A machine learning approach for classifying offline handwritten Arabic words

AlKhateeb, JH, Ren, J, Jiang, J and Ipson, S (2009) A machine learning approach for classifying offline handwritten Arabic words In: CW '09, 2009-09-07 - 2009-09-11, Bradford, UK.

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Abstract

In this paper, a machine learning approach for classifying handwritten Arabic word is proposed, which includes three stages including preprocessing, feature extraction and classification. Firstly, words are segmented from inputted scripts and also normalized in size. Secondly, three different feature extraction methods are applied to each segmented word namely the discrete cosine transform (DCT), moment invariants, and absolute mean value of overlapping blocks. Finally, theses features are utilized to train a neural network for classification. This approach has been tested using the IFN/ENIT database which consists of 32492 Arabic words. The proposed approach gives a good accuracy when compared with other methods.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Divisions :	Surrey research (other units)
Authors :	Name Email ORCID AlKhateeb, JH Ren, J Jiang, J jianmin.jiang@surrey.ac.uk Ipson, S
Date :	2009
DOI :	10.1109/CW.2009.62
Depositing User :	Symplectic Elements
Date Deposited :	17 May 2017 12:25
Last Modified :	23 Jan 2020 17:50
URI:	http://epubs.surrey.ac.uk/id/eprint/835251

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