A new approach for off-line handwritten Arabic word recognition using KNN classifier

Due to similarities between Arabic letters, and the various writing styles employed, recognition of Arabic handwritten text can be difficult. In this paper, an off-line Arabic handwritten word recognition system is proposed, in which technical details are presented in terms of three stages, i.e. preprocessing, feature extraction and classification. Firstly, words are segmented from input scripts and also normalized in size. Secondly, each segmented word is divided into overlapping blocks. Absolute mean values computed for each block of segmented words constitutes a feature vector. Finally, the resulting feature vectors are used to classify the words using the K nearest Neighbour classifier (KNN). The proposed system has been successfully tested on the IFN/ENIT database consisting of 32492 Arabic handwritten words which are written by more than 1000 different writers. Experimental results show a good recognition rate when compared with other methods.