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Robust Handwritten Digit Classification Using Tensors Decomposition

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Mining Intelligence and Knowledge Exploration (MIKE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11987))

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Abstract

Various handwritten digits recognition methods have been developed and their good performances have been reported. Nonetheless, recognizing digits types and patterns remains a challenging task. Recognition performance is highly affected by image corruption, perspective and illumination variation. On the other hand, vectors and matrices are sometimes not suitable or enough to describe the intrinsic nature of data from various fields of data processing. In this paper, we propose a handwritten digit classification method based on robust higher-order tensors. The proposed approach handles invariance to occlusions, misalignment and illumination variation. Evaluation of the MNIST and USPS handwritten numeral databases is performed to assess the performance and effectiveness of the proposed method. Substantial results are achieved in terms of recognition accuracy, image alignment, numerical stability and computational speed.

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Author information

Authors and Affiliations

  1. Université de Tunis, Institut Supérieur de Gestion, LARODEC, Le Bardo, Tunisia

    Dhouha Rahmani & Haïfa Nakouri

  2. Université de la Manouba, Ecole Supérieure de l’Economie Numérique, La Manouba, Tunisia

    Haïfa Nakouri

Authors
  1. Dhouha Rahmani
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  2. Haïfa Nakouri
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Corresponding author

Correspondence to Haïfa Nakouri .

Editor information

Editors and Affiliations

  1. National Institute of Technology, Goa, India

    Purushothama B. R.

  2. National Institute of Technology, Goa, India

    Veena Thenkanidiyoor

  3. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

  4. Indian Institute of Information Technology, Sri City, India

    Odelu Vanga

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Rahmani, D., Nakouri, H. (2020). Robust Handwritten Digit Classification Using Tensors Decomposition. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_25

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  • DOI: https://doi.org/10.1007/978-3-030-66187-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66186-1

  • Online ISBN: 978-3-030-66187-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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