@inproceedings{scholars1065,
             doi = {10.1109/ITSIM.2010.5561302},
         address = {Kuala Lumpur},
            note = {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},
           title = {Handwriting recognition system using Fast Wavelets Transform},
            year = {2010},
         journal = {Proceedings 2010 International Symposium on Information Technology - Visual Informatics, ITSim'10},
          volume = {1},
        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. {\^A}{\copyright} 2010 IEEE.},
            isbn = {9781424467181},
          author = {Gumah, M. E. and Schneider, E. and Aburas, A. A.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049396533&doi=10.1109\%2fITSIM.2010.5561302&partnerID=40&md5=a839702dfcd624bb464ccad31f3a9546},
        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}
}