By Topic

Novelty detection with instance-based learning for optical character quality control

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Zhijun Pei ; Dept. of Electron. Eng., Tianjin Univ. of Technol. & Educ., Tianjin ; Huaxia Zhang ; Haiyan Ren

Novelty detection involves modeling the normal behavior of a system and detecting any divergence from normality which may indicate onset of damage or faults. Using instance-based learning, a novelty detection approach for optical characters quality control in machine vision inspection application is given in the paper. A normal characters information pattern adapted to special application can be established by training and products information can be effectively inspected with no delay for the print error can be automatically distinguished from print quality in the process, which has been verified by the experiment.

Published in:

Automation and Logistics, 2008. ICAL 2008. IEEE International Conference on

Date of Conference:

1-3 Sept. 2008