Mei Yu and Yong Deak Kim
Ajou University Suwon, 442-749, Korea
指導教授 張元翔
報告人員 陳昱辰

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License plate recognition (LPR) has many applications in traffic monitoring systems.
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Vehicle license plate recognition (LPR) is one form of automatic vehicle identification

Korean License Plate
Extraction

Edge Detection
It is noticed that most of vehicles usually have more horizontal lines than vertical lines.

Korean License Plate
Extraction
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Size-and-Shape Filtering
Binary size-and-shape filter is very useful in pattern recognition, because it is usually needed to recognize objects with special shapes in images.

For the binary image {E,,,}, the size-andshape filter basedon seed filling algorithm is described as follows:
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1) Search the entire image row by row, for each white pixel E,,, checked, then run over the eight connected white region by using seed filling algorithm in which E,,, the region.
in image, if it has not been is adopted as the first starting seed of


2) If it does not satisfy some predefined restricted conditions, then fill the region with black, that is,remove the region as noise, since it is impossible to be the region of interest (ROI)
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3) Continue to scan the image row by row to find another unchecked white pixel as the first starting seed of a new region, until all white pixels in the image have been checked.

Korean License Plate
Extraction

Edge Matching and License Plate
Extraction
The ratio of width to height of Korean license plate is about 2: 1, it can be used to judge whether two edge areas are the pair of vertical edges of a license plate.
The vertical coordinates of the two vertical edges of a license plate should have small difference.

Korean License Plate
Extraction
 after license plate is segmented, the percentage of character regions (white pixels) on a license plate is about from 10%to 40%. That is, if the percentage of character regions in the possible plate region is lower than 10% or higher than
40%,it can not be the real license plate region.


Their backgrounds are green and yellow, while the characters are white and dark blue, respectively.


Since luminance of different part of license plate may be not uniform because of the light condition, a license plate is separated into three or four parts when it is segmented. These parts are the part of region name and class code, the part of usage code, and the parts of serial number.


Template matching for character recognition is straightforward and can be reliable. Since characters on license plates have the same font, ternplate matching is employed for character recognition.

 the experiments are implemented in the following six aspects:
(1) license plates in normal shapes
(2) license plates that are out of shape or leaned due to the angle of view
(3) license plates which have similar color to vehicle bodies,
(4) damaged or bent license plates,
(5) dirtylicense plates,
(6) degraded images

RESULT

RESULT

RESULT

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The proposed algorithm is fast enough, the recognition unit of a LPR system can be implemented only in software so that the cost of the system can be reduced.