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Meta-learning of Pooling Layers for Character Recognition

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12823))

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Abstract

In convolutional neural network-based character recognition, pooling layers play an important role in dimensionality reduction and deformation compensation. However, their kernel shapes and pooling operations are empirically predetermined; typically, a fixed-size square kernel shape and max pooling operation are used. In this paper, we propose a meta-learning framework for pooling layers. As part of our framework, a parameterized pooling layer is proposed in which the kernel shape and pooling operation are trainable using two parameters, thereby allowing flexible pooling of the input data. We also propose a meta-learning algorithm for the parameterized pooling layer, which allows us to acquire a suitable pooling layer across multiple tasks. In the experiment, we applied the proposed meta-learning framework to character recognition tasks. The results demonstrate that a pooling layer that is suitable across character recognition tasks was obtained via meta-learning, and the obtained pooling layer improved the performance of the model in both few-shot character recognition and noisy image recognition tasks.

We provide our implementation at https://github.com/Otsuzuki/Meta-learning-of-Pooling-Layers-for-Character-Recognition.

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Authors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Takato Otsuzuki, Heon Song, Seiichi Uchida & Hideaki Hayashi

Authors
  1. Takato Otsuzuki
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  2. Heon Song
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  3. Seiichi Uchida
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  4. Hideaki Hayashi
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Corresponding author

Correspondence to Hideaki Hayashi .

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Editors and Affiliations

  1. Universitat Autònoma de Barcelona, Barcelona, Spain

    Josep Lladós

  2. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  3. Kyushu University, Fukuoka-shi, Japan

    Seiichi Uchida

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Otsuzuki, T., Song, H., Uchida, S., Hayashi, H. (2021). Meta-learning of Pooling Layers for Character Recognition. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12823. Springer, Cham. https://doi.org/10.1007/978-3-030-86334-0_13

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  • DOI: https://doi.org/10.1007/978-3-030-86334-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86333-3

  • Online ISBN: 978-3-030-86334-0

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