Arbitrary-Oriented Scene Text Detection via Rotation Proposals

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 is 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 orientation. The Rotation Region-of-Interest (RRoI) pooling layer is proposed to project arbitrary-oriented proposals to the feature map for a text region classifier. The whole framework is built upon the Faster-RCNN architecture, which ensures the computational efficiency of the arbitrary-oriented text detection comparing 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.