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Previous scene text detection methods have progressed substantially over thepast years. However, limited by the receptive field of CNNs and the simplerepresentations like rectangle bounding box or quadrangle adopted to describetext, previous methods may fall short when dealing with more challenging textinstances, such as extremely long text and arbitrarily shaped text. To addressthese two problems, we present a novel text detector namely LOMO, whichlocalizes the text progressively for multiple times (or in other word, LOokMore than Once). LOMO consists of a direct regressor (DR), an iterativerefinement module (IRM) and a shape expression module (SEM). At first, textproposals in the form of quadrangle are generated by DR branch. Next, IRMprogressively perceives the entire long text by iterative refinement based onthe extracted feature blocks of preliminary proposals. Finally, a SEM isintroduced to reconstruct more precise representation of irregular text byconsidering the geometry properties of text instance, including text region,text center line and border offsets. The state-of-the-art results on severalpublic benchmarks including ICDAR2017-RCTW, SCUT-CTW1500, Total-Text, ICDAR2015and ICDAR17-MLT confirm the striking robustness and effectiveness of LOMO.

Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes