A Novel Handwritten Digits Recognition Method based on Subclass Low Variances Guided Support Vector Machine

Soumaya Nheri, Riadh Ksantini, Mouhamed Bécha Kaâniche, Adel Bouhoula

2018

Abstract

Handwritten Digits Recognition (HWDR) is one of the very popular application in computer vision and it has always been a challenging task in pattern recognition. But it is very hard practical problem and many problems are still unresolved. To develop a high performance automatic HWDR, several learning algorithms have been proposed, studied and modified. Much of the effort involved in Handwritten digits classification with Support Vector Machine (SVM). More specifically, in the current study we are focusing on one-class SVM (OSVM) approaches which are of huge interest for our problem. Covariance Guided OSVM (COSVM) algorithm improves up on the OSVM method, by emphasizing the low variance directions. However, COSVM does not handle multi-modal target class data. Thus, we design a new subclass algorithm based on COSVM, which takes advantage of the target class clusters variance information. To investigate the effectiveness of the novel Subclass COSVM (SCOSVM), we compared our proposed approach with other methods based on other contemporary one-class classifiers, on well-known standard MNIST benchmark datasets and Optical Recognition of Handwritten Digits datasets. The experimental results verify the significant superiority of our method.

Paper Citation

in Harvard Style

Nheri S., Ksantini R., Kaâniche M. and Bouhoula A. (2018). A Novel Handwritten Digits Recognition Method based on Subclass Low Variances Guided Support Vector Machine. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 28-36. DOI: 10.5220/0006611100280036

in Bibtex Style

@conference{visapp18,
author={Soumaya Nheri and Riadh Ksantini and Mouhamed Bécha Kaâniche and Adel Bouhoula},
title={A Novel Handwritten Digits Recognition Method based on Subclass Low Variances Guided Support Vector Machine},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={28-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006611100280036},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - A Novel Handwritten Digits Recognition Method based on Subclass Low Variances Guided Support Vector Machine
SN - 978-989-758-290-5
AU - Nheri S.
AU - Ksantini R.
AU - Kaâniche M.
AU - Bouhoula A.
PY - 2018
SP - 28
EP - 36
DO - 10.5220/0006611100280036
PB - SciTePress