  Scene text recognition has witnessed rapid development with the advance of
convolutional neural networks. Nonetheless, most of the previous methods may
not work well in recognizing text with low resolution which is often seen in
natural scene images. An intuitive solution is to introduce super-resolution
techniques as pre-processing. However, conventional super-resolution methods in
the literature mainly focus on reconstructing the detailed texture of natural
images, which typically do not work well for text due to the unique
characteristics of text. To tackle these problems, in this work, we propose a
content-aware text super-resolution network to generate the information desired
for text recognition. In particular, we design an end-to-end network that can
perform super-resolution and text recognition simultaneously. Different from
previous super-resolution methods, we use the loss of text recognition as the
Text Perceptual Loss to guide the training of the super-resolution network, and
thus it pays more attention to the text content, rather than the irrelevant
background area. Extensive experiments on several challenging benchmarks
demonstrate the effectiveness of our proposed method in restoring a sharp
high-resolution image from a small blurred one, and show that the recognition
performance clearly boosts up the performance of text recognizer. To our
knowledge, this is the first work focusing on text super-resolution. Code will
be released in https://github.com/xieenze/TextSR.
