  Scene text detection has witnessed rapid development in recent years.
However, there still exists two main challenges: 1) many methods suffer from
false positives in their text representations; 2) the large scale variance of
scene texts makes it hard for network to learn samples. In this paper, we
propose the ContourNet, which effectively handles these two problems taking a
further step toward accurate arbitrary-shaped text detection. At first, a
scale-insensitive Adaptive Region Proposal Network (Adaptive-RPN) is proposed
to generate text proposals by only focusing on the Intersection over Union
(IoU) values between predicted and ground-truth bounding boxes. Then a novel
Local Orthogonal Texture-aware Module (LOTM) models the local texture
information of proposal features in two orthogonal directions and represents
text region with a set of contour points. Considering that the strong
unidirectional or weakly orthogonal activation is usually caused by the
monotonous texture characteristic of false-positive patterns (e.g. streaks.),
our method effectively suppresses these false positives by only outputting
predictions with high response value in both orthogonal directions. This gives
more accurate description of text regions. Extensive experiments on three
challenging datasets (Total-Text, CTW1500 and ICDAR2015) verify that our method
achieves the state-of-the-art performance. Code is available at
https://github.com/wangyuxin87/ContourNet.
