Multiple License Plate Detection for Complex Background
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Vehicle License Plate Location Based on Histogramming and Mathematical Morphology
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
An efficient method of license plate location
Pattern Recognition Letters
A license plate locating algorithm based on multiple gauss filters and morphology mathematics
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Learning-Based License Plate Detection Using Global and Local Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
License Plate Detection Based on Expanded Haar Wavelet Transform
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
A Color and Texture Feature Based Approach to License Plate Location
CIS '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security
Automatic license plate recognition system based on color image processing
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Automatic license plate recognition
IEEE Transactions on Intelligent Transportation Systems
License Plate Recognition From Still Images and Video Sequences: A Survey
IEEE Transactions on Intelligent Transportation Systems
Expert Systems with Applications: An International Journal
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Some previous works use discrete wavelet transform (DWT) to extract license plate (LP), however, most of them are not capable of dealing with complex environments such as the low-contrast source images and the dynamic-range problems. In this paper, we propose a license plate localization (LPL) algorithm based on DWT. The LP can be extracted from complex environments and different quality of source images by using two frequency subbands. We first use the HL subband to search the features of LP and then verify the features by checking whether a horizontal line around the feature exists in the LH subband or not.The proposed method can extract both front and back LPs of various vehicles. The experiments show that the proposed method can achieve good LPL results with both short run-time and high accurate detection rate.