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

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

16 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 languageEnglish
Title of host publication2008 5th International multi-conference on systems, signals and devices
PublisherIEEE
Pages486-490
Number of pages5
ISBN (Print)9781424422050
DOIs
Publication statusPublished - 2008
Event5th International Multi-Conference on Systems, Signals and Devices - Amman, Jordan
Duration: 20 Jul 200822 Jul 2008

Conference

Conference5th International Multi-Conference on Systems, Signals and Devices
CountryJordan
CityAmman
Period20/07/0822/07/08

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Keywords

  • character-recognition
  • Arabic scripts

Cite this

AlKhateeb, J. H., Ren, J., Jiang, J., Ipson, S., & Haikal, E. A. (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 (pp. 486-490). IEEE. https://doi.org/10.1109/SSD.2008.4632863