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Text Dependent Writer Identification using Support Vector Machine

by Saranya K, Vijaya M S
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 2
Year of Publication: 2013
Authors: Saranya K, Vijaya M S
10.5120/10894-5797

Saranya K, Vijaya M S . Text Dependent Writer Identification using Support Vector Machine. International Journal of Computer Applications. 65, 2 ( March 2013), 6-11. DOI=10.5120/10894-5797

@article{ 10.5120/10894-5797,
author = { Saranya K, Vijaya M S },
title = { Text Dependent Writer Identification using Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 2 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number2/10894-5797/ },
doi = { 10.5120/10894-5797 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:17:35.726009+05:30
%A Saranya K
%A Vijaya M S
%T Text Dependent Writer Identification using Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 2
%P 6-11
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Writer identification is the process of identifying the writer of the document based on their handwriting. Recent advances in computational engineering, artificial intelligence, data mining, image processing, pattern recognition and machine learning have shown that it is possible to automate writer identification. This paper proposes a model for text-dependent writer identification based on English handwriting. Features are extracted from scanned images of handwritten words and trained using pattern classification algorithm namely support vector machine. It is observed that accuracy of proposed writer identification model with Polynomial kernel show 94. 27% accuracy.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Support Vector Machine Training Writer Identification

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