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Word-based handwritten arabic scripts recognition using DCT features and neural network classifier

Alkhateeb, JH, Ren, J, Jiang, J, Ipson, SS and El Abed, H (2008) Word-based handwritten arabic scripts recognition using DCT features and neural network classifier In: SSD 2008, 2008-07-20 - 2008-07-22, Amman, Jordan.

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Abstract

In this paper, a system is proposed for word-based recognition of handwritten Arabic scripts. Techniques are discussed in details in terms of three stages in the system, i.e. preprocessing, feature extraction and classification. Firstly, words are segmented from inputted scripts and also normalized in size. Then, DCT features are extracted for each word sample. Finally, these features are then utilized to train a neural network for classification. The proposed system has been successfully tested on database (version v2.0p1e) consisting of 32492 Arabic words handwritten by more than 1000 different writers, and the results were promising and very encouraging.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Alkhateeb, JHUNSPECIFIEDUNSPECIFIED
Ren, JUNSPECIFIEDUNSPECIFIED
Jiang, Jjianmin.jiang@surrey.ac.ukUNSPECIFIED
Ipson, SSUNSPECIFIEDUNSPECIFIED
El Abed, HUNSPECIFIEDUNSPECIFIED
Date : 2008
Identification Number : 10.1109/SSD.2008.4632863
Contributors :
ContributionNameEmailORCID
publisherIEEE, UNSPECIFIEDUNSPECIFIED
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:24
Last Modified : 17 May 2017 15:03
URI: http://epubs.surrey.ac.uk/id/eprint/835195

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