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MPGTSRN: Scene Text Image Super-Resolution Guided by Multiple Visual-Semantic Prompts

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Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15332))

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Abstract

Scene text image super-resolution (STISR) aims at enhancing the visual clarity of a low-resolution text image for human perception or tasks like text recognition. In recent STISR work, various visual and semantic clues of the text play a key role in recovering the details of the text, but the utilization of different clues and their interactions is still insufficient, which often results in distorted or blurred appearances of the reconstructed text. To address this problem, we propose a multi-prompt guided text image super-resolution network (MPGTSRN). Specifically, we introduce multiple visual prompts for the text and combine them with semantic features to comprehensively capture the diverse characteristics of the text. We then propose a recurrent reconstruction network integrating multiple visual-semantic prompts to enhance the representation of the text and yield a high-resolution text image. We further propose a cross-representation attention mechanism that utilizes the complementarity of different prompts to guide the reconstruction network to adaptively focus on salient parts of the text and effectively improves the text details. The experimental results show the superiority of our proposed MPGTSRN in the STISR task.

M. Li and Z. Zhuang—Equal contribution.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology Nanjing University, 163 Xianlin Road, Nanjing, China

    Mingjun Li, Zeming Zhuang, Shuo Xu & Feng Su

Authors
  1. Mingjun Li
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  2. Zeming Zhuang
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  3. Shuo Xu
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  4. Feng Su
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Corresponding author

Correspondence to Feng Su .

Editor information

Editors and Affiliations

  1. University of Salford, Salford, Lancashire, UK

    Apostolos Antonacopoulos

  2. Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Subhasis Chaudhuri

  3. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

    Saumik Bhattacharya

  6. Indian Statistical Institute, Kolkata, West Bengal, India

    Umapada Pal

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Li, M., Zhuang, Z., Xu, S., Su, F. (2025). MPGTSRN: Scene Text Image Super-Resolution Guided by Multiple Visual-Semantic Prompts. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15332. Springer, Cham. https://doi.org/10.1007/978-3-031-78125-4_22

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  • DOI: https://doi.org/10.1007/978-3-031-78125-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78124-7

  • Online ISBN: 978-3-031-78125-4

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