Result: Statistical neural network based classifiers for letter recognition

Title:
Statistical neural network based classifiers for letter recognition
Authors:
ERKMEN, Burcu, YILDIRIM, Tulay
Source:
Intelligent computing in signal processing and pattern recognition (International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006)0ICIC 2006. :1081-1086
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 13 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Documentation, Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse composante principale, Principal component analysis, Análisis componente principal, Analyse statistique, Statistical analysis, Análisis estadístico, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Classification, Clasificación, Complexité calcul, Computational complexity, Complejidad computación, Compression donnée, Data compression, Compresión dato, Extraction caractéristique, Feature extraction, Extraction forme, Pattern extraction, Extracción forma, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Lettre alphabet, Letter, Letra alfabeto, Modélisation, Modeling, Modelización, Opération en ligne, Online operation, Reconnaissance caractère, Character recognition, Reconocimiento carácter, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Réseau neuronal, Neural network, Red neuronal, Résolution problème, Problem solving, Resolución problema
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Yildiz Technical University, Department of Electronics and Communications Engineering, 34349 Besiktas, Istanbul, Turkey
ISSN:
0170-8643
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18315956
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18315956
Database:
PASCAL Archive

Further Information

In this paper, Statistical Neural Networks have been proven to be an effective classifier method for large sample and high dimensional letter recognition problem. For this purpose, Probabilistic Neural Network (PNN) and General Regression Neural Networks (GRNN) have been applied to classify the 26 capital letters in the English alphabet. Principal Component Analysis (PCA) has been established as a feature extraction and a data compression method to achieve less computational complexity. The low computational complexity obtained by PCA provides a solution for high dimensional letter recognition problem for online operations. Simulation results illustrate that GRNN and PNN are suitable and effective methods for solving classification problems with higher classification accuracy and better generalization performances than their counterparts.