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.
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