Number recognition using inductive learning on spiral architecture

He, X; Hintz, T; Wu, Q; Zheng, L

Number recognition using inductive learning on spiral architecture

He, X

Hintz, T Wu, Q

Zheng, L

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2005 International Conference on Computer Vision, VISION'05, 2005, pp. 58 - 62
Issue Date:: 2005-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (681.26 kB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Hintz, T	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Zheng, L	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	Proceedings of the 2005 International Conference on Computer Vision, VISION'05, 2005, pp. 58 - 62	en_US
dc.identifier.isbn	9781932415650	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7063
dc.description.abstract	In this paper, a number recognition algorithm on Spiral Architecture is proposed. This algorithm employs RULES-3 inductive learning method and template matching technique. The algorithm starts from a collection of samples of numbers or letters used in number plates. Edge maps of the samples are then detected based on Spiral Architecture. A set of rules are extracted using these samples by RULES-3. The rules describe the frequencies of 9 different edge masks appearing in the samples. Each mask is a cluster of 7 hexagonal pixels. In order to recognize a number plate, all characters (digits or letters) are tested one by one using the extracted rules. The number recognition is achieved by the frequencies of the 9 masks.	en_US
dc.relation.ispartof	Proceedings of the 2005 International Conference on Computer Vision, VISION'05	en_US
dc.title	Number recognition using inductive learning on spiral architecture	en_US
dc.type	Conference Proceeding
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	Las Vegas, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, a number recognition algorithm on Spiral Architecture is proposed. This algorithm employs RULES-3 inductive learning method and template matching technique. The algorithm starts from a collection of samples of numbers or letters used in number plates. Edge maps of the samples are then detected based on Spiral Architecture. A set of rules are extracted using these samples by RULES-3. The rules describe the frequencies of 9 different edge masks appearing in the samples. Each mask is a cluster of 7 hexagonal pixels. In order to recognize a number plate, all characters (digits or letters) are tested one by one using the extracted rules. The number recognition is achieved by the frequencies of the 9 masks.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/7063