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An Efficient Selected Feature Set for the Middle Age Persian Character Recognition
Cosmos Club, Washington, DC October 13-October 15
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AIPR.2004.1233rd Applied Imagery Pattern Recognit ...
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S. Alirezaee, H. Aghaeinia, M. Ahmadi, K. Faez, "An Efficient Selected Feature Set for the Middle Age Persian Character Recognition," Applied Image Pattern Recognition Workshop,, pp. 246-250, 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04), 2004.
 
 
BibTex
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@article{ 10.1109/AIPR.2004.12,
author = {S. Alirezaee and H. Aghaeinia and M. Ahmadi and K. Faez},
title = {An Efficient Selected Feature Set for the Middle Age Persian Character Recognition},
journal ={Applied Image Pattern Recognition Workshop,},
volume = {0},
year = {2004},
issn = {1550-5219},
pages = {246-250},
doi = {http://doi.ieeecomputersociety.org/10.1109/AIPR.2004.12},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Applied Image Pattern Recognition Workshop,
TI - An Efficient Selected Feature Set for the Middle Age Persian Character Recognition
SN - 1550-5219
SP246
EP250
A1 - S. Alirezaee,
A1 - H. Aghaeinia,
A1 - M. Ahmadi,
A1 - K. Faez,
PY - 2004
KW - null
VL - 0
JA - Applied Image Pattern Recognition Workshop,
ER -
 
S. Alirezaee, Amirkabir Univ. of Tech., & Iran Telecommunication Research Center, Tehran, Iran; University of Windsor, Canada
H. Aghaeinia, Amirkabir Univ. of Tech., & Iran Telecommunication Research Center, Tehran, Iran
M. Ahmadi, University of Windsor, Canada
K. Faez, Amirkabir Univ. of Tech., & Iran Telecommunication Research Center, Tehran, Iran
In this paper, a morphological based method for recognition of handwritten middle Persian characters is presented. After pre-processing and noise cancellation, Morphological erosion operator with many structure elements is applied. The structure elements are with variable length lines at directions 0, 45, 90, 135 degrees. A five element feature set has been defined so: (1) relative energy of eroded version with respect to the original image energy (REL_ENG), (2) displacement of the center of mass (CM_DIS), (3) minimum eigenvalue (EIG_MIN), (4) maximum eigenvalue (EIG_MAX) and (5) its direction (EIG-DIR). These features are used to design a feedforward neural network with one hidden layer. The best classification error is about 2.39% (97.61% recognition rate), and is achieved with 150 neurons for the hidden layer.
Citation:
S. Alirezaee, H. Aghaeinia, M. Ahmadi, K. Faez, "An Efficient Selected Feature Set for the Middle Age Persian Character Recognition," aipr, pp.246-250, 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04), 2004
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