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Rule Based Neural Networks Construction for Handwritten Arabic City-Names Recognition

  • Labiba Souici
  • Nadir Farah
  • Toufik Sari
  • Mokhtar Sellami
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3192)

Abstract

A recent innovation in artificial intelligence research has been the integration of multiple techniques into hybrid systems. These systems seek to overcome the deficiencies of traditional artificial techniques by combining techniques with complementary capabilities. At the crossroads of symbolic and neural processing, researchers have been actively investigating the synergies that might be obtained from combining the strengths of these two paradigms. In this article, we deal with a knowledge based artificial neural network for handwritten Arabic city-names recognition. We start with words perceptual features analysis in order to construct a hierarchical knowledge base reflecting words description. A translation algorithm then converts the symbolic representation into a neural network, which is empirically trained to overcome the handwriting variability.

Keywords

Hide Markov Model Postal Address Artificial Intelligence Research Translation Algorithm Arabic Script 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Labiba Souici
    • 1
  • Nadir Farah
    • 1
  • Toufik Sari
    • 1
  • Mokhtar Sellami
    • 1
  1. 1.Departement d’informatique, Laboratoire LRIUniversity Badji MokhtarAnnabaALGERIA

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