  Reading irregular scene text of arbitrary shape in natural images is still a
challenging problem, despite the progress made recently. Many existing
approaches incorporate sophisticated network structures to handle various
shapes, use extra annotations for stronger supervision, or employ hard-to-train
recurrent neural networks for sequence modeling. In this work, we propose a
simple yet strong approach for scene text recognition. With no need to convert
input images to sequence representations, we directly connect two-dimensional
CNN features to an attention-based sequence decoder which guided by holistic
representation. The holistic representation can guide the attention-based
decoder focus on more accurate area. As no recurrent module is adopted, our
model can be trained in parallel. It achieves 1.5x to 9.4x acceleration to
backward pass and 1.3x to 7.9x acceleration to forward pass, compared with the
RNN counterparts. The proposed model is trained with only word-level
annotations. With this simple design, our method achieves state-of-the-art or
competitive recognition performance on the evaluated regular and irregular
scene text benchmark datasets.
