Meta-learning of Pooling Layers for Character Recognition

Takato Otsuzuki, Heon Song, Seiichi Uchida, Hideaki Hayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationDocument Analysis and Recognition - ICDAR 2021 - 16th International Conference, Proceedings
EditorsJosep Lladós, Daniel Lopresti, Seiichi Uchida
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030863333
Publication statusPublished - 2021
Event16th International Conference on Document Analysis and Recognition, ICDAR 2021 - Lausanne, Switzerland
Duration: Sept 5 2021Sept 10 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12823 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Document Analysis and Recognition, ICDAR 2021

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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