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Proceedings Paper

Recognizing license plate character based on simplified PCNN
Author(s): Jun Wu; Zhitao Xiao
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Paper Abstract

In LPR system, character recognition subsystem is heavily affected by image quality. To resolve this problem and improve recognition rate, a new algorithm is proposed, in which pulse coupled neural network (PCNN) is applied into the recognition of license plate character. PCNN model is simplified to improve computation efficiency, and then is utilized to extract three features from dimension-normalized binary result of input character image. Based on these features, weighted voting is performed and final estimation of input character is made. The experiment results show that compared with common algorithms based on BP network, the new algorithm based on simplified PCNN model has higher total recognition rate and stronger robustness, and is more convenient and flexible.

Paper Details

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880I (15 November 2007); doi: 10.1117/12.746815
Show Author Affiliations
Jun Wu, Tianjin Polytechnic Univ. (China)
Zhitao Xiao, Tianjin Polytechnic Univ. (China)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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