  Scene text image contains two levels of contents: visual texture and semantic
information. Although the previous scene text recognition methods have made
great progress over the past few years, the research on mining semantic
information to assist text recognition attracts less attention, only RNN-like
structures are explored to implicitly model semantic information. However, we
observe that RNN based methods have some obvious shortcomings, such as
time-dependent decoding manner and one-way serial transmission of semantic
context, which greatly limit the help of semantic information and the
computation efficiency. To mitigate these limitations, we propose a novel
end-to-end trainable framework named semantic reasoning network (SRN) for
accurate scene text recognition, where a global semantic reasoning module
(GSRM) is introduced to capture global semantic context through multi-way
parallel transmission. The state-of-the-art results on 7 public benchmarks,
including regular text, irregular text and non-Latin long text, verify the
effectiveness and robustness of the proposed method. In addition, the speed of
SRN has significant advantages over the RNN based methods, demonstrating its
value in practical use.
