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A modular neural network classifier for the recognition of occluded characters in automatic license plate reading

Nijhuis, J A G ; Broersma, A and Spaanenburg, Lambert LU (2002) 5th International Conference on Computational Intelligent Systems for Applied Research (FLINS) p.363-372
Abstract
Occlusion is the most common reason for lowered recognition yield in free-flow license-plate reading systems. (Non-)occluded characters can readily be learned in separate neural networks but not together. Even a small proportion of occluded characters in the training set will already significantly reduce the overall recognition yield. This paper shows that a modular network can handle a realistic mixture of (non-) occluded characters with a 99.8% recognition yield per character.
Please use this url to cite or link to this publication:
author
; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
COMPUTATIONAL INTELLIGENT SYSTEMS FOR APPLIED RESEARCH : Proceedings of the 5th International FLINS Conference
editor
Da Ruan, Pierre D'hondt and Kerre, Etienne E
pages
363 - 372
publisher
World Scientific Publishing
conference name
5th International Conference on Computational Intelligent Systems for Applied Research (FLINS)
conference location
Gent, Belgium
conference dates
2002-09-16 - 2002-09-18
ISBN
978-981-238-066-1
language
English
LU publication?
no
id
d8939333-df10-45d5-ac94-5e84895e29ae (old id 603909)
date added to LUP
2016-04-04 10:31:04
date last changed
2021-01-25 10:54:20
@inproceedings{d8939333-df10-45d5-ac94-5e84895e29ae,
  abstract     = {{Occlusion is the most common reason for lowered recognition yield in free-flow license-plate reading systems. (Non-)occluded characters can readily be learned in separate neural networks but not together. Even a small proportion of occluded characters in the training set will already significantly reduce the overall recognition yield. This paper shows that a modular network can handle a realistic mixture of (non-) occluded characters with a 99.8% recognition yield per character.}},
  author       = {{Nijhuis, J A G and Broersma, A and Spaanenburg, Lambert}},
  booktitle    = {{COMPUTATIONAL INTELLIGENT SYSTEMS FOR APPLIED RESEARCH : Proceedings of the 5th International FLINS Conference}},
  editor       = {{Da Ruan, Pierre D'hondt and Kerre, Etienne E}},
  isbn         = {{978-981-238-066-1}},
  language     = {{eng}},
  pages        = {{363--372}},
  publisher    = {{World Scientific Publishing}},
  title        = {{A modular neural network classifier for the recognition of occluded characters in automatic license plate reading}},
  year         = {{2002}},
}