  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 CNN-based networks: character erasing, character
extraction, character magnify, and image synthesis. The architecture of the
networks are extended based on the hourglass encoder-decoders. It inputs the
original scene text image and outputs the text magnified image while keeps the
background unchange. Intermediately, we can get the side-output results of text
erasing and text extraction. The four sub-networks are first trained
independently and fine-tuned in end-to-end 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.

Computer Vision and Pattern Recognition;Machine Learning;Machine Learning