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

Jawad AlKhateeb, Fouad Khelifi, Jianmin Jiang, Stanley Ipson

Research output: Contribution to conferencePaperpeer-review

14 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages191-194
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2009
Externally publishedYes
EventThe International Conference on Signal and Image Processing Applications: International Conference on Signal and Image Processing Applications (ICSIPA09 - Kuala Lumpur, Malaysia
Duration: 18 Nov 200919 Nov 2009

Conference

ConferenceThe International Conference on Signal and Image Processing Applications
CountryMalaysia
CityKuala Lumpur
Period18/11/0919/11/09

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