  This paper introduces a novel rotation-based framework for arbitrary-oriented
text detection in natural scene images. We present the Rotation Region Proposal
Networks (RRPN), which are designed to generate inclined proposals with text
orientation angle information. The angle information is then adapted for
bounding box regression to make the proposals more accurately fit into the text
region in terms of the orientation. The Rotation Region-of-Interest (RRoI)
pooling layer is proposed to project arbitrary-oriented proposals to a feature
map for a text region classifier. The whole framework is built upon a
region-proposal-based architecture, which ensures the computational efficiency
of the arbitrary-oriented text detection compared with previous text detection
systems. We conduct experiments using the rotation-based framework on three
real-world scene text detection datasets and demonstrate its superiority in
terms of effectiveness and efficiency over previous approaches.
