%0 Conference Paper
%A Gumah, M.E.
%A Schneider, E.
%A Aburas, A.A.
%D 2010
%F scholars:1065
%K A-coefficient; Arabic characters; Artificial Neural Network; Character images; Classification methods; Features extraction; Handwriting recognition; Optical Characters Recognition; Wavelets transform, Feature extraction; Hidden Markov models; Information technology; Neural networks; Optical character recognition, Wavelet transforms
%R 10.1109/ITSIM.2010.5561302
%T Handwriting recognition system using Fast Wavelets Transform
%U https://khub.utp.edu.my/scholars/1065/
%V 1
%X 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. © 2010 IEEE.
%Z cited By 2; Conference of 2010 International Symposium on Information Technology, ITSim'10 ; Conference Date: 15 June 2010 Through 17 June 2010; Conference Code:81915