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Reseach Article

A New Decision Tree for Recognition of Persian Handwritten Characters

by Mohammad Rajabi, Naser Nematbakhsh, S. Amirhassan Monadjemi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 6
Year of Publication: 2012
Authors: Mohammad Rajabi, Naser Nematbakhsh, S. Amirhassan Monadjemi
10.5120/6271-8433

Mohammad Rajabi, Naser Nematbakhsh, S. Amirhassan Monadjemi . A New Decision Tree for Recognition of Persian Handwritten Characters. International Journal of Computer Applications. 44, 6 ( April 2012), 52-58. DOI=10.5120/6271-8433

@article{ 10.5120/6271-8433,
author = { Mohammad Rajabi, Naser Nematbakhsh, S. Amirhassan Monadjemi },
title = { A New Decision Tree for Recognition of Persian Handwritten Characters },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 6 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 52-58 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number6/6271-8433/ },
doi = { 10.5120/6271-8433 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:34:53.416294+05:30
%A Mohammad Rajabi
%A Naser Nematbakhsh
%A S. Amirhassan Monadjemi
%T A New Decision Tree for Recognition of Persian Handwritten Characters
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 6
%P 52-58
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a binary decision tree, based on Neural Networks, Support Vector Machine and K-Nearest Neighbor is employed and presented for recognition of Persian handwritten isolated digits and characters. In the proposed method, a part of the training data is divided into two clustersusing a clustering algorithm, and this process continues until each subtree reaches clusters with optimum clustering, where the tree leaves are the final obtained clusters. According to the clustering results, classifiers such as ANN and SVM can perform correctly, therefore the decision tree can be built. A part of the test data is selected as validation data and in each node of the tree, a classifier with the highest recognition accuracy on validation data is selected. Recognition accuracy at 8, 20, and 33 clusters have been evaluated and compared with other existing methods. Recognition accuracy of 98. 72% and 97. 3% on IFHCDB database is obtained respectivelywhen 8-class and 20-class problems is assumed. Again 98. 9% accuracy on HODA database is achieved.

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Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine K Nearest Neighbor Decision Tree Self Organized Map Neural Networks Multi Class Classification