Handwriting recognition system using fast wavelets transform - Gumah, Mohamed E; Schneider, Etienne; Aburas, Abdurazzag Ali (2010 International Symposium on Information Technology, 2010)
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.