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• W3048778885 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." @default.
• W3048778885 created "2020-08-18" @default.
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• W3048778885 date "2020-08-11" @default.
• W3048778885 modified "2023-09-23" @default.
• W3048778885 title "TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene Text Detection" @default.
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• W3048778885 doi "https://doi.org/10.48550/arxiv.2008.04851" @default.
• W3048778885 hasPublicationYear "2020" @default.
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