Arabic OCR Using a Novel Hybrid Classification Scheme

Hidden Markov Models or HMMs, are a relatively recent phenomenon for Arabic handwriting recognition. They are robust and efficient in classification. In this paper, an effort has been made to further boost the recognition capability of HMM Based Arabic Optical Character Recognition Systems, by using a two-tier hybrid classification scheme. The first tier consists of Part of Arabic Word or PAW, Based HMMs, and the second tier is a k-Nearest Neighbor Classifier or KNN Classifier. The second tier receives its inputs from the first tier. A second novel Hybrid Scheme is also examined. The recognition accuracies of the proposed schemes have been compared to contemporary techniques and they show an improvement in classification accuracy. HMMs have been implemented using the HTK Toolkit. The database used has been obtained from the IFN-ENIT Database of Arabic Words.