Neural Network Based OCR for Keg Identification

Keir, Andrew G., Lees, Michael, & Campbell, Duncan A. (2006) Neural Network Based OCR for Keg Identification. In Kovalerchuk, B. (Ed.) Second IASTED International Conference on Computational Intelligence, 20 - 22 November, 2006, San Francisco, California, USA.


A keg asset management system that can reduce the annual rate of keg attrition by 5% to 20% can deliver significant savings to breweries with large fleets of kegs. A typically large brewery can have at least tens of thousands of kegs, a sizable investment given an initial cost of around USD100 per keg. A key element in a keg tracking system is on-line keg identification. This research explores the feasibility of an intelligent machine vision approach to identifying the unique serial number embossed on the dome of each keg at manufacture. The demonstration system developed auto-locates candidate serial numbers and applies optical character recognition (OCR) techniques. The neural network based OCR achieved the best performance over template matching achieving an overall recognition rate of 92% and no missed digits. If non-permanent serial number occlusions can be removed by caustic washing prior to the image capture stage in a production line implementation, the recognition rate approaches 97%.

Impact and interest:

2 citations in Scopus
1 citations in Web of Science®
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708 since deposited on 11 Apr 2007
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ID Code: 6445
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Neural networks, OCR, keg tracking
ISBN: 0889866023
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2006 ACTA Press
Copyright Statement: Reproduced in accordance with the copyright policy of the publisher.
Deposited On: 11 Apr 2007 00:00
Last Modified: 29 Feb 2012 13:23

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