  Previous scene text detection methods have progressed substantially over the
past years. However, limited by the receptive field of CNNs and the simple
representations like rectangle bounding box or quadrangle adopted to describe
text, previous methods may fall short when dealing with more challenging text
instances, such as extremely long text and arbitrarily shaped text. To address
these two problems, we present a novel text detector namely LOMO, which
localizes the text progressively for multiple times (or in other word, LOok
More than Once). LOMO consists of a direct regressor (DR), an iterative
refinement module (IRM) and a shape expression module (SEM). At first, text
proposals in the form of quadrangle are generated by DR branch. Next, IRM
progressively perceives the entire long text by iterative refinement based on
the extracted feature blocks of preliminary proposals. Finally, a SEM is
introduced to reconstruct more precise representation of irregular text by
considering the geometry properties of text instance, including text region,
text center line and border offsets. The state-of-the-art results on several
public benchmarks including ICDAR2017-RCTW, SCUT-CTW1500, Total-Text, ICDAR2015
and ICDAR17-MLT confirm the striking robustness and effectiveness of LOMO.
