Mendeley
TY  _  JOUR
ID  -  139604223786902
TI  -  Fusion of Neural Networks Based on Negative Correlation Learning for Offline Handwritten  Word Recognition
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Abbaszadeh Arani S. A. A.
AU  -  Kabir E.
AD  -  دانشگاه تربیت مدرس
AD  -  دانشگاه تربیت مدرس
Y1  -  2016
PY  -  2016
VL  -  1
IS  -  
SP  -  53
EP  -  62
KW  -  Ensemble of classifiers
recognition of handwritten words
multilayer perceptron
negative correlation learning
DO  -  
N2  -  In this study, an ensemble classification method, based on negative correlation learning, is used for holistic recognition of handwritten words with limited vocabulary. In this method, training data set, after preprocessing and feature extraction, is applied to the base Multilayer Perceptron classifiers. These classifiers are trained by negative correlation learning to make them diverse. Features extracted from a test input are applied to the base classifiers, which produce somehow diverse outputs. By combining these outputs, the final output of the system is obtained. For experiments, three feature sets based on zoning, gradient image and contour chain code are extracted from the images. In experiments, performed on 775 images of 31 Province centers from "Iranshahr" dataset, when gradient-based features were used to train 6 Multilayer Perceptron classifiers by negative correlation, by Fusion the outputs of these classifiers through voting, an average recognition rate of 96.10 percent is achieved.
UR  - rimag.ir/en/Article/28165
L1  - rimag.ir/en/Article/Download/28165
ER  -  
