Handwriting recognition system using Fast Wavelets Transform

Gumah, M.E. and Schneider, E. and Aburas, A.A. (2010) Handwriting recognition system using Fast Wavelets Transform. In: UNSPECIFIED.

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Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: 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
Uncontrolled Keywords: 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
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:49
Last Modified: 09 Nov 2023 15:49
URI: https://khub.utp.edu.my/scholars/id/eprint/1065

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