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

Topics: Document Imaging in Business; Features Extraction; Image Generation Pipeline: Algorithms and Techniques; Image-Based Modeling and 3D Reconstruction; Machine Learning Technologies for Vision

Authors: Soumaya Nheri 1 ; Riadh Ksantini 2 ; Mouhamed Bécha Kaâniche 1 and Adel Bouhoula 1

Affiliations: 1 Higher School of Communication of Tunis and University of Carthage, Tunisia ; 2 Higher School of Communication of Tunis, University of Carthage and University of Windsor, Tunisia

Keyword(s): Handwritten Digits Recognition, Support Vector Machine, Kernel Covariance Matrix, One-Class Classification, Outlier Detection, Subclass Low Variances.

Related Ontology Subjects/Areas/Topics: Applications ; Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Document Imaging in Business ; Features Extraction ; Geometry and Modeling ; Image and Video Analysis ; Image Formation and Preprocessing ; Image Generation Pipeline: Algorithms and Techniques ; Image-Based Modeling ; Pattern Recognition ; Software Engineering

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 pr oposed 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. (More)

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Paper citation in several formats:
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; ISSN 2184-4321, SciTePress, pages 28-36. DOI: 10.5220/0006611100280036

@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},
issn={2184-4321},
}

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
IS - 2184-4321
AU - Nheri, S.
AU - Ksantini, R.
AU - Kaâniche, M.
AU - Bouhoula, A.
PY - 2018
SP - 28
EP - 36
DO - 10.5220/0006611100280036
PB - SciTePress