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IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of image processing operators
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Data compression via textual substitution
Journal of the ACM (JACM)
Fast Iris Detection for Personal Verification Using Modular Neural Nets
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Machine Graphics & Vision International Journal
Multi-Font Arabic Word Recognition Using Spectral Features
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
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Spectral features for Arabic word recognition
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A novel high-speed neural model for fast pattern recognition
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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Machine Graphics & Vision International Journal
Pattern detection using fast normalized neural networks
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Human face detection using new high speed modular neural networks
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology
A new fast neural network model
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
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Recentlly, electronic archives are becoming very important for office automation. Minimizing the size of the stored data in electronic archive is a main issue to reduce the phyzical storage area. In this paper, the effect of different types of Arabic fonts on electronic archives size is discussed. Simunation results show that PDF is the best file format for storage of the Arabic documents in electronic archive. Furthermore, an approach for fast word detection is in PDF file is presented. Such aprroch uses fast neural networks (FNNs) implemented in the frequency domain. The operation of these networks relies on performing cross correlation in the frequency domain rather than spatial one. It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). Simulation results using MATLAB confirm the theoretical computations.