Handwritten numeral recognition using self-organizing maps and fuzzy rules

Zheru Chi, Jing Wu, Hong Yan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

95 Citations (Scopus)

Abstract

Handwritten numeral recognition using combined self-organizing maps (SOMs) and fuzzy rules is presented in this paper. In the learning phase, the SOM algorithm is used to produce prototypes which together with corresponding variances are used to determine fuzzy regions and membership functions. Fuzzy rules are then generated by learning from training patterns. In the recognition stage, an input pattern is classified by a fuzzy rule based classifier. An unsure pattern is then re-classified by an SOM classifier. Experiments on a database of 20,852 handwritten numerals (10,426 used for training and a further 10,426 for testing) show that this combination technique achieves satisfactory results in terms of classification accuracy and time, and computer memory required.
Original languageEnglish
Pages (from-to)59-66
Number of pages8
JournalPattern Recognition
Volume28
Issue number1
DOIs
Publication statusPublished - 1 Jan 1995
Externally publishedYes

Keywords

  • Fuzzy rules
  • Handwritten character recognition
  • Self-organizing maps

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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