  Existing object detection-based text detectors mainly concentrate on
detecting horizontal and multioriented text. However, they do not pay enough
attention to complex-shape text (curved or other irregularly shaped text).
Recently, segmentation-based text detection methods have been introduced to
deal with the complex-shape text; however, the pixel level processing increases
the computational cost significantly. To further improve the accuracy and
efficiency, we propose a novel detection framework for arbitrary-shape text
detection, termed as RayNet. RayNet uses Center Point Set (CPS) and Ray
Distance (RD) to fit text, where CPS is used to determine the text general
position and the RD is combined with CPS to compute Ray Points (RP) to localize
the text accurate shape. Since RP are disordered, we develop the Ray Points
Connection (RPC) algorithm to reorder RP, which significantly improves the
detection performance of complex-shape text. RayNet achieves impressive
performance on existing curved text dataset (CTW1500) and quadrangle text
dataset (ICDAR2015), which demonstrate its superiority against several
state-of-the-art methods.
