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:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Neural networks, OCR, keg tracking|
|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|
Repository Staff Only: item control page