approach outperforms recent stateoftheart image superresolution methods, and combining it with standard OCR system lead to signification improvements on the recognition results.