Transcript Korean License Plate Extraction

An Approach to Korean License
Plate Recognition Based on
Vertical Edge Matching
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
Introduction


License plate recognition (LPR) has many
applications in traffic monitoring systems.
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

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:

1) Search the entire image row by row, for each
white pixel E,,, in image, if it has not been
checked, then run over the eight connected
white region by using seed filling algorithm in
which E,,, is adopted as the first starting seed of
the region.


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)
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.
License Plate Segmentation

Their backgrounds are green and yellow,
while the characters are white and dark blue,
respectively.
License Plate Segmentation

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.
Character Recognition

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.
Experiments and Analysis
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
Conclusion

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.