  Low-resolution text images are often seen in natural scenes such as documents
captured by mobile phones. Recognizing low-resolution text images is
challenging because they lose detailed content information, leading to poor
recognition accuracy. An intuitive solution is to introduce super-resolution
(SR) techniques as pre-processing. However, previous single image
super-resolution (SISR) methods are trained on synthetic low-resolution images
(e.g.Bicubic down-sampling), which is simple and not suitable for real
low-resolution text recognition. To this end, we pro-pose a real scene text SR
dataset, termed TextZoom. It contains paired real low-resolution and
high-resolution images which are captured by cameras with different focal
length in the wild. It is more authentic and challenging than synthetic data,
as shown in Fig. 1. We argue improv-ing the recognition accuracy is the
ultimate goal for Scene Text SR. In this purpose, a new Text Super-Resolution
Network termed TSRN, with three novel modules is developed. (1) A sequential
residual block is proposed to extract the sequential information of the text
images. (2) A boundary-aware loss is designed to sharpen the character
boundaries. (3) A central alignment module is proposed to relieve the
misalignment problem in TextZoom. Extensive experiments on TextZoom demonstrate
that our TSRN largely improves the recognition accuracy by over 13%of CRNN, and
by nearly 9.0% of ASTER and MORAN compared to synthetic SR data. Furthermore,
our TSRN clearly outperforms 7 state-of-the-art SR methods in boosting the
recognition accuracy of LR images in TextZoom. For example, it outperforms
LapSRN by over 5% and 8%on the recognition accuracy of ASTER and CRNN. Our
results suggest that low-resolution text recognition in the wild is far from
being solved, thus more research effort is needed.
