  Scene text recognition has been a hot research topic in computer vision due
to its various applications. The state of the art is the attention-based
encoder-decoder framework that learns the mapping between input images and
output sequences in a purely data-driven way. However, we observe that existing
attention-based methods perform poorly on complicated and/or low-quality
images. One major reason is that existing methods cannot get accurate
alignments between feature areas and targets for such images. We call this
phenomenon "attention drift". To tackle this problem, in this paper we propose
the FAN (the abbreviation of Focusing Attention Network) method that employs a
focusing attention mechanism to automatically draw back the drifted attention.
FAN consists of two major components: an attention network (AN) that is
responsible for recognizing character targets as in the existing methods, and a
focusing network (FN) that is responsible for adjusting attention by evaluating
whether AN pays attention properly on the target areas in the images.
Furthermore, different from the existing methods, we adopt a ResNet-based
network to enrich deep representations of scene text images. Extensive
experiments on various benchmarks, including the IIIT5k, SVT and ICDAR
datasets, show that the FAN method substantially outperforms the existing
methods.
