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There are many difficulties facing a handwritten Arabic recognition systemsuch as unlimited variation in human handwriting, similarities of distinctcharacter shapes, interconnections of neighbouring characters and theirposition in the word. The typical Optical Character Recognition (OCR) systemsare based mainly on three stages, preprocessing, features extraction andrecognition. This paper proposes new methods for handwritten Arabic characterrecognition which is based on novel preprocessing operations includingdifferent kinds of noise removal also different kind of features likestructural, Statistical and Morphological features from the main body of thecharacter and also from the secondary components. Evaluation of the accuracy ofthe selected features is made. The system was trained and tested by backpropagation neural network with CENPRMI dataset. The proposed algorithmobtained promising results as it is able to recognize 88% of our test setaccurately. In Comparable with other related works we find that our result isthe highest among other published works.

A Novel Method for the Recognition of Isolated Handwritten Arabic Characters