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Since automatic license plate recognition (ALPR) or automatic number plate recognition (ANPR) relies on optical character recognition (OCR) of images, it makes sense that a higher quality input image results in higher accuracy.
Unlike what is shown on TV, you cannot zoom into a blurry
image and expect to get more details. An image with acceptable sharpness and contrast must be acquired with the appropriate system from the start. This means the right image sensor, camera, optics, and lighting all combined in a reliable way.
Since automatic license plate recognition (ALPR) or automatic
number plate recognition (ANPR) relies on optical character
recognition (OCR) of images, it makes sense that a higher quality
input image results in higher accuracy.
Unlike what is shown on TV, you cannot zoom into a blurry
image and expect to get more details. An image with acceptable
sharpness and contrast must be acquired with the appropriate
system from the start. This means the right image sensor, camera,
optics, and lighting all combined in a reliable way.
OCR ALGORITHMS WORK
BETTER WITH HIGH
QUALITY IMAGES FOR
WHAT DEFINES GOOD
IMAGE QUALITY FOR
The ﬁrst step is to have reliable triggering in order to have the license plate in the proper
location in the image, which can be especially difﬁcult in multi-lane systems. After that, a
good/accurate image can be described by:
Free of artifacts
And sometimes with accurate color
The sources of these image quality issues can vary. Some possible reasons are shown in
the table below, and sharpness, contrast, and artifacts are further detailed in the following
Image Quality Parameter
Corresponding Source of Limitations
Image System Parameters to Control
• Limited depth of ﬁeld
• F value of lens
• Motion blur
• Sensitivity of image sensor
• Variable lighting
• Iris control
• Limited number of images
• Frame rate of the image sensor/camera
• Reﬂections of the license plate
• Dynamic range of the image sensor/camera
• Reﬂections of snow, rain, ﬂog
• Ghost images
• Alignment of ﬁlter, lens, and lighting.
• Bright spots and streaks from sun exposure
• Channel matching in the camera
• Blooming and smear control in the camera
Sharpness is one component of image quality. It indicates the clarity of an image and
therefore the amount of ﬁne details in the image. If all of the components in the vision
system are not well matched and aligned, the spatial details will be blurred. If you
match these well, the total accuracy of your ALPR system can be increased.
Especially in high speed ALPR systems such as open road tolling, it can be a challenge
to get the required sharpness. Here are some factors that impact sharpness and how to
DEPTH OF FIELD
A general deﬁnition of Depth of Field (DOF) is
the distance between the nearest and farthest
objects in a scene that appear acceptably
sharp in an image.
With an image for ALPR, the entire image
needs to be sharp so a very large depth of
ﬁeld is required.
A larger DOF is achieved with smaller iris
openings versus larger openings. A way to
allow for smaller iris openings is with a more
Motion blur is the fuzzy details that can appear
when capturing a still image of a fast moving
object, such as a car/license plate on the
Again, a lower F value of the lens can help
here as it allows for shorter exposure times to
better freeze the moving object. More sensitive
sensors also mean less light is required to get
a good image, thus enabling shorter exposure
Different license plates have different reﬂection coefﬁcients. For optimal results,
the wavelength of the IR lighting should be matched to the license plate.
Having a ﬁxed iris verses auto iris offers more control over the image. By taking
multiple images of the same object with different exposure times with a ﬁxed iris,
better control over the focus and exposure is achieved. Auto iris functionality can
generate a dynamic depth of ﬁeld and therefore fuzzy portions in the image.
Now that you have done what is possible
to get a sharp image for your OCR
USING CAMERAS WITH A
HIGH DYNAMIC RANGE
algorithm for automatic license plate
recognition, another critical image quality
Image sensors with high dynamic range
parameter that is critical is contrast.
can distinguish the foreground (the
license plate characters) better from the
Contrast is the difference in brightness
background. For license plates in certain
between the light and dark areas. Much
regions of the world, this is particularly
ﬁner details can be detected if the difference
challenging. If implemented properly,
between the light and dark areas is more
camera manufacturers ensure the full linear
dynamic range of the sensor is available.
They can even add functionality to increase
the dynamic range.
Some suggestions on ways to improve
contrast that are speciﬁc to the needs of
Poor reﬂection of light on the license plate
can limit the contrast. Different license
plates have different reﬂection coefﬁcients.
FRAME RATES OF A
As with optimizing sharpness, for optimal
results, the wavelength of the IR lighting
must be matched with the license plate to be
Cameras with higher frame rates allow
for multiple images to be taken of the
Snow, rain, and fog also reﬂect the IR
same object with different exposure times.
LED. Again, special attention to the IR
This way multiple images under different
wavelengths used will enhance the contrast
conditions are available, and the best one
of the image.
can be selected. There are now CCD
cameras available with 2MP HD resolution
and speeds of more than 60 frames/second.
For CMOS cameras, the speeds can be
more than 5 times higher.
Reducing image sensor artifacts is not a simple thing to do, but camera manufacturers
can help to remove or minimize certain artifacts that are speciﬁc to the needs of ALPR:
Ghost images can appear if Infrared (IR)
lighting is used in combination with a visible
light block ﬁlter. By using the correct ﬁlters,
ghost images can be decreased as long as
the ﬁlter is properly aligned with the lens,
camera, and the lighting. The simplest way
to prevent ghost images and lens artifacts
from interfering with the system performance
is to utilize a camera supplier that also has
the expertise to properly integrate the ﬁlter
and lens with the camera.
MANAGE BLOOMING AND
Blooming and smear are challenges with
outdoor vision systems, where blooming
and smear (streaks) are artifacts created by
saturation from very bright spots in a scene
(See Figure 5). Bright spots can originate
from headlights, reﬂections off license plates,
the sun at certain times of the year, or sun
reﬂecting on the road. Image processing in
the system cannot correct these artifacts so
blooming and smear must be managed in
the camera through special functionality to
ensure that the license plate is not obscured
in the original image data.
Even with effective management of blooming
and smear, direct sunlight can cause a poor
image if the image sensor channel matching
is insufﬁcient in the camera. Image sensors
usually have 2 or 4 readout channels that
need to be stitched together in the camera
to recreate the complete image. Cameras
with bad channel matching can deliver
images with one part overexposed and the
other part underexposed. This leads to poor
performance of the OCR algorithm.
With a higher quality of the input image, there is a better starting point for the license plate
recognition algorithm, and therefore the higher license plate recognition accuracy.
With proper alignment of the lens, ﬁlter, camera, and lighting, as well as specialized
functionality in the camera to deal with extreme lighting conditions of trafﬁc applications,
image artifacts are reduced or eliminated. When combined with optimized sharpness
contrast, the result is in high quality images.
This improves the efﬁciency of the OCR algorithm, providing the system integrator with a
better chance to win the tender contracts. In the end, the return on investment will be greater
and ultimately road safety is improved.
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