  Arbitrary-shaped text detection is a challenging task due to the complex
geometric layouts of texts such as large aspect ratios, various scales, random
rotations and curve shapes. Most state-of-the-art methods solve this problem
from bottom-up perspectives, seeking to model a text instance of complex
geometric layouts with simple local units (e.g., local boxes or pixels) and
generate detections with heuristic post-processings. In this work, we propose
an arbitrary-shaped text detection method, namely TextRay, which conducts
top-down contour-based geometric modeling and geometric parameter learning
within a single-shot anchor-free framework. The geometric modeling is carried
out under polar system with a bidirectional mapping scheme between shape space
and parameter space, encoding complex geometric layouts into unified
representations. For effective learning of the representations, we design a
central-weighted training strategy and a content loss which builds propagation
paths between geometric encodings and visual content. TextRay outputs simple
polygon detections at one pass with only one NMS post-processing. Experiments
on several benchmark datasets demonstrate the effectiveness of the proposed
approach. The code is available at https://github.com/LianaWang/TextRay.
