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

J. H. AlKhateeb; Jinchang Ren; Jianmin Jiang; S. Ipson; El Abed Haikal

doi:10.1109/SSD.2008.4632863

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

J. H. AlKhateeb, Jinchang Ren, Jianmin Jiang, S. Ipson, El Abed Haikal

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

21 Citations (Scopus)

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.

Original language	English
Title of host publication	2008 5th International multi-conference on systems, signals and devices
Publisher	IEEE
Pages	486-490
Number of pages	5
ISBN (Print)	9781424422050
DOIs	https://doi.org/10.1109/SSD.2008.4632863
Publication status	Published - 2008
Event	5th International Multi-Conference on Systems, Signals and Devices - Amman, Jordan Duration: 20 Jul 2008 → 22 Jul 2008

Conference

Conference	5th International Multi-Conference on Systems, Signals and Devices
Country	Jordan
City	Amman
Period	20/07/08 → 22/07/08

Keywords

character-recognition
Arabic scripts

Access to Document

10.1109/SSD.2008.4632863

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Cite this

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title = "Word-based handwritten arabic scripts recognition using DCT features and neural network classifier",

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.",

keywords = "character-recognition, Arabic scripts",

author = "AlKhateeb, {J. H.} and Jinchang Ren and Jianmin Jiang and S. Ipson and Haikal, {El Abed}",

year = "2008",

doi = "10.1109/SSD.2008.4632863",

language = "English",

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pages = "486--490",

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publisher = "IEEE",

note = "5th International Multi-Conference on Systems, Signals and Devices ; Conference date: 20-07-2008 Through 22-07-2008",

}

AlKhateeb, JH, Ren, J, Jiang, J, Ipson, S & Haikal, EA 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, pp. 486-490, 5th International Multi-Conference on Systems, Signals and Devices, Amman, Jordan, 20/07/08. https://doi.org/10.1109/SSD.2008.4632863

Word-based handwritten arabic scripts recognition using DCT features and neural network classifier. / AlKhateeb, J. H.; Ren, Jinchang; Jiang, Jianmin; Ipson, S.; Haikal, El Abed.

2008 5th International multi-conference on systems, signals and devices. IEEE, 2008. p. 486-490.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Word-based handwritten arabic scripts recognition using DCT features and neural network classifier

AU - AlKhateeb, J. H.

AU - Ren, Jinchang

AU - Jiang, Jianmin

AU - Ipson, S.

AU - Haikal, El Abed

PY - 2008

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N2 - 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.

AB - 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.

KW - character-recognition

KW - Arabic scripts

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DO - 10.1109/SSD.2008.4632863

M3 - Conference contribution book

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T2 - 5th International Multi-Conference on Systems, Signals and Devices

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