TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene Text Detection
This work addresses the problem of detecting arbitrarily shaped text in images for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles arbitrary-shaped scene text detection by proposing TextRay, a method that uses contour-based geometric modeling within a single-shot anchor-free framework, achieving state-of-the-art results on benchmark datasets with improved accuracy and efficiency.
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.