A rotation, scaling, and translation invariant pattern classification system

Cem Yüceer, Kemal Oflazer

Research output: Contribution to journalArticle

43 Citations (Scopus)


This paper describes a hybrid pattern classification system based on a pattern preprocessor and an artificial neural network classifier that can recognize patterns even when they are deformed by transformation of rotation, scaling, and translation or a combination of these. After a description of the system architecture we provide experimental results from three different classification domains: classification of letters in the English alphabet, classification of the letters in the Japanese Katakana alphabet, and classification of geometric figures. For the first problem, our system can recognize patterns deformed by a single transformation with well over 90% success ratio and with 89% success ratio when all three transformations are applied. For the second problem, the system performs very good for patterns deformed by scaling and translation but worse (about 75%) when rotations are involved. For the third problem, the success ratio is almost 100% when only a single transformation is applied and 88% when all three transformations are applied. The system is general purpose and has a reasonable noise tolerance.

Original languageEnglish
Pages (from-to)687-710
Number of pages24
JournalPattern Recognition
Issue number5
Publication statusPublished - May 1993



  • Artificial neural networks
  • Deformation invariant pattern classification
  • Pattern recognition

ASJC Scopus subject areas

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

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