  In this paper, we propose a novel method called Rotational Region CNN (R2CNN)
for detecting arbitrary-oriented texts in natural scene images. The framework
is based on Faster R-CNN [1] architecture. First, we use the Region Proposal
Network (RPN) to generate axis-aligned bounding boxes that enclose the texts
with different orientations. Second, for each axis-aligned text box proposed by
RPN, we extract its pooled features with different pooled sizes and the
concatenated features are used to simultaneously predict the text/non-text
score, axis-aligned box and inclined minimum area box. At last, we use an
inclined non-maximum suppression to get the detection results. Our approach
achieves competitive results on text detection benchmarks: ICDAR 2015 and ICDAR
2013.
