  Scene text detection has been made great progress in recent years. The
detection manners are evolving from axis-aligned rectangle to rotated rectangle
and further to quadrangle. However, current datasets contain very little curve
text, which can be widely observed in scene images such as signboard, product
name and so on. To raise the concerns of reading curve text in the wild, in
this paper, we construct a curve text dataset named CTW1500, which includes
over 10k text annotations in 1,500 images (1000 for training and 500 for
testing). Based on this dataset, we pioneering propose a polygon based curve
text detector (CTD) which can directly detect curve text without empirical
combination. Moreover, by seamlessly integrating the recurrent transverse and
longitudinal offset connection (TLOC), the proposed method can be end-to-end
trainable to learn the inherent connection among the position offsets. This
allows the CTD to explore context information instead of predicting points
independently, resulting in more smooth and accurate detection. We also propose
two simple but effective post-processing methods named non-polygon suppress
(NPS) and polygonal non-maximum suppression (PNMS) to further improve the
detection accuracy. Furthermore, the proposed approach in this paper is
designed in an universal manner, which can also be trained with rectangular or
quadrilateral bounding boxes without extra efforts. Experimental results on
CTW-1500 demonstrate our method with only a light backbone can outperform
state-of-the-art methods with a large margin. By evaluating only in the curve
or non-curve subset, the CTD + TLOC can still achieve the best results. Code is
available at https://github.com/Yuliang-Liu/Curve-Text-Detector.
