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LK-Net: Efficient Large Kernel ConvNet for Document Enhancement

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Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15321))

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Abstract

Various types of degradation in document images, such as blurring, shadow, and physical wear and tear, significantly impact the effectiveness of downstream tasks in multimedia applications. The need for document image enhancement arises from the urgent need to improve the legibility and quality of these images, which are integral for accurate Optical Character Recognition(OCR), information retrieval, document analysis, etc. This paper introduces a novel and simple approach employing Large Kernel Convolutional Networks (ConvNets) for document image enhancement, capitalizing on their ability to encapsulate expansive contextual information to improve image quality. Extensive experimental evaluations across multiple benchmarks have demonstrated that our method achieves state-of-the-art (SOTA) while maintaining low computational cost. Code and pre-trained models are available at https://github.com/qijunshi/LKNet.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No.62176091.

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Authors and Affiliations

  1. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, 200241, China

    Qijun Shi, Hongjian Zhan, Yangfu Li & Yue Lu

  2. Shanghai Hex Information Technology Co., Ltd., Shanghai, China

    Weijun Zou & Huasheng Li

  3. Indian Statistical Institute Kolkata, Kolkata, India

    Umapada Pal

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  1. Qijun Shi
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  2. Hongjian Zhan
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  3. Yangfu Li
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  4. Weijun Zou
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  5. Huasheng Li
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  6. Umapada Pal
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  7. Yue Lu
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Corresponding author

Correspondence to Hongjian Zhan .

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Editors and Affiliations

  1. University of Salford, Salford, 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. IIT Kharagpur, Kharagpur, India

    Saumik Bhattacharya

  6. Indian Statistical Institute Kolkata, Kolkata, India

    Umapada Pal

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Shi, Q. et al. (2025). LK-Net: Efficient Large Kernel ConvNet for Document Enhancement. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15321. Springer, Cham. https://doi.org/10.1007/978-3-031-78305-0_18

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  • DOI: https://doi.org/10.1007/978-3-031-78305-0_18

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