Offline Automatic Segmentation based Recognition of Handwritten Arabic Words

The world heritage of handwritten Arabic documents is huge however only manual indexing and retrieval techniques of the content of these documents are available. To facilitate an automatic retrieval of such handwritten Arabic document, a number of automatic recognition systems for handwritten Arabic words have been proposed. Nevertheless, these systems suffer from low recognition accuracy due to the peculiarities of the handwritten Arabic language. Thus, in this Paper we propose a segmentation based recognition system for handwritten Arabic words. We divide a handwritten word into smaller pieces of a word and then these small pieces are segmented into candidate letters. These candidate letters are converted into their correspondence chain-code representation. Thereafter we extract discrete, statistical and structural features for classification. Additionally, we introduce a novel active contour based feature to increase the recognition accuracy of strongly deformed Arabic letters. We also use a decision tree to reduce the number of potential classes. We then use a neural network to compute weights for all statistical features and use them as input for a k-NN classifier. Our experiments show that the extracted features by our technique achieve higher recognition accuracy as compared to other features.