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

AlKhateeb, J. H. and Ren, Jinchang and Jiang, Jianmin and Ipson, S. and Haikal, El Abed; (2008) Word-based handwritten arabic scripts recognition using DCT features and neural network classifier. In: 2008 5th International multi-conference on systems, signals and devices. IEEE, JOR, pp. 486-490. ISBN 9781424422050 (https://doi.org/10.1109/SSD.2008.4632863)

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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 term 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 v2.0ple) consisting of 32492 Arabic words handwritten by more than 1000 different writers, and the results were promising and very encouraging.

ORCID iDs

AlKhateeb, J. H., Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194, Jiang, Jianmin, Ipson, S. and Haikal, El Abed;
CORE (COnnecting REpositories)