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.
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Souici, L., Farah, N., Sari, T., Sellami, M. (2004). Rule Based Neural Networks Construction for Handwritten Arabic City-Names Recognition. In: Bussler, C., Fensel, D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2004. Lecture Notes in Computer Science(), vol 3192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30106-6_34
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DOI: https://doi.org/10.1007/978-3-540-30106-6_34
Publisher Name: Springer, Berlin, Heidelberg
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