TextRay：基于轮廓几何建模的异形场景文本检测
TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene Text Detection
摘要：
由于文本的复杂几何布局，如大的纵横比，各种比例，随机旋转和曲线形状，因此形状复杂的文本检测是一项具有挑战性的任务。大多数最先进的方法从自下而上的角度解决这个问题，试图用简单的局部单元（例如，局部框或像素）并利用启发式后处理生成检测。在这项工作中，我们提出了一种任意形状的文本检测方法，即TextRay，它进行自上而下的基于轮廓的几何建模和几何参数学习在一个单镜头锚无框架。在极坐标系下进行几何建模，采用形状空间和参数空间的双向映射方案，将复杂的几何布局编码成统一的表示。为了有效地学习表示，我们设计了一个中心加权的训练策略和内容损失，建立几何编码和视觉内容之间的传播路径。TextRay在一个通道中输出简单的多边形检测，只有一个NMS后处理。在多个基准数据集上的实验证明了该方法的有效性。该代码可在https://github.com/LianaWang/TextRay上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
