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Progressive Evolution from Single-Point to Polygon for Scene Text

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Abstract

The advancement of text shape representations towards compactness has enhanced text detection and spotting performance, but at a high annotation cost. Current models use single-point annotations to reduce costs, yet they lack sufficient localization information for downstream applications. To overcome this limitation, we introduce Point2Pol- ygon, which can efficiently transform single-points into compact polygons. Our method uses a coarse-to-fine process, starting with creating and selecting anchor points based on recognition confidence, then vertically and horizontally refining the polygon using recognition information to optimize its shape. We demonstrate the accuracy of the generated polygons through extensive experiments: 1) By creating polygons from ground truth points, we achieved an accuracy of 82.0% on ICDAR 2015; 2) In training detectors with polygons generated by our method, we attained 86% of the accuracy relative to training with ground truth (GT); 3) Additionally, the proposed Point2Polygon can be seamlessly integrated to empower single-point spotters to generate polygons. This integration led to an impressive 82.5% accuracy for the generated polygons. It is worth mentioning that our method relies solely on synthetic recognition information, eliminating the need for any manual annotation beyond single points.

L. Deng and M. Huang—Equal contribution.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 62225603, No. 62206104).

Author information

Authors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linger Deng, Xudong Xie, Yuliang Liu & Xiang Bai

  2. South China University of Technology, Guangzhou, China

    Mingxin Huang & Lianwen Jin

Authors
  1. Linger Deng

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  2. Mingxin Huang

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  3. Xudong Xie

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  4. Yuliang Liu

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  5. Lianwen Jin

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  6. Xiang Bai

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Corresponding author

Correspondence toYuliang Liu.

Editor information

Editors and Affiliations

  1. Luleå Tekniska Universitet, Luleå, Sweden

    Elisa H. Barney Smith

  2. Luleå Tekniska Universitet, Luleå, Sweden

    Marcus Liwicki

  3. Tsinghua University, Beijing, China

    Liangrui Peng

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Deng, L., Huang, M., Xie, X., Liu, Y., Jin, L., Bai, X. (2024). Progressive Evolution from Single-Point to Polygon for Scene Text. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds) Document Analysis and Recognition - ICDAR 2024. ICDAR 2024. Lecture Notes in Computer Science, vol 14808. Springer, Cham. https://doi.org/10.1007/978-3-031-70549-6_7

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