Handwriting recognition system using fast wavelets transform

Optical Characters Recognition (OCR) is one of the active subjects of research since the early days of computer science. There are two main stages in most of OCR systems: features extraction and classification. Artificial Neural Networks and Hidden Markov Models are the most popular classification m...

Full description

Saved in:
Bibliographic Details
Published in2010 International Symposium on Information Technology Vol. 1; pp. 1 - 6
Main Authors Gumah, Mohamed E, Schneider, Etienne, Aburas, Abdurazzag Ali
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Accuracy
Arabic characters
Artificial neural networks
Character recognition
Fast wavelets transform
Hidden Markov models
Optical character recognition software
Optical Characters Recognition
Wavelet transforms
Online AccessGet full text

Cover

More Information
Summary:Optical Characters Recognition (OCR) is one of the active subjects of research since the early days of computer science. There are two main stages in most of OCR systems: features extraction and classification. Artificial Neural Networks and Hidden Markov Models are the most popular classification methods used for OCR systems. In this paper, a method that relays on Fast Wavelets Transform (FWT) for optical character recognition is proposed. The idea of the proposed technique is to use the FWT to produce a coefficient vector of the character images, which will be directly used to recognize characters. Using the proposed technique, an accuracy of 94.18% in average was achieved.
ISBN:1424467152
9781424467150
ISSN:2155-8973
DOI:10.1109/ITSIM.2010.5561302