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Detecting irregular-shaped text instances is the main challenge for textdetection. Existing approaches can be roughly divided into top-down andbottom-up perspective methods. The former encodes text contours into unifiedunits, which always fails to fit highly curved text contours. The latterrepresents text instances by a number of local units, where the complicatednetwork and post-processing lead to slow detection speed. In this paper, todetect arbitrary-shaped text instances with high detection accuracy and speedsimultaneously, we propose a \textbf{Bi}directional \textbf{P}erspectivestrategy based \textbf{Net}work (BiP-Net). Specifically, a new textrepresentation strategy is proposed to represent text contours from a top-downperspective, which can fit highly curved text contours effectively. Moreover, acontour connecting (CC) algorithm is proposed to avoid the information loss oftext contours by rebuilding interval contours from a bottom-up perspective. Theexperimental results on MSRA-TD500, CTW1500, and ICDAR2015 datasets demonstratethe superiority of BiP-Net against several state-of-the-art methods.

BiP-Net: Bidirectional Perspective Strategy based Arbitrary-Shaped Text Detection Network