Scene Text Magnifier Scene text magnifier aims to magnify text in natural scene images without recognition. It could help the special groups, who have myopia or dyslexia to better understand the scene. In this paper, we design the scene text magnifier through interacted four CNNbased networks: character erasing, character extraction, character magnify, and image synthesis. The architecture of the networks are extended based on the hourglass encoderdecoders. It inputs the original scene text image and outputs the text magnified image while keeps the background unchange. Intermediately, we can get the sideoutput results of text erasing and text extraction. The four subnetworks are first trained independently and finetuned in endtoend mode. The training samples for each stage are processed through a flow with original image and text annotation in ICDAR2013 and Flickr dataset as input, and corresponding text erased image, magnified text annotation, and text magnified scene image as output. To evaluate the performance of text magnifier, the Structural Similarity is used to measure the regional changes in each character region. The experimental results demonstrate our method can magnify scene text effectively without effecting the background.

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.