An effective result-feedback neural algorithm for handwritten character recognition

X. Zhu, Y. Hao, Y. Shi, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

In this paper, a new algorithm of handwritten character recognition based on result-feedback is proposed. It is designed as an effective neural network by adding confidence back-propagation and input modification, thus both pre-processing and recognition operations are closely integrated together. The convergence of the algorithm is proved and many experiments show that the error rate in such a result-feedback neural network (RFNN) can be greatly reduced as well as the robust to environmental noise.
Original languageEnglish
Pages (from-to)139-150
Number of pages12
JournalNeural, parallel & scientific computations
Volume9
Issue number2
Publication statusPublished - 2001

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software
  • Applied Mathematics
  • Theoretical Computer Science

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