Classification of Typed Characters Using Backpropagation Neural Network

This thesis concentrates on classification of typed characters using a neural network. Recognition of typed or printed characters using intelligent methods like neural network has found much application in the recent decades. The ability of moment invariants to represent characters independent of po...

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Main Author: Alamelu, Subbiah
Format: Thesis
Language:English
English
Published: 2001
Subjects:
Online Access:http://psasir.upm.edu.my/id/eprint/10735/1/FK_2001_2.pdf
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author Alamelu, Subbiah
author_facet Alamelu, Subbiah
author_sort Alamelu, Subbiah
collection UPM
description This thesis concentrates on classification of typed characters using a neural network. Recognition of typed or printed characters using intelligent methods like neural network has found much application in the recent decades. The ability of moment invariants to represent characters independent of position, size and orientation have caused them to be proposed as pattern sensitive features in classification and recognition of these characters. In this research, uppercase English characters is represented by invariant features derived using functions of regular moments, namely Hu invariants. Moments up to the third order have been used for the recognition of these typed characters. A single layer perceptron artificial neural network trained by the backpropagation algorithm is used to classify these characters into their respective categories. Experimental study conducted with three different fonts commonly used in word processing applications shows good classification results. Some suggestions for further work in this area have also been presented.
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spelling upm.eprints-107352024-05-13T08:33:20Z http://psasir.upm.edu.my/id/eprint/10735/ Classification of Typed Characters Using Backpropagation Neural Network Alamelu, Subbiah This thesis concentrates on classification of typed characters using a neural network. Recognition of typed or printed characters using intelligent methods like neural network has found much application in the recent decades. The ability of moment invariants to represent characters independent of position, size and orientation have caused them to be proposed as pattern sensitive features in classification and recognition of these characters. In this research, uppercase English characters is represented by invariant features derived using functions of regular moments, namely Hu invariants. Moments up to the third order have been used for the recognition of these typed characters. A single layer perceptron artificial neural network trained by the backpropagation algorithm is used to classify these characters into their respective categories. Experimental study conducted with three different fonts commonly used in word processing applications shows good classification results. Some suggestions for further work in this area have also been presented. 2001-09 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/10735/1/FK_2001_2.pdf Alamelu, Subbiah (2001) Classification of Typed Characters Using Backpropagation Neural Network. Masters thesis, Universiti Putra Malaysia. Neural networks (Computer science) English
spellingShingle Neural networks (Computer science)
Alamelu, Subbiah
Classification of Typed Characters Using Backpropagation Neural Network
title Classification of Typed Characters Using Backpropagation Neural Network
title_full Classification of Typed Characters Using Backpropagation Neural Network
title_fullStr Classification of Typed Characters Using Backpropagation Neural Network
title_full_unstemmed Classification of Typed Characters Using Backpropagation Neural Network
title_short Classification of Typed Characters Using Backpropagation Neural Network
title_sort classification of typed characters using backpropagation neural network
topic Neural networks (Computer science)
url http://psasir.upm.edu.my/id/eprint/10735/1/FK_2001_2.pdf
work_keys_str_mv AT alamelusubbiah classificationoftypedcharactersusingbackpropagationneuralnetwork