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 this https URL

该研究提出了一种名为 TextRay 的任意形状文本检测方法，采用一次性无锚点框架中的自顶向下轮廓基准几何建模和几何参数学习，能够将复杂的几何布局编码为统一的表示，并输出仅经过一个 NMS 后处理的简单多边形检测。

TextRay: 基于轮廓的几何模型在任意形状场景文本检测中的应用

TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene  Text Detection

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
this https URL

该论文提出了一种名为 ContourNet 的算法来解决场景文本检测中出现的误检和规模变化困难的问题，并通过 Adaptive Region Proposal Network 和 Local Orthogonal Texture-aware Module 两个模块达到了更为准确的任意形状文本检测。