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

• URL: http://arxiv.org/abs/2008.04851v2
• Date: Wed, 12 Aug 2020 07:29:25 GMT
• Title: TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene Text Detection
• Authors: Fangfang Wang, Yifeng Chen, Fei Wu, and Xi Li
• Abstract summary: We propose an arbitrary-shaped text detection method, namely TextRay, which conducts top-down contour-based geometric modeling and geometric parameter learning. Experiments on several benchmark datasets demonstrate the effectiveness of the proposed approach.
• Score: 20.34326396800748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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