  Deep learning-based scene text detection methods have progressed
substantially over the past years. However, there remain several problems to be
solved. Generally, long curve text instances tend to be fragmented because of
the limited receptive field size of CNN. Besides, simple representations using
rectangle or quadrangle bounding boxes fall short when dealing with more
challenging arbitrary-shaped texts. In addition, the scale of text instances
varies greatly which leads to the difficulty of accurate prediction through a
single segmentation network. To address these problems, we innovatively propose
a two-stage segmentation based arbitrary text detector named \textit{NASK}
(\textbf{N}eed \textbf{A} \textbf{S}econd loo\textbf{K}). Specifically,
\textit{NASK} consists of a Text Instance Segmentation network namely
\textit{TIS} (\(1^{st}\) stage), a Text RoI Pooling module and a Fiducial pOint
eXpression module termed as \textit{FOX} (\(2^{nd}\) stage). Firstly,
\textit{TIS} conducts instance segmentation to obtain rectangle text proposals
with a proposed Group Spatial and Channel Attention module (\textit{GSCA}) to
augment the feature expression. Then, Text RoI Pooling transforms these
rectangles to the fixed size. Finally, \textit{FOX} is introduced to
reconstruct text instances with a more tighter representation using the
predicted geometrical attributes including text center line, text line
orientation, character scale and character orientation. Experimental results on
two public benchmarks including \textit{Total-Text} and \textit{SCUT-CTW1500}
have demonstrated that the proposed \textit{NASK} achieves state-of-the-art
results.
