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Machine Learning Classification Algorithms to Recognize Chart Types in Portable Document Format (PDF) Files

by V. Karthikeyani, S. Nagarajan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 2
Year of Publication: 2012
Authors: V. Karthikeyani, S. Nagarajan
10.5120/4789-6997

V. Karthikeyani, S. Nagarajan . Machine Learning Classification Algorithms to Recognize Chart Types in Portable Document Format (PDF) Files. International Journal of Computer Applications. 39, 2 ( February 2012), 1-5. DOI=10.5120/4789-6997

@article{ 10.5120/4789-6997,
author = { V. Karthikeyani, S. Nagarajan },
title = { Machine Learning Classification Algorithms to Recognize Chart Types in Portable Document Format (PDF) Files },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 2 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number2/4789-6997/ },
doi = { 10.5120/4789-6997 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:25:21.414937+05:30
%A V. Karthikeyani
%A S. Nagarajan
%T Machine Learning Classification Algorithms to Recognize Chart Types in Portable Document Format (PDF) Files
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 2
%P 1-5
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Chart recognition system from PDF files is a relatively young research field where techniques and algorithms are proposed to identify type of charts and interpret them. This paper focus on recognition of chart type that is a part of PDF document using texture features and classification algorithm. Eleven types of texture features and three classifiers, namely, Multilayer perceptron, support vector machine and K nearest neighbour, are used. Performance analysis of the proposed chart type recognition systems show that texture features for chart type recognition has promising future and produces best result while using KNN and SVM algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

Chart Classification Texture Feature Neural Network. Support Vector Machine K Nearest Neighbour Classifier.

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