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Improving Accuracy of Document Image Classification Through Soft Voting Ensemble

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Smart Applications with Advanced Machine Learning and Human-Centred Problem Design (ICAIAME 2021)

Part of the book series: Engineering Cyber-Physical Systems and Critical Infrastructures ((ECPSCI,volume 1))

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Abstract

Convolutional Neural Networks (CNNs) work well on document image classification tasks, yielding prediction accuracies comparable to state of the art neural networks. In this work, we investigate the performance of CNN architectures namely NasNet Large, InceptionV3 and EfficientNetB3 which are pre-trained on the ImageNet for an efficient document image classification. Beyond that we ensemble these architectures to achieve a superior classification performance. As an ensemble method a simple and effective ensemble strategy called soft voting is utilized. The experiments are conducted on document images which are used in Kocaeli University application system to apply for master degree or undergraduate transfer between programs. The achieved experimental results show that, in terms of F-score, soft voting outperforms CNN architectures by achieving 94.04% even when the training data is limited.

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Acknowledgements

This work has been supported by the Kocaeli University Scientific Research and Development Support Program (BAP) in Turkey under project number FBA-2020-2152.

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Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Computer Engineering Department, Bandırma Onyedi Eylul University, Bandırma, Turkey

    Semih Sevim

  2. Faculty of Engineering, Computer Engineering Department, Kocaeli University, Kocaeli, Turkey

    Sevinç İlhan Omurca

  3. Faculty of Technology, Computer Engineering Department, Sakarya University of Applied Sciences, Sakarya, Turkey

    Ekin Ekinci

Authors
  1. Semih Sevim
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  2. Sevinç İlhan Omurca
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  3. Ekin Ekinci
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Correspondence to Semih Sevim .

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Sevim, S., Omurca, S.İ., Ekinci, E. (2023). Improving Accuracy of Document Image Classification Through Soft Voting Ensemble. In: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design. ICAIAME 2021. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-09753-9_13

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