USING STROKE-BASED OR CHARACTER-BASED SELF-ORGANIZING MAPS IN THE RECOGNITION OF ONLINE, CONNECTED CURSIVE SCRIPT

L SCHOMAKER*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

35 Citations (Scopus)

Abstract

Comparisons are made between a number of stroke-based and character-based recognizers of connected cursive script. In both approaches a Kohonen self-organizing neural network is used as a feature-vector quantizer. It is found that a ''best match only'' character-based recognizer performs better than a ''best match only'' stroke-based recognizer at the cost of a substantial increase in computation. However, allowing up to three multiple stroke interpretations yielded a much larger improvement on the performance of the stroke-based recognizer. Within the character-based architecture, a comparison is made between temporal and spatial resampling of characters. No significant differences between these resampling methods were found. Geometrical normalization (orientation, slant) did not significantly improve the recognition. Training sets of more than 500 cursive words appeared to be necessary to yield acceptable performance.

Original languageEnglish
Pages (from-to)443-450
Number of pages8
JournalPattern recognition
Volume26
Issue number3
Publication statusPublished - Mar-1993

Keywords

  • CHARACTER RECOGNITION
  • ONLINE CURSIVE SCRIPT
  • NEURAL NETWORKS
  • SELF-ORGANIZING MAPS
  • TEMPORAL RESAMPLING
  • SPATIAL RESAMPLING
  • STROKE-BASED VS CHARACTER-BASED RECOGNITION

Cite this