ROAD TO THE BEST ALPR IMAGES

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INTRODUCTIONSince 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 ALPR

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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:

Good Sharpness

Sufficient Contrast

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

sections.

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WHAT DEFINES GOOD IMAGE QUALITY FOR ALPR?

Image Quality Parameter Corresponding Source of Limitations Image System Parameters to Control

Sharpness Limited depth of field

Motion blur

Variable lighting

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F value of lens

Sensitivity of image sensor

Iris control

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Contrast Limited number of images

Reflections of the license plate

Reflections of snow, rain, flog

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Frame rate of the image sensor/camera

Dynamic range of the image sensor/camera

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Artifacts Ghost images

Bright spots and streaks from sun exposure

and reflections

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Alignment of filter, lens, and lighting.

Channel matching in the camera

Blooming and smear control in the camera

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SHARPNESSSharpness 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

overcome them:

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

sensitive sensor.

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LIGHTING

Different license plates have different reflection coefficients. For optimal results,

the wavelength of the IR lighting should be matched to the license plate.

IRIS CONTROL

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.

MOTION BLUR

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

highway.

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

times.

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CONTRASTNow that you have done what is possible

to get a sharp image for your OCR

algorithm for automatic license plate

recognition, another critical image quality

parameter that is critical is contrast.

Contrast is the difference in brightness

between the light and dark areas. Much

finer details can be detected if the difference

between the light and dark areas is more

pronounced.

Some suggestions on ways to improve

contrast that are specific to the needs of

ALPR:

EXPLOITING HIGHER

FRAME RATES OF A

CAMERA

Cameras with higher frame rates allow

for multiple images to be taken of the

same object with different exposure times.

This way multiple images under different

conditions are available, and the best one

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.

USING CAMERAS WITH A

HIGH DYNAMIC RANGE

Image sensors with high dynamic range

can distinguish the foreground (the

license plate characters) better from the

background. For license plates in certain

regions of the world, this is particularly

challenging. If implemented properly,

camera manufacturers ensure the full linear

dynamic range of the sensor is available.

They can even add functionality to increase

the dynamic range.

LIGHTING

Poor reflection of light on the license plate

can limit the contrast. Different license

plates have different reflection coefficients.

As with optimizing sharpness, for optimal

results, the wavelength of the IR lighting

must be matched with the license plate to be

measured.

Snow, rain, and fog also reflect the IR

LED. Again, special attention to the IR

wavelengths used will enhance the contrast

of the image.

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MINIMIZE ARTIFACTS

Reducing image sensor artifacts is not a simple thing to do, but camera manufacturers

can help to remove or minimize certain artifacts that are specifi c to the needs of ALPR:

PREVENTING GHOST

IMAGES

Ghost images can appear if Infrared (IR)

lighting is used in combination with a visible

light block fi lter. By using the correct fi lters,

ghost images can be decreased as long as

the fi lter 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 fi lter

and lens with the camera.

MANAGE BLOOMING AND

SMEAR

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, refl ections off license plates,

the sun at certain times of the year, or sun

refl ecting 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.

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IMPROVE CHANNEL

MATCHING

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.

CONCLUSIONWith 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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