Multiresolution recognition of unconstrained handwritten numerals with wavelet transform and multilayer cluster neural network

Seong Whan Lee, Chang Hun Kim, Hong Ma, Yuan Y. Tang

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)

Abstract

In this paper, we propose a new scheme for multiresolution recognition of unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network. The proposed scheme consists of two stages: a feature extraction stage for extracting multiresolution features with wavelet transform, and a classification stage for classifying unconstrained handwritten numerals with a simple multilayer cluster neural network. In order to verify the performance of the proposed scheme, experiments with unconstrained handwritten numeral database of Concordia University of Canada, Electro-Technical Laboratory of Japan, and Electronics and Telecommunications Research Institute of Korea were performed. The error rates were 3.20%, 0.83%, and 0.75%, respectively. These results showed that the proposed scheme is very robust in terms of various writing styles and sizes.

Original languageEnglish
Pages (from-to)1953-1961
Number of pages9
JournalPattern Recognition
Volume29
Issue number12
DOIs
Publication statusPublished - 1996 Dec

Keywords

  • Handwritten numeral recognition
  • Multilayer cluster neural network
  • Multiresolution recognition
  • Wavelet transform

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Multiresolution recognition of unconstrained handwritten numerals with wavelet transform and multilayer cluster neural network'. Together they form a unique fingerprint.

Cite this