A comparison of a novel neural spell checker and standard spell checking algorithms

Research output: Contribution to journalArticlepeer-review


In this paper, we propose a simple and flexible spell checker using efficient associative matching in the AURA modular neural system. Our approach aims to provide a pre-processor for an information retrieval (IR) system allowing the user's query to be checked against a lexicon and any spelling errors corrected, to prevent wasted searching. IR searching is computationally intensive so much so that if we can prevent futile searches we can minimise computational cost. We evaluate our approach against several commonly used spell checking techniques for memory-use, retrieval speed and recall accuracy. The proposed methodology has low memory use, high speed for word presence checking, reasonable speed for spell checking and a high recall rate.
Original languageEnglish
Pages (from-to)2571-2580
Number of pages9
JournalPattern recognition
Issue number11
Publication statusPublished - Nov 2002

Bibliographical note

Copyright © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. This is an author produced version of a paper published in Pattern Recognition. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.


  • binary neural spell checker
  • associative matching
  • supervised learning
  • accuracy
  • memory usage

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