A hybrid neural network model in handwritten word recognition

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

A hybrid neural network model is developed and applied to handwritten word recognition. The word recognition system requires a module that assigns character class confidence values to segments of images of handwritten words. The module must accurately represent ambiguities between character classes and assign low confidence values to a wide variety of non-character segments resulting from erroneous segmentations. The proposed hybrid neural model is a cascaded system. The first stage is a self-organizing feature map algorithm (SOFM). The second stage maps distances into allograph membership values using a gradient descent learning algorithm. The third stage is a multilayer feedforward network (MLFN). The new system performs better than the baseline system. Experiments were performed on a standard test set from the SUNY/USPS Database.

Original languageEnglish
Pages (from-to)337-346
Number of pages10
JournalNeural Networks
Volume11
Issue number2
DOIs
Publication statusPublished - 1998 Mar 31

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence

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