2008 5th International Multi-Conference on Systems, Signals and Devices 2008
DOI: 10.1109/ssd.2008.4632863
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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 ofhandwritten Arabic scripts. Techniques are discussed in details in terms ofthree stages in the system, i. e. preprocessing, feature extraction and classification. Firstly, words are segmented from inputted scripts and also normalized in size. Then, DCTfeatures 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 (ver… Show more

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Cited by 35 publications
(30 citation statements)
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“…Recognition rate was 91.70% on the IFN/ENIT Arabic standard database. AlKhateeb 2011 [6] used DCT features which extracted from each word sample, then features are fed to train a neural network for classification. The proposed system tested on IFN/ENIT Database each time 80% of the samples in the database are used for training and the remaining 20% for testing.…”
Section: Related Workmentioning
confidence: 99%
“…Recognition rate was 91.70% on the IFN/ENIT Arabic standard database. AlKhateeb 2011 [6] used DCT features which extracted from each word sample, then features are fed to train a neural network for classification. The proposed system tested on IFN/ENIT Database each time 80% of the samples in the database are used for training and the remaining 20% for testing.…”
Section: Related Workmentioning
confidence: 99%
“…Where and One of the characteristics of DCT is its ability to convert the energy of the image into a few coefficients [14]. DCT is applied on the low-frequency sub-band (LL) of DWT image to extract DCT coefficients.…”
Section: Discrete Cosine Transformmentioning
confidence: 99%
“…This is a relatively high recognition rate, though the number of classes that needed to be recognised (34) was relatively low. In a similar study, whole word recognition was applied to 500 Tunisian town/village names using an artificial neural network and a recognition accuracy of 82.5% was achieved [3]. In another study involving Arabic character recognition, in addition to moment features, the "number of dots" and "number of holes" were also extracted, which described the diacritics in more detail [1].…”
Section: Related Workmentioning
confidence: 99%
“…The 2-D coefficients of the DCT are converted to a 1-D vector using a technique called zig-zagging [3] (Figure 7 (b)), the purpose of which is to extract the low frequency coefficients from the DCT first. In this study, the extraction of features varied in terms of the size of the cells that the image or sliding window was partitioned into for the DCT-II calculation and the number of DCT coefficients used as features.…”
Section: Discrete Cosine Transformmentioning
confidence: 99%