Abstract
Arabic handwritten recognition is a challenging task due to high variability of Arabic script and its intrinsic characteristics such as cursiveness, ligatures and diacritics. This paper presents a word-based off-line Arabic handwritten recognition system based on discrete cosine transform features and SVM classifier enhanced using a reject option. The latter is based on the number of sub-words in the input word image calculated using a novel segmentation algorithm. To evaluate our proposed system, we used the IFNIENIT database of Arabic handwritten words and the results has shown the effectiveness of our approach in enhancing the recognition performance.
