DocumentCode :
3243122
Title :
A multi-font character recognition based on its fundamental features by artificial neural networks
Author :
Neves, E.M.de A. ; Gonzaga, Adilson ; Slaets, Annie France Frère
Author_Institution :
Inst. de Fisica de Sao Carlos, Brazil
fYear :
1996
fDate :
9-11 Dec 1996
Firstpage :
196
Lastpage :
201
Abstract :
Neural networks present an alternative approach for the character recognition problem. This paper describes the development of a recognition system of multi-font character using topological feature extraction to recognize capital isolated letters. By properly specifying a set of features such as vertical, horizontal, and slant strokes, curvature, open and closed areas, called here “fundamental features”, the recognition was performed using a backpropagation neural network
Keywords :
backpropagation; feature extraction; image classification; neural nets; optical character recognition; topology; artificial neural networks; backpropagation neural network; capital isolated letters; curvature; fundamental features; horizontal strokes; multi-font character recognition; slant strokes; topological feature extraction; vertical strokes; Artificial intelligence; Artificial neural networks; Character recognition; Feature extraction; Histograms; Humans; Neural networks; Optical character recognition software; Psychology; Text recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetic Vision, 1996. Proceedings., Second Workshop on
Conference_Location :
Sao Carlos
Print_ISBN :
0-8186-8058-X
Type :
conf
DOI :
10.1109/CYBVIS.1996.629463
Filename :
629463
Link To Document :
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