  Irregular scene text, which has complex layout in 2D space, is challenging to
most previous scene text recognizers. Recently, some irregular scene text
recognizers either rectify the irregular text to regular text image with
approximate 1D layout or transform the 2D image feature map to 1D feature
sequence. Though these methods have achieved good performance, the robustness
and accuracy are still limited due to the loss of spatial information in the
process of 2D to 1D transformation. Different from all of previous, we in this
paper propose a framework which transforms the irregular text with 2D layout to
character sequence directly via 2D attentional scheme. We utilize a relation
attention module to capture the dependencies of feature maps and a parallel
attention module to decode all characters in parallel, which make our method
more effective and efficient. Extensive experiments on several public
benchmarks as well as our collected multi-line text dataset show that our
approach is effective to recognize regular and irregular scene text and
outperforms previous methods both in accuracy and speed.
