  Reading text in the wild is a challenging task in the field of computer
vision. Existing approaches mainly adopted Connectionist Temporal
Classification (CTC) or Attention models based on Recurrent Neural Network
(RNN), which is computationally expensive and hard to train. In this paper, we
present an end-to-end Attention Convolutional Network for scene text
recognition. Firstly, instead of RNN, we adopt the stacked convolutional layers
to effectively capture the contextual dependencies of the input sequence, which
is characterized by lower computational complexity and easier parallel
computation. Compared to the chain structure of recurrent networks, the
Convolutional Neural Network (CNN) provides a natural way to capture long-term
dependencies between elements, which is 9 times faster than Bidirectional Long
Short-Term Memory (BLSTM). Furthermore, in order to enhance the representation
of foreground text and suppress the background noise, we incorporate the
residual attention modules into a small densely connected network to improve
the discriminability of CNN features. We validate the performance of our
approach on the standard benchmarks, including the Street View Text, IIIT5K and
ICDAR datasets. As a result, state-of-the-art or highly-competitive performance
and efficiency show the superiority of the proposed approach.
