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Scene text recognition has witnessed rapid development with the advance ofconvolutional neural networks. Nonetheless, most of the previous methods maynot work well in recognizing text with low resolution which is often seen innatural scene images. An intuitive solution is to introduce super-resolutiontechniques as pre-processing. However, conventional super-resolution methods inthe literature mainly focus on reconstructing the detailed texture of naturalimages, which typically do not work well for text due to the uniquecharacteristics of text. To tackle these problems, in this work, we propose acontent-aware text super-resolution network to generate the information desiredfor text recognition. In particular, we design an end-to-end network that canperform super-resolution and text recognition simultaneously. Different fromprevious super-resolution methods, we use the loss of text recognition as theText Perceptual Loss to guide the training of the super-resolution network, andthus it pays more attention to the text content, rather than the irrelevantbackground area. Extensive experiments on several challenging benchmarksdemonstrate the effectiveness of our proposed method in restoring a sharphigh-resolution image from a small blurred one, and show that the recognitionperformance clearly boosts up the performance of text recognizer. To ourknowledge, this is the first work focusing on text super-resolution. Code willbe released in https://github.com/xieenze/TextSR.

TextSR: Content-Aware Text Super-Resolution Guided by Recognition