Ahmed Sahloul¹ and Cheng Suen², ¹Damietta University, Egypt and ²Concordia University, Canada

Recognition of handwritten Arabic text awaits accurate recognition solutions. There are many difficulties facing a good handwritten Arabic recognition system such as unlimited variation in human handwriting, similarities of distinct character shapes, and their position in the word. The typical Optical Character Recognition (OCR) systems are based mainly on three stages, preprocessing, features extraction and recognition.In this paper, we present an efficient approach for the recognition of off-line Arabic handwritten characters which is based on structural, Statistical and Morphological features from the main body of the character and also from the secondary components. Evaluation of the accuracy of the selected features is made. The system was trained and tested with CENPRMI dataset. The proposed algorithm obtained promising results in terms of accuracy (success rate of 100% for some letters at average 88%). In Comparable with other related works we find that our result is the highest among others.

Handwritten Arabic Characters, Feature extraction, Secondary component