  Inspired by speech recognition, recent state-of-the-art algorithms mostly
consider scene text recognition as a sequence prediction problem. Though
achieving excellent performance, these methods usually neglect an important
fact that text in images are actually distributed in two-dimensional space. It
is a nature quite different from that of speech, which is essentially a
one-dimensional signal. In principle, directly compressing features of text
into a one-dimensional form may lose useful information and introduce extra
noise. In this paper, we approach scene text recognition from a two-dimensional
perspective. A simple yet effective model, called Character Attention Fully
Convolutional Network (CA-FCN), is devised for recognizing the text of
arbitrary shapes. Scene text recognition is realized with a semantic
segmentation network, where an attention mechanism for characters is adopted.
Combined with a word formation module, CA-FCN can simultaneously recognize the
script and predict the position of each character. Experiments demonstrate that
the proposed algorithm outperforms previous methods on both regular and
irregular text datasets. Moreover, it is proven to be more robust to imprecise
localizations in the text detection phase, which are very common in practice.
