Word-based Arabic handwritten recognition using SVM classifier with a reject option

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 IFN/ENIT database of Arabic handwritten words and the results has shown the effectiveness of our approach in enhancing the recognition performance.