Article: Enhanced structural perceptual feature extraction model for Arabic literal amount recognition Journal: International Journal of Intelligent Systems Technologies and Applications (IJISTA) 2016 Vol.15 No.3 pp.240 - 254 Abstract: One of the important applications for document recognition is the bank cheque processing, which is known as cheque literal amount. A few studies focused on Arabic bank cheque processing system compared to other systems, such as Latin and Chinese. The Arabic script has a number of characteristics that makes it unique among other scripts. It is known that humans are the best pattern recognisers. As such, the features detected while human reads the script can get better recognition rates. Therefore, proposing human reading inspired features (which are called perceptual features) can overcome the unique technical challenges in Arabic literal amount recognition. In this paper, the enhanced structural perceptual feature extraction model (PFM) has been proposed. Two main groups of features, which are the components and dots features and the loops and characters shapes features were combined to construct the PFM. This model was evaluated on standard Arabic Handwriting DataBase (AHDB) dataset. The PFM results outperformed the results reported in the previous studies. Inderscience Publishers - linking academia, business and industry through research

Title: Enhanced structural perceptual feature extraction model for Arabic literal amount recognition

Authors: Qais Al-Nuzaili; Siti Z. Mohd. Hashim; Faisal Saeed; Mohammed Sayim Khalil; Dzulkifli Bin Mohamad

Addresses: Faculty of Computing, Universiti Teknologi Malaysia, Johor, Skudai 81310, Malaysia ' Faculty of Computing, Universiti Teknologi Malaysia, Johor, Skudai 81310, Malaysia ' Faculty of Computing, Universiti Teknologi Malaysia, Johor, Skudai 81310, Malaysia ' Center of Excellence in Information Assurance, CoEIA, King Saud University, Riyadh, 11653, Saudi Arabia ' Faculty of Computing, Universiti Teknologi Malaysia, Johor, Skudai 81310, Malaysia

Abstract: One of the important applications for document recognition is the bank cheque processing, which is known as cheque literal amount. A few studies focused on Arabic bank cheque processing system compared to other systems, such as Latin and Chinese. The Arabic script has a number of characteristics that makes it unique among other scripts. It is known that humans are the best pattern recognisers. As such, the features detected while human reads the script can get better recognition rates. Therefore, proposing human reading inspired features (which are called perceptual features) can overcome the unique technical challenges in Arabic literal amount recognition. In this paper, the enhanced structural perceptual feature extraction model (PFM) has been proposed. Two main groups of features, which are the components and dots features and the loops and characters shapes features were combined to construct the PFM. This model was evaluated on standard Arabic Handwriting DataBase (AHDB) dataset. The PFM results outperformed the results reported in the previous studies.

Keywords: Arabic script; literal amount recognition; perceptual features; feature extraction; bank cheque processing; Arabic handwriting; handwriting recognition; document recognition; bank cheques.

DOI: 10.1504/IJISTA.2016.078353

International Journal of Intelligent Systems Technologies and Applications, 2016 Vol.15 No.3, pp.240 - 254

Received: 02 Jul 2015
Accepted: 19 Nov 2015
Published online: 15 Aug 2016 *

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Title: Enhanced structural perceptual feature extraction model for Arabic literal amount recognition

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