Recognizing license plate character based on simplified PCNN

Jun Wu; Zhitao Xiao

doi:10.1117/12.746815

15 November 2007 Recognizing license plate character based on simplified PCNN

Jun Wu, Zhitao Xiao

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67880I (2007) https://doi.org/10.1117/12.746815
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Jun Wu and Zhitao Xiao "Recognizing license plate character based on simplified PCNN", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880I (15 November 2007); https://doi.org/10.1117/12.746815

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