License plate extraction is considered to be the most crucial step of Automatic license plate recognition (ALPR) system.
In this paper, a region-based license plate hybrid detection method is proposed to solve practical problems under
complex background in which existing large quantity of disturbing information. In this method, coarse license plate
location is carried out firstly to get the head part of a vehicle. Then a new Fast Mean Shift method based on random
sampling of Kernel Density Estimate (KDE) is adopted to segment the color vehicle images, in order to get candidate
license plate regions. The remarkable speed-up it brings makes Mean Shift segmentation more suitable for this
application. Feature extraction and classification is used to accurately separate license plate from other candidate regions.
At last, tilted license plate regulation is used for future recognition steps.
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