Hierarchical system for character recognition with stochastic knowledge representation

J. A. Vlontzos, Sun-Yuan Kung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Hierarchical systems use schemata (knowledge sources) to represent knowledge of the environment but it is difficult for them to deal with the variability of the observed data. The authors describe a hierarchical system that uses the hidden Markov model (HMM) methodology to represent both general knowledge about objects and knowledge about their possible instantiations. The HMM is shown to be a compact, computationally efficient and accurate knowledge source. The authors discuss the algorithms used and their implementation using systolic arrays.

Original languageEnglish (US)
Title of host publicationIEEE Int Conf on Neural Networks
PublisherPubl by IEEE
Pages601-608
Number of pages8
StatePublished - Dec 1 1988
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Vlontzos, J. A., & Kung, S-Y. (1988). Hierarchical system for character recognition with stochastic knowledge representation. In IEEE Int Conf on Neural Networks (pp. 601-608). Publ by IEEE.