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Semantic-Information Space Sharing Interaction Network for Arbitrary Shape Text Detection

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14431))

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Abstract

Arbitrary shape text detection is a challenging task due to significant variations in text shapes, sizes, and aspect ratios. Previous approaches relying on single-level feature map generated through a top-down fusion of different feature levels have limitations in harnessing high-level semantic information and expressing multi-scale features. To address these challenges, this paper introduces a novel arbitrary shape scene text detector called the Semantic-information Space Sharing Interaction Network (SSINet). The proposed network leverages the Semantic-information Space Sharing Module (SSM) to generate a single-level feature map capable of expressing multi-scale features with rich semantic and prominent foreground, enabling effective processing of text-related information. Experimental evaluations on three benchmark datasets, namely CTW-1500, MSRA-TD500, and ICDAR2017-MLT, validate the effectiveness of our method. The proposed SSINet achieves impressive results with an F-score of 86.0% on CTW-1500, 89.1% on MSRA-TD500, and 72.4% on ICDAR2017-MLT. The code will be available at https://github.com/123cjjjj/SSINet.

This work was supported in part by the Natural Science Foundation of Hunan Province (No. 2020JJ4057), the Key Research and Development Program of Changsha Science and Technology Bureau (No. kq2004050), and the Scientific Research Foundation of the Education Department of Hunan Province of China (No. 21A0052).

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

    Hua Chen, Runmin Wang, Yanbin Zhu, Zhenlin Zhu, Jielei Hei, Juan Xu & Yajun Ding

Authors
  1. Hua Chen
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  2. Runmin Wang
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  3. Yanbin Zhu
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  4. Zhenlin Zhu
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  5. Jielei Hei
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  6. Juan Xu
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  7. Yajun Ding
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Corresponding author

Correspondence to Runmin Wang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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Cite this paper

Chen, H. et al. (2024). Semantic-Information Space Sharing Interaction Network for Arbitrary Shape Text Detection. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14431. Springer, Singapore. https://doi.org/10.1007/978-981-99-8540-1_4

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  • DOI: https://doi.org/10.1007/978-981-99-8540-1_4

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  • Online ISBN: 978-981-99-8540-1

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