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Wordlength estimation for the enhancement of hand-written word recognition

Paschalakis, Stavros and Fairhurst, Michael and Allgrove, C. (1999) Wordlength estimation for the enhancement of hand-written word recognition. In: Seventh International Conference on Image Processing And Its Applications, 1999. Conference Publications . IEEE, pp. 750-754. ISBN 0-85296-717-9. (doi:10.1049/cp:19990424) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:16439)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1049/cp:19990424

Abstract

Although the problem of recognising machine printed words has been largely solved using available techniques, no ideal solution has been found for the problem of hand-written word recognition, especially with cursive script. This paper describes a method which can be used to estimate the length of hand-written words. The method shares a number of components with recognition techniques. It does not, however, aim to identify the word or any of its constituent characters; instead, it aims to directly identify the number of letters in the word as supporting information to aid more sophisticated recognition processes. The method of wordlength estimation has potential applications in many areas of text analysis. The work presented here concerns the application of the method in the field of automated bank cheque processing; more specifically, in the recognition of the legal amount field. It is also interesting to note that the method has been tested on two different languages, English and French (i.e. on words extracted from the legal amount field of both British and French cheques), in order to test its generic applicability.

Item Type: Book section
DOI/Identification number: 10.1049/cp:19990424
Additional information: Issue: 465
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: F.D. Zabet
Date Deposited: 01 May 2009 17:12 UTC
Last Modified: 16 Nov 2021 09:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/16439 (The current URI for this page, for reference purposes)

University of Kent Author Information

Fairhurst, Michael.

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