A new recurrent neural-network architecture for visual pattern recognition

Seong Whan Lee, Hee Heon Song

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)


In this paper, we propose a new type of recurrent neural-network architecture, in which each output unit is connected to itself and is also fully connected to other output units and all hidden units. The proposed recurrent neural network differs from Jordan's and Elman's recurrent neural networks with respect to function and architecture, because it has been originally extended from being a mere multilayer feedforward neural network, to improve discrimination and generalization powers. We also prove the convergence properties of learning algorithm in the proposed recurrent neural network, and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeric database of Concordia University, Montreal, Canada. Experimental results have confirmed that the proposed recurrent neural network improves discrimination and generalization powers in the recognition of visual patterns.

Original languageEnglish
Pages (from-to)331-340
Number of pages10
JournalIEEE Transactions on Neural Networks
Issue number2
Publication statusPublished - 1997


  • Convergence properties
  • Recurrent neural network
  • Visual pattern recognition

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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