Combining a hybrid Approach for Features Selection and Hidden Markov Models in Multifont Arabic Characters Recognition
Nesrine Amor, Najoua Essoukri Ben Amara · 2006
Optical character recognition (OCR) has been an active subject of research since the early days of computers. Despite the age of the subject, it remains one of the most challenging and exciting areas of research in computer science. In recent years it has grown into a mature discipline, producing a huge body of work. In this paper, we present an Arabic optical multifont character recognition approach based on both Hough transform and wavelet transform for features selection and hidden Markov models for classification. In the next sections, the whole OCR system is presented. The different tests carried out on a set of about 170.000 samples of multifont Arabic characters and the obtained results so far are developed