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Link to original content: https://doi.org/10.1007/978-3-031-26293-7_38
Looking from a Higher-Level Perspective: Attention and Recognition Enhanced Multi-scale Scene Text Segmentation | SpringerLink
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Looking from a Higher-Level Perspective: Attention and Recognition Enhanced Multi-scale Scene Text Segmentation

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Computer Vision – ACCV 2022 (ACCV 2022)

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Abstract

Scene text segmentation, which aims to generate pixel-level text masks, is an integral part of many fine-grained text tasks, such as text editing and text removal. Multi-scale irregular scene texts are often trapped in complex background noise around the image, and their textures are diverse and sometimes even similar to those of the background. These specific problems bring challenges that make general segmentation methods ineffective in the context of scene text. To tackle the aforementioned issues, we propose a new scene text segmentation pipeline called Attention and Recognition enhanced Multi-scale segmentation Network (ARM-Net), which consists of three main components: Text Segmentation Module (TSM) generates rectangular receptive fields of various sizes to fit scene text and integrate global information adequately; Dual Perceptual Decoder (DPD) strengthens the connection between pixels that belong to the same category from the spatial and channel perspective simultaneously during upsampling, and Recognition Enhanced Module (REM) provides text attention maps as a prior for the segmentation network, which can inherently distinguish text from background noise. Via extensive experiments, we demonstrate the effectiveness of each module of ARM-Net, and its performance surpasses that of existing state-of-the-art scene text segmentation methods. We also show that the pixel-level mask produced by our method can further improve the performance of text removal and scene text recognition.

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Acknowledgement

This research is supported in part by NSFC (Grant No.: 61936003), GD-NSF (no.2017A030312006, No.2021A1515011870), Zhuhai Industry Core and Key Technology Research Project (no. ZH22044702200058PJL), and the Science and Technology Foundation of Guangzhou Huangpu Development District (Grant 2020GH17)

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Authors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Yujin Ren, Jiaxin Zhang, Bangdong Chen, Xiaoyi Zhang & Lianwen Jin

  2. SCUT-Zhuhai Institute of Modern Industrial Innovation, Zhuhai, China

    Lianwen Jin

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  1. Yujin Ren
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  2. Jiaxin Zhang
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  3. Bangdong Chen
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  4. Xiaoyi Zhang
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  5. Lianwen Jin
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Corresponding author

Correspondence to Lianwen Jin .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Ren, Y., Zhang, J., Chen, B., Zhang, X., Jin, L. (2023). Looking from a Higher-Level Perspective: Attention and Recognition Enhanced Multi-scale Scene Text Segmentation. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13847. Springer, Cham. https://doi.org/10.1007/978-3-031-26293-7_38

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  • DOI: https://doi.org/10.1007/978-3-031-26293-7_38

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