Automatic Number Plate Recognition

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Seminar Report
Government Polytechnic College, Neyyattinkara Electronics and Communication
SEMINAR REPORT
ON
AUTOMATIC NUMBER PLATE RECOGNITION
Done By
DIVIN CHANDRAN C K
DIPLOMA IN ELECTRONICS AND COMMUNICATION
DEPARTMENT OF ELECTRONICS AND COMMUNICATION
GOVERNMENT POLYTECHNIC COLLEGE
NEYYATTINKARA
2017

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SEMINAR REPORT
ON
AUTOMATIC NUMBER PLATE RECOGNITION
Done By
DIVIN CHANDRAN C K
DIPLOMA IN ELECTRONICS AND COMMUNICATION
NEYYATTINKARA
2017

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NEYYATTINKARA
2017
Certificate
This is to certify that this seminar report is a bonafide record of the work done by
DIVIN CHANDRAN C K under our guidance towards the partial fulfillment of the
requirement for the award of Diploma in Electronics and Communication
Engineering of the Department of Technical Education, Kerala during the year 2017
Guided By, Sri. Sulficar A
Sri. Aravind Sekhar R HOD
Lecturer Electronics and communication

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ACKNOWLEDGEMENT
I take this opportunity to express our sincere gratitude and profound obligation to Sri. Sulficar
A, Head Of Department of Electronics and Communication Engineering, Government polytechnic
College Neyyattinkara.
I also wish to express my gratitude to Sri. Aravind Sekhar R, Sri. Pavitrakumar G, Smt. Reeya
George, Smt. Divya C for their help and encouragement done throughout this work.
Last but not the least, I am extremely grateful to all our friends without whose timely aid,
could not have completed the work successfully.
DIVIN CHANDRAN C K
(Reg.no: 14200145)

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CONTENT
 INTRODUCTION………….…………………………………...........….....................2
 MODULE – 1
BACKGROUND & OVERVIEW OF ANPR …..……………....................................3
1.1BACKGROUND …..……………………………………...........….....….3
1.1 AN OVERVIEW OF ANPR…………………………............…........…..6
 MODULE – II
PROCESS OF AUTOMATIC NUMBER PLATE RECOGNITION...........................9
2.1 PRE-PROCESSING……………………………………..........…............9
2.2 NUMBER PLATE LOCALIZATION……….………….........................9
2.3 CHARACTER SEGMENTATION……………...…….....................….14
2.4 CHARACTER RECOGNITION……………………….............…...….16
 MODULE - III
TYPES & PERFOMANCE OF ANPR SYSTEMS…………....................................18
3.1TYPES OF ANPR ……………………………..………….....................18
3.2ANPR PERFOMANCE AND POLICIES ……………...........……........22
3.3 ANPR POLICIES……………………..……………………...…......27
 MODULE – IV
ADVANTAGES & APPLICATIONS.. …………………………................................29
4.1 ADVANTAGES…………………………………............………...........29
4.2 APPLICATIONS………………………………...........…...……............32
 CONCLUSIONS………………………………………..............….……....…......34
 REFERENCE………………………………………………...….......................…35

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INTRODUCTION
Law enforcement agencies throughout the nation are increasingly adopting automated
number plate recognition (ANPR) technologies to enhance their enforcement and
investigative capabilities, expand their collection of relevant data, and expedite the tedious
and time consuming process of manually comparing vehicle license plates with lists of stolen,
wanted, and other vehicles of interest. Police officers, sheriff’s deputies, and other law
enforcement practitioners are often on the lookout for vehicles that have been reported stolen,
are wanted in connection with a crime or traffic violation, are suspected of being involved in
criminal or terrorist activities, are parking violation scofflaws, have failed to maintain current
registration or to comply with statutory insurance requirements, or any of a number of other
legitimate reasons. ANPR systems function to automatically capture an image of the
vehicle’s number plate, transform that image into alphanumeric characters using optical
character recognition or similar software, compare the plate number acquired to one or more
databases of vehicles of interest to law enforcement and other agencies, and to alert the
officer when a vehicle of interest has been observed. The automated capture, analysis, and
comparison of vehicle number plates typically occur within seconds, alerting the officer
almost immediately when a wanted plate is observed. Although the ANPR term includes a
specific reference to “automated,” it should be noted that human intervention is needed
insofar as the officer monitoring the equipment must independently validate that the ANPR
system has accurately “read” the number plate, that the plate observed is issued from the
same state as the one in which it is wanted, and to verify the currency of the alert, i.e.,
verifying that the reason this vehicle or the owner was wanted or of interest is still valid.
This National Institute of Justice (NIJ)–supported project was designed to assess and
document ANPR implementation and operational experiences among law enforcement
agencies in the United States, and to identify emerging implementation practices to
provideoperational and policy guidance to the field. Several data collection techniques were
used to gather information for this project, including:
1) A survey of law enforcement agencies to assess the scope of the current
ALPRimplementation, deployment, and operational uses,
2) Site visits to interview law enforcement practitioners and observe ANPRs system in
operation,
3) Reviewing documents and policies addressing ANPR implementation and use.

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MODULE - I
BACKGROUND & OVERVIEW OF ANPR
1.1 BACKGROUND
Owners of motorized vehicles driven on public thoroughfares are required by law to
annually register their vehicles with their state bureau or department of motor vehicles, and to
attach number plates that are publicly and legibly displayed. Vehicle license plates generally
consist of a series of alpha numeric characters that reference the license plate to the specific
vehicle registered (including the make, model, year, and vehicle identification number (VIN))
and the owner and/or lien holder of the vehicle. New York is credited as the first state to enact
legislation requiring vehicle registration on April 25, 1901, with California following in 1902.
In Delaware, where numbered number plates were first issued in porcelain in 1909 beginning
with a numbering sequence of 1000, the state changed the numbering scheme in 1910,
beginning with the number “1”, which is reserved for the Governor. Delaware number plates
are sold to the owner of the vehicle and can be passed down generation to generation. In 2008,
a man and his son paid $675,000 in private auction for number plate number “6”and this
figure was matched for Delaware license plate number “11” the following year.
Contemporary number plates, which measure 6 x 12 inches in the United States, feature
numbering schemes that vary from state to state. States typically use numbers or a
combination of letters and numbers in their vehicle number plates. Some states, like
Maryland, use stacked letters—one over the other.
Figure 1.1: Examples of Different State Number Plate Numbering Schemes
Connecticut is credited with being the first state to issue vanity plates beginning in
1937, when “motorists with good driving records were allowed to have plates with their
initials (2 or 3 letters).” In Texas any person, non-profit organization, or for-profit entity can
design a specialty license plate for consideration and potential adoption by the state for an
initial deposit of $4,615, which will be refunded to non-profit organizations after 500 of the

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plates are sold or renewed.A Texas plate with “PORSCHE” recently sold in private auction
for $7,500, “AMERICA” for $3,000, and “FERRARI” for $15,000.
Specialty plates generate substantial revenue for states. West Virginia, for example,
reports approximately $1.2 million in revenue from the sale of vanity number plates. Texas is
estimated to have generated approximately $2.1 million in revenue from the sale of specialty
plates through the first 10 months of 2010, Virginia projected potential revenue exceeding $1
million for the sale of specialty plates with company logos, and approximately $600,000 of
revenue generated in Nebraska in 2009 from sales of its “Huskers” license plates. The
California Legislature recently considered a bill to study the potential use of electronic
number plates which would show digital advertisements when the vehicle to which it is
attached is stopped for more than four seconds; the vehicle’s number plate number would
display when the vehicle is in motion.
1.1.1 POLICE USE OF NUMBER PLATE DATA
As noted above, law enforcement practitioners are often searching for vehicles that
have been reported stolen, are suspected of being involved in criminal or terrorist activities,
are owned by persons who are wanted by authorities, have failed to pay parking violations or
maintain current vehicle license registration or insurance, or any of a number of other
legitimate reasons. Victims and witnesses are frequently able to provide police with a
description of a suspect’s vehicle, including in some cases a full or partial reading of their
license plate number. Depending on the seriousness of the incident, officers may receive a list
of vehicles of interest by their agency at the beginning of their shift, or receive radio alerts
throughout the day, providing vehicle descriptions and plate numbers including stolen
vehicles, vehicles registered or associated with wanted individuals or persons of interest,
vehicles attached to an AMBER or missing persons alert, and “be on the lookout” or “BOLO”
alerts. These lists may be sizable depending on the jurisdiction, population size, and criteria
for the list, and can present challenges for the patrol officer.
Officers monitor traffic during patrol, searching for vehicles of interest among their
other duties. When a potential vehicle of interest is observed, the officer will typically
compare characteristics of the observed vehicle and driver with those of the wanted vehicle,
including the number plate number, if known. If warranted, the officer may stop the vehicle to

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further investigate. A number plate check will be run on the vehicle, either by the officer
using an in-field computer to initiate the query, or by radioing dispatch for the query. Results
of the query and of the officer’s interaction and investigation of the driver will assist the
officer in determining next steps.
In addition to spotting vehicles of interest, officers on patrol are also alert to vehicles
with expired or missing license plates and annual renewal tags. Failure to maintain current
number plate registration may indicate that one or more of several conditions have not been
met, including failure to secure vehicle insurance, failing compulsory safety and/or emissions
inspections, and simple failure to properly register the vehicle with the state motor vehicle
authority. Some jurisdictions may withhold vehicle registration renewal if the owner has
unpaid parking or traffic tickets. The lack of a plate or current tags may also indicate that the
vehicle has been stolen.
1.1.2 AUTOMATED NUMBER PLATE RECOGNITION
(ANPR) TECHNOLOGY
Automated Number plate recognition (ANPR) technology was invented in 1976 in the
Police Scientific Development Branch (PSDB), Home Office, United Kingdom. The
European Secure Vehicle Alliance (ESVA) notes that the “Provisional Irish Republican Army
(IRA) terrorist bombings in the City of London resulted in the establishment of the ‘ring of
steel’ in 1993 – a surveillance and security cordon using initially CCTV cameras. In 1997,
ANPR cameras, linked to police databases, were fitted at entrances to the ring of steel and
gave feedback to monitoring officers within four seconds.” Implementation continued over
the next several years with forces implementing ANPR systems.
The Home Office Police Standards Unit and the Association of Chief Polices Officers
(ACPO) began testing dedicated “intercept teams” using ANPR across nine police forces in
the multiphased “Project Laser” beginning 2002.The strategic intent of the ANPR strategy for
the Police Services was to “target criminals through their use of the roads.” Intercept teams,
typically ranged in size of 7 – 12 officers and equipped with ANPR, were designed to enable
police to engage criminality on the road and intercept vehicles and drivers wanted in
connection with crime, terrorism, and motor vehicle violations. An analysis of the Laser pilot
projects, which collectively produced over 46,000 arrests, concluded that “ANPR makes a
direct contribution to both national and force objectives and is used on a daily basis to engage

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criminals. In comparison to a number of other technology-enabled projects in the criminal
justice area, its success has been remarkable.”
Following success of the Laser pilots, the Police Standards Unit invested £32 million
for development of the National ANPR Data Centre (NADC) and a Back Office Facility
(BOF), which provide data storage and analytic tools for forces in England and Wales, and
support deployment of ANPR at national, regional and local levels. Implementing a single
technology platform in forces across the whole of England and Wales has enabled the UK to
implement universal business practices and technical and data standards. By the end of the
first quarter of 2010, the NADC was receiving approximately 10-12 million number plate
reads per day from over 5,000 ANPR cameras, had the capacity to receive up to 50 million
reads per day, and maintained a database of more than 7 billion vehicle sightings.
ALPR also has many applications beyond law enforcement. It is used by departments
of transportation to monitor travel time on key roadways for better traffic management (where
ALPR captures images of vehicles at two different points on a roadway and calculates travel
times between the two points), automated tolling and toll enforcement, access control, and
congestion charging, among other things.
1.2 AN OVERVIEW OF ANPR
ANPR systems generally consist of a high speed camera with an infrared (“IR”) filter
or two cameras—one high resolution digital camera and one IR camera—to capture images
of number plates; a processor and application capable of performing sophisticated optical
character recognition (OCR) to transform the image of the plate into alphanumeric characters;
application software to compare the transformed number plate characters to databases of
number plates of interest to law enforcement; and a user interface to display the images
captured, the results of the OCR transformation, and an alert capability to notify operators
when a plate matching an agency’s “hot list” is observed. The precise configuration of ALPR
systems varies depending on the manufacturer of the equipment and the specific operational
deployment.
ANPR systems are able to capture up to 1,800 plates per minute at speeds up to 120-
160 miles per hour. Systems range in cost from $10,000 - $22,000, depending on the

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manufacturer and the specific configuration specified, and agencies have often been able to
fund acquisition through federal grant funding sources.
1.2.1 CAMERAS
Camera hardware is significant to the front-end component of any ALPR system.
Since the initial image capture forms a critically important part of the ALPR system and will
often determine the overall performance, ALPR systems typically use still or video cameras
Fig 1.1:Cameras
Specialized for the task. Currently, many of the ANPR systems include a set of high
resolutiondigital and IR illuminated cameras which allow the ANPR system to capture
images under a variety of light and weather conditions.
1.2.2 USER INTERFACE
In vehicle-mounted ANPR systems, captured images are displayed on a user interface
either a dedicated computer for the ANPR system, or use of the in-field computer already
installed in the police vehicle so, the officer can be alerted when a vehicle on one of the hot
lists has been observed in the vicinity of the officer.
The user interface allows the officer to compare the ALPR OCR interpretation of the
number plate number to ensure the accuracy of the read and to see the larger, contextual
image to help the officer in identifying which specific vehicle has the plate of interest. In
addition, the user interface also typically enables the officer to manually enter plates on
vehicles of interest, manage hot list information, deal with alert queues, and run reports.

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1.2.3 SOFTWARE
Fig 1.2: operation through computer
As vehicles pass through the field of view of the ANPR camera a picture is taken of
number plate and the vehicle. A series of algorithms are performed on the image to isolate the
plate and render the alphanumeric characters into an electronically readable format. The
sophistication and complexity of each of these algorithms determines the accuracy of the
system. There are six primary algorithms that the software requires for identifying a number
plate:
1. Plate localization – Finding and isolating the plate on the picture.
2. Plate orientation and sizing – Compensates for the skew of the plate and adjusts the
dimensions to the required size.
3.Normalization – Adjusts the brightness and contrast of the image.
4.Character segmentation – Finds the individual characters on the plates.
5.Optical character recognition (OCR) – Translation of images of text into an electronically
readable format.
6.Syntactical/Geometrical analysis – Check characters and positions against state-specific
rules to identify the state of issuance for the number plate.

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MODULE - II
PROCESS OF AUTOMATIC NUMBER PLATE RECOGNITION
The process of Automatic Number Plate Recognition consists of four main stages:
1. Pre-processing
2. Number plate localization
3.Character segmentation
4.Character recognition
2.1 PRE-PROCESSING
As mentioned before, the system of automatic number plate recognition faces many
challenges. So, this step is essential to enhance the input image and making it more suitable
for the next processing steps. The first step done in the pre-processing is to apply minimum
filter to the image in order to enhance the dark values in the image by increasing their area.
This is mainly done to make the characters and the plate edges bold, and to remove the effect
of the light diagonal strips that appear in the characters and edges of the Egyptian license
plates. This process is followed by increasing saturation of the image to increase the
separation between colours. Then the image is converted to grayscale (taking the luminance
component of NTSC). Then, increasing the image contrast to separate the background from
highlights.
2.2 NUMBER PLATE LOCALIZATION
In this stage, the location of the Number plate is identified and the output of this stage
will be a sub-image that contains only the Number plate. This is done in two main steps.

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2.2.1 LOCATING A LARGE BOUNDING RECTANGLE OVER THE
NUMBER PLATE
In this step a rectangle that contains the number plate is located (this rectangle may
also has some extra parts from the four sides), and this rectangle is the input to the next step
for further processing (removing the extra parts, character segmentation then recognition).
Figure 2.1:Large bounding rectangle over the number plate-1
First, Sobel vertical edge detection is applied to the image. Then a threshold of 36
( this value is determined using trial and error) is applied, such that every edge with
magnitude less than 36 is considered false edge and is set to 0. Then a vertical projection
(projecting on the Y-axis) of the edge detected image is taken and smoothed using an average
filter with width equals 9. It’s obvious that the characters of the plate along with the plate’s
vertical edges will have very strong vertical edges. Moreover, these edges will sum up
horizontally in the vertical projection and a strong peak will appear in the rows of the plate
( These row will be called band). So, the approach is to take some number of peaks in the
vertical projection and processing each of them individually in the next steps and when a
successful band is found, the processing of the following bands is cancelled. The reason
being thinking more than one peak is that the image may contain objects that produce many
vertical edges ask these “false” edges may be centred in the same area so they will form a
peak that may be stronger than the peak of the plate itself.

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For each band, we take a sub-image referenced by this band and all subsequent
processing will be applied on this sub-image. Now the problem is to cut the band image from
the left and right to get a bounding rectangle over the number plate (Again, this rectangle
doesn’t have to be tight on the plate). For this sake, a vertical Sobel edge detection is applied
again, but the height is larger than the width of the filter, this is to decrease the effect of false
edges and noise, experimentally, the best size is 6x3 filter .
Again a threshold of 30 is applied for the same reason as before. Now, a horizontal
projection of the edge detected band image is taken (projection on the X-axis) and smoothed
using an average filter of large size this time, since there are gaps between the letters and the
projection will have many peaks at the x coordinates where letters exist but it will drop down
in the x coordinates of the gaps. So, smoothing it with average filter of large width will
resolve this problem and many number of peaks will be converted to one wide peak that
represents the range of the X-axis where the plate is located in that specific band we are
working with. The width of the average filter is taken to be the height of the band. Relating
the height of the band with the width of the average filter is very important since over-
smoothing of the projection will merge the plate peak with the other main peaks in the band
like the peak got from vehicle lamps for example (and it already explained why the width

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shouldn’t be very small). Now, a predefined number of peaks (It’s already explained why we
take more than one candidate peak not just the strongest one) will be selected from the
smoothed projection.
For each peak, a sub-image is taken according to the range of current peak. So, the
bounding rectangle of the license plate is located. This is will be the input to the next step.
2.2.2 DETERMINING THE EXACT LOCATION OF THE NUMBER
PLATE
Using the sub-image from the last step which contains the number plate with some
extra parts (if any), the following processing is applied to this sub-image. The number plate
may be skewed because of the angle of the camera while image acquisition process. And it is
very important to de-skew the plate to its original orientation, thus making the plate
Figure 2.4: Determining exact location of number plate-1
Figure 2.5:Determining exact location of number plate-2
aligned with the X and Y axes (The reason behind its importance will be clear below). So a
Hough transform is applied to the horizontally edge detected image in order to find the shear
parameters by which the image can be de-skewed to retrieve the standard orientation. After
this operation we have a plate with its axes aligned with the X and Y axes. Then a Gaussian
smoothing filter is applied to smooth the image and remove noise. This operation makes the
characters of the plate bold and increases the characters area along with the effect of
increasing contrast, and subsequently this will ease the process of segmentation and
recognition afterward.

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All the above is considered a pre-processing for this step. Next, we aim at finding the
exact band of the plate. In other words, the goal of this step is to cut the top and bottom extra
parts of the previously cut rectangle (but this time the cut will be accurate because we have
limited the area we are working with and moreover we de-skewed the plate). This is done
using the same idea we used previously to get the plate band. It consists of applying Sobel
vertical edge detection, then applying a threshold, then doing a vertical projection (projecting
on the Y-axis), Then getting the strongest peak in this projection and cut the image
accordingly using the range of this peak, thus cutting the exact plate band from the image and
leaving the top and bottom extra parts . This time just the strongest peak is taken since we
already limited the possibility that false edges appear when we cut a rectangle around the
plate and we are sure that the vertical edges produced by the plate’s characters are summed up
correctly in a limited number of rows due to the de-skew operation.
Figure 2.6:Determining exact location of number plate-3
We got rid of the top and bottom extra parts. But we still have extra parts from left
and right that have to be cut to end up with an exact rectangle around the plate. So, a stamp
filter is applied to the sub-image we got from the previous step. This filter is just a blurring
followed by a soft threshold operation. Now the white colour will dominate the plate area .
After this a horizontal projection is done then smoothed using average filter with width equals
40. Then we get the strongest peak from this projection. This peak corresponds to the plate
range on the X-axis. So, a sub-image is cut using the peak range. In many cases when the
colour of the vehicle is bright, the previous operation is not sufficient to cut all the extra
pieces from left and right. So, this is followed by getting Sobel horizontal edge detection,
applying a threshold, then getting the horizontal projection, then smoothing this projection
with average filter of size 40. Then we will get two points that will define range of the peak.

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The first point is the point with least x coordinate that has a value (from the smoothed
projection) greater than or equal the average value. The second point is the point with
maximum x coordinate that has a value greater than or equal to the average. We will cut the
image again using these two points we got. And this is the final plate that the next processing
stages will work on.
At the current moment we have a ”candidate” final plate. The next processing stages
are computationally expensive. Also using the fact that all the plates have a very similar (if
not exact) values for some measures like aspect ratio, contrast, average brightness, average
saturation in both the coloured and grayscale plate images. We can begin to reject the plates
based on the previous measures, such that, If we found that the current candidate plate for any
measure has a very far value from the ranges of values for the true plates, It’s simply rejected
and the processing continues on the next candidate plate. But a false plate may pass these
tests, and it will be rejected in subsequent stages. The next stage is to segment characters
from the plate that passed all the measures tests.
2.3 CHARACTER SEGMENTATION
This stage is meant for segmentation of the characters from the plate. The output of
this stage is a set of monochrome images foreach candidate character in plate.’
Figure 2.7: Character Segmentation-1
The first step in this stage is to convert the plate image to a binary image. This is done
using adaptive threshold with a window of size 11 (This is selected using trial and error).
Then a process of noise removal is applied. This is done by getting the connected components

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from the binary image based on the 8-neighbourhood using flood fill. For every component,
we decide if it’s a noise or not based on the aspect ratio of the component and based on the
number of pixels in that component. This is based on the fact that the characters of the plate
have a certain range of aspect ratio and a certain range of number of pixels. After removing
the noise components a maximum filter is applied to make the effect of thinning the
characters to make sure that no two components are merged. This is followed by a horizontal
projection, to detect the boundaries between the characters to be able to cut them
individually. The peaks in this projection correspond to the gaps between the characters. So,
we get all of these peaks and a rejection process is applied also, since a true plate has a fixed
range of gaps between characters. So, any plate that has number of peaks that do not fit in
that range, will be rejected. Also, there is a powerful rejection measure; it is the variance of
the characters width (the variance of the spaces between peaks). After this the characters are
cut according to the peaks of the previous projection. Then another set of measures are
computed to reject the false characters that may still exist after the noise removal operation.
These measures are aspect ratio, deviation from average height test, deviation from average
contrast, deviation from average brightness, deviation from hue, deviation from average
saturation. After rejecting false characters, if the number of characters is not located in a
predefined range, then the plate is rejected. Otherwise, the processing is continued.
Figure 2.8: Character Segmentation-2
and for every character a copy of its corresponding location in the grayscale is got. The gray
level histogram is computed for the sub-image of each character, This gray level histogram
will have a standard shape which is one peak at the dark values (this corresponds to the

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character’s pixels) and another peak at the bright values (this corresponds to the background)
and some small values between them. So, this gray level image is converted to binary using
the following procedure. First, we find two peaks in the histogram then we find the minimum
value in between, this will be the value of the threshold (thus, every pixel that has a gray level
value less than the mentioned value, will be converted to black, every other value will be
converted to white). This way for converting the grayscale image that contains only a
character to binary one proved to be effective. At this point we have a set of binary images
each contains one character and this is the output of this stage and the input to the next.
2.4 CHARACTER RECOGNITION
The goal of this stage is to recognize and classify the binary images that contain
characters received from the previous one. After this stage every character must have a label
and an error factor, and this error factor if greater than a predefined value will be used to
reject false characters accidently passed from the previous steps. For the sake of
classification, some features must be collected from the characters. The feature we work with
in this system is the chain code of the contour of the image after dividing it into four tracks
then into four sectors.
Figure 2.9: Character Recognition

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Also we used a feed forward artificial neural network trained with back propagation
with sigmoid activation function and the ANN is trained on the chain code feature of the
optimal characters images. The neural network has 4X4X8=128 input neuron, it also has 37
output neurons corresponds to the Arabic alpha-numeric set of characters except zero, it also
ceil (37+128)/2)=83 hidden neurons.
So, for every character we get the chain code feature and do a feed forward on the
trained FFNN (Feed Forward Neural Network) then the class the corresponds to the neuron
with the maximum value will the predicted class of that character. If the error exceeds a
predefined value then the character is considered a false one and rejected. The plate is known
to have a fixed range of characters that may appear in it, so if the total number of passed
characters does not match this range, then the plate is rejected. Otherwise, the number plate
number is found.

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MODULE - III
TYPES & PERFORMANCE OF ANPR SYSTEMS
3.1 TYPES OF ANPR
3.1.1 MOBILE ALPR SYSTEMS
Mobile ANPR systems use vehicle-mounted cameras to capture number plate data
and can be configured in a number of ways to meet specific agency needs. Typically, the
processor is located in the trunk of the vehicle and the data is processed locally to notify the
officer of a possible hit. ANPR cameras are affixed to a vehicle and can be either hardwired
or magnetmounted for a portable (vehicle to vehicle) configuration. They can be integrated
into the light bar, mounted on either the roof or trunk of the vehicle, or within covert housing.
Figure 3.1: Lightbar (left) and Covert (right) Mounted Mobile ALPR Cameras
SYSTEM PORTABILITY
Mobile ANPR systems can be hardwired to a vehicle or magnet-mounted, for a
portable (vehicle to vehicle) configuration. Magnet-mounted set-ups offer more flexibility
and allow the agency to relocate the system from one vehicle to another. Consideration
should be given, however, to the location of the hardware and connection cables on the
various vehicle models and the ease with which they can be transferred. Agencies should
weigh the pros and cons of each configuration against the technical and personnel resources
of their agency.

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VEHICLE SPACE AVAILABILITY
Mobile ANPR system components include cameras, processors, an interface screen,
and keyboard which need to be added to a vehicle. Consideration should be given to the
existing space limitations in both the vehicle cockpit and trunk.
NUMBER OF CAMERAS
Each camera added to the ANPR system on a vehicle provides an additional field of
view and increases the amount of data and images the processor must analyse.
DATA TRANSFER
A variety of methods exist to transfer hot list and ANPR data from the vehicle’s
computer processor. Consideration should be given to whether the agency plans to manually
transfer the hot lists and data files using a USB memory stick or automate the transfer using
wireless or cellular networks.
3.1.2 FIXED AND PORTABLE ANPR SYSTEMS
ANPR cameras that are permanently affixed to a structure such as a light pole, bridge
or overhead sign.
Figure 3.2: Stationary/Fixed ALPR Cameras on a Bridge and Utility Pole

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Fixed and portable ANPR systems require an installation design plan that includes
infrastructure to support the camera system. This infrastructure includes power for the system
and any networking that provides the ability to transmit data between the camera and the
command/information centre.
Some common considerations for fixed systems are:
• Existing physical infrastructure
• Site location
• Available power
• Available network infrastructure
• Number of cameras
• Dispatch requirements
EXISTING PHYSICAL INFRASTRUCTURE
A great deal of physical infrastructure already exists at key locations along roadways
or potential targets (e.g. sports stadium or power plant). Utilizing established infrastructure
can offer a number of advantages such as reduction in costs associated with setting up a site,
ease of access, and existing power connections. Consideration should be given however to
the agency responsible for the infrastructure as special permits and ongoing maintenance may
be required.
SITE LOCATION
When choosing site locations for fixed and portable ANPR units, consideration
should be given to whether officers will be routinely stationed nearby and their possible
response times.
AVAILABLE POWER
Fixed and portable systems require power at the location of the camera. The need for
power may limit the possible locations for mounting or require additional resources.

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AVAILABLE NETWORK INFRASTRUCTURE
Fixed and portable systems require network connectivity between the ANPR
system’scomputer processor (generally located with the camera) and the server receiving
database updates. The updates enable the processors at the camera location to identify
vehicles of interest that have been recently entered into the databases. Agencies should
consider how this network connectivity will be accomplished to ensure successful updates
are received and how the information will be secured.
DISPATCH REQUIREMENTS
Fixed and portable systems typically provide alert notifications to the
communications/operations centre. This increases the workload for the dispatch personnel.
Depending on the system configuration, the ANPR system may require an additional
computer screen for the dispatcher to monitor. Dispatch personnel need to be effectively
trained and be able to include the associated actions into their existing responsibilities. It is
also important to ensure that the dispatch facility has sufficient power and space for any
additional computers or servers the ANPR system may require
.
NUMBER OF CAMERAS
A fixed system typically requires the installation of one camera for each lane of traffic
being monitored. Multiple cameras at one location may improve the ability to locate a suspect
or wanted vehicle.

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3.2 ANPR PERFORMANCE AND POLICIES
A number of factors impact the performance of ANPR systems, and there are several
measures that are relevant to the overall performance of the technology.
1. Capture Efficacy – a measure of the effectiveness of ANPR units to capture the license
plate information of vehicles that pass through the field of view of ANPR cameras. For
example, if 100 cars pass the ALPR unit, what proportion/percentage of vehicles containing
license plates are actually captured (i.e., read) by the ALPR units?
2. Read Accuracy – a measure of the accuracy of ANPR system interpretation of captured
plates with the actual alphanumeric characters of the plate.
3. Matching Effectiveness – a measure of the effectiveness of ANPR units (really of their
underlying software matching algorithms) to accurately match number plates reads to records
contained in the agency’s hot list(s). For example, if the ANPR unit accurately captures or
reads only a portion of a vehicle’s plate, or misreads one or more characters on a vehicle
plate, is the unit (and its software) nevertheless able to match the plate with hotlist records
stored or accessed through the device (perhaps with a scoring factor related to the probability
of an actual match)? This is more a function of the software supporting the ANPR unit, the
calibration of matching algorithms, and a measure of the performance and elasticity of search
parameters.
4.Capture/Read Factors – there are a host of factors that may influence the ability of ANPR
units to capture and accurately read and match number plates. Capture/Read factors include
the following:
a. Character and/or plate colour
b. Plate design factors (logos, stacked characters, etc.)
c. State of origin (i.e., the state which issued the plate)
d. Plate covers or other obstructions (e.g., bent, dirty, trailer hitch obstruction,
etc.)
e. Plate location on the vehicle
f. Interval between vehicles
g. Vehicle speed
h. Lighting conditions (e.g., day vs. night)

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i. Weather conditions (e.g., snow, rain, fog)
j. ALPR equipment (e.g., age and/or ability of the ALPR camera)
k. ALPR implementation (e.g., camera angle)
5. Plate design: Each state has multiple license plate designs and plates vary substantially
from state to state. The shape of the characters, amount of contrast between a particular
state’s background and the colour of the license plate characters, and whether the characters
are raised or flat can all impact the accuracy of the OCR read. Some colours, especially
reddish tones, may be difficult for ALPR system OCR software to read.
Figure 3.3:Sample Plate Designs
POOR IMAGE RESOLUTION
Poor image resolution can result from several factors. License plates can be too far
away for the capabilities of the ANPR camera to capture and motion blur can also occur. Poor
lighting and low contrast due to overexposure, reflection, adverse weather conditions, or
shadows can also result in a poor image quality.
Figure 3.4: Poor Image Quality

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BENT, DIRTY, DAMAGED, OR MODIFIED PLATES
Because many ALPR systems use reflectivity and the contrast created by the
alphanumeric characters, plates that are bent, dirty, damaged, or modified may cause the
ALPR software to misidentify a character.
Figure 3.5:Bent, Dirty, or Damaged Plates
PLATE LOCATION
Occasionally, an object might obscure all or a portion of the license plate and interfere
with accurate OCR. Oftentimes the object is a tow bar, dirt on the license plate, or a loaded
bike rack; other times the object may be a ANPR circumvention device. The National
Policing Improvement Agency (NPIA) and the Association of Chief Police Officers (ACPO)
have specified minimum performance capabilities of ANPR technologies in capturing and
reading number plate information for UK and Schengen Community plates.
Figure 3.6: Obstructed Plates

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Table 3.1: ‘Capture’ and ‘Read’ rates for All United Kingdom (UK)
Type of System Capture Rate
Correct Read
Rate
Overall capture
& correct read
rate
Static ANPR Camera 98% 95% 93.1%
CCTV Integrated
ANPR
(Dual purpose CCTV
and
ANPR Camera)
85% 85% 72.0%
Mobile ANPR Camera
(Stationary)
98% 95% 93.1%
Mobile ANPR Camera
(Moving)
80% 85% 68.0%
Given the fact that in static ANPR implementations the camera capturing the license plate is
stationary, whereas mobile implementations involve mobile cameras and potentially mobile
target vehicles, variations in capture efficacy and read accuracy rates are expected and
observed. Slightly lower performance rates are acceptable for capturing and reading plates of
just Schengen member countries.

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Table 3.2: ‘Capture’ and ‘Read’rates of Schengen Community
Type of System Capture Rate
Correct Read
Rate
Overall capture
& correct read
rate
Static ANPR Camera 85% 80% 68.0%
CCTV Integrated
ANPR
(Dual purpose CCTV
and
ANPR Camera)
85% 80% 68.0%
Mobile ANPR Camera
(Stationary)
85% 80% 68.0%
Mobile ANPR Camera
(Moving)
75% 80% 60.0%
Law enforcement agencies in the United States must deal with license plates from other
states, as well as international jurisdictions, which can pose difficulties if the units are not
configured to “read” plates from multiple jurisdictions. ANPR manufacturers are constantly
upgrading their equipment and software to address this issue. Minimum performance
standards for ANPR in the United States are still at an early stage. IACP is managing an NIJ-
funded project to develop technical performance standards for ANPR systems. The goals of
the project are to a) identify the specific performance parameters that are critical to ALPR
functions, b) develop metrics to accurately measure their performance, and c) establish
protocols for the testing of the equipment by an independent laboratory.

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3.3 ANPR POLICIES
In order to be effective, ANPR technology must be properly implemented and
integrated into the daily operations of law enforcement agencies. Developing and enforcing
policies defining the strategic objectives of an agency’s program, training requirements,
deployment options, operating procedures, hot list management, proper use and maintenance
of the technology, and data collection, retention, sharing, and access enables law enforcement
to effectively manage ALPR.
Agency policies typically:
1. Define the ANPR system and its data as “for official use only” (FOUO),
2. Restrict and audit queries of the ANPR dataset, and
3. Require that all operators of the equipment receive proper training before use.
Essential components of that training include:
1. Ensuring that appropriate hot list information is as current and accurate as possible, and
2. Clear directions that when the ALPR unit matches an observed vehicle’s number plate with
a record in the hot list and alerts the officer (also known as a “hit”), that the officer must
verify
a. that the ALPR “read” was accurate (i.e., that the ALPR OCR software has
properly interpreted the license plate number),
b. that the issuing State matches the plate on record, and
c. that the circumstance that triggered the alert is still current, e.g., that the
vehicle is still wanted or stolen.
3. If the record in the hot list was created based, not on the status of the vehicle, but rather on
the status of the registered owner (e.g., the owner has an outstanding warrant for arrest, or has
had their driving privileges suspended or revoked), the officer must also be cognizant of the
fact that the driver may not be the registered owner.

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Additionally, depending on the nature of the alert (e.g., a “hit” on the Terrorist Watch List),
the officer may be directed to notify another agency (e.g., the Terrorist Screening Centre) and
hold the person, surreptitiously watch but not contact the person, or simply document the
contact and forward the information to others.
Nearly half of responding agencies (19 agencies, 48%) indicated that they had a
policy addressing ANPR use and operations, and six agencies (15%) noted they were in the
process of developing or planning one. Among agencies that have or are developing ALPR
policies, the policies usually address data access (68%), data retention (48%), and data
sharing (44%).
Table 5.3: Policy Issues Addressed by Agencies
Policy Issues n %
Data access 17 68%
Data retention 12 48%
Data sharing 11 44%
Deployment 6 24%
Data quality 4 16%
Other 4 16%

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MODULE - IV
ADVANTAGES & APPLICATIONS
4.1 ADVANTAGES
With the help of this technology, pre-paid parking members can be easily
differentiated from non-members.With the ability to capture the driver’s image, car hijacking
can be prevented to a large extent.By having a list of stolen cars and unpaid fines in the
database, identification of cars with such history can be easily made and real time alerts can
be obtained, as soon as the car is located on a highway or at a signal.Traffic congestions can
be lowered, by diverting different vehicles into different lanes with the help of their entry
permits.Those persons or vehicles whose presence is barred from entry can be easily
identified.
License Plate Recognition (LPR) technology uses image processing to identify vehicle
number plates. The technology is rapid and able to identify and record a license plate number
under most ordinary driving conditions, including when a car is moving at high speed and at
night. It offers advantages in law enforcement, security and vehicle access.
TRAFFIC ENFORCEMENT
Number plate recognition technology is an advantage in several areas of traffic
enforcement. For example, a number plate recognition device can be put at an intersection
and record the number plate of a car that runs a red light. A citation can then be sent to the
registered owner of the car. Similarly, an LPR device can be placed in an area where speeding
is a common problem, and record the infraction for follow-through with a citation.

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LAW ENFORCEMENT
Number plate recognition technology has several applications in areas of law
enforcement. An LPR device can be mounted on a patrol car to record plates of passing cars.
This information can then be compared with the law enforcement data base for vehicles
associated with a crime. This is useful in Amber alerts, finding stolen vehicles and executing
felony warrants.
VEHICLE ACCESS
An LPR system is also useful for vehicle access. On toll roads, these systems can be
used to allow cars to pass through toll gates without stopping. The LPR records the plate of
the car and associates the number with the registered owner. The bill can then be sent by mail.
This technology also can be used in secure, gated locations. When a vehicle recorded in a
database approaches a security gate, the system recognizes the number plate, and the gate
opens automatically. This allows the driver to pass without interruption and eliminates the
possibility of an entrance code being stolen.
DIFFICUTIES AND FUTURE SCOPE
 There are a number of possible difficulties that the software must be able to cope with.
These include:
 Poor file resolution, usually because the plate is too far away but sometimes resulting
from the use of a low-quality camera.
 Blurry images, particularly motion blur
 Poor lighting and low contrast due to overexposure and reflection or shadows.
 An object obscuring (part of) the plate, quite often a tow bar, or dirt on the plate.
 Read number plates that are different at the front and the back because of towed
trailers, campers, etc.
 Vehicle lane changes in the camera's angle of view during number plate reading.
 A different font, popular for vanity plates (some countries do not allow such plates,
eliminating the problem).
 Circumvention techniques

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 Lack of coordination between countries or states. Two cars from different countries or
states can have the same number but different design of the plate.
While some of these problems can be corrected within the software, it
is primarily left to the hardware side of the system to work out solutions to these difficulties.
Increasing the height of the camera may avoid problems with objects (such as other vehicles)
obscuring the plate but introduces and increases other problems, such as the adjusting for the
increased skew of the plate.
On some cars, tow bars may obscure one or two characters of the
number plate. Bikes on bike racks can also obscure the number plate, though in some
countries and jurisdictions, such as Victoria, Australia, "bike plates" are supposed to befitted.
Some small-scale systems allow for some errors in the number plate. When used for giving
specific vehicles access to a barricaded area, the decision may be made to have an acceptable
error rate of one character. This is because the likelihood of an unauthorized car having such
a similar number plate is seen as quite small. However, this level of inaccuracy would not be
acceptable in most applications of an ANPR system.
FUTURE SCOPE
In recent years, digitisation and IP-based communication networks
have allowed Automatic Number Plate Recognition (ANPR) to achieve ever-greater utility.
Digitisation and ANPR have grown alongside each other and it has
been natural for clients to include ANPR when upgrading, mainly because by the time
digitisation reached traffic enforcement clients had a more open mind towards computers and
expected their enforcement units to do what their home PC was able to. From the
manufacturer's perspective, our aim is to make the operational aspects of enforcement as easy
as possible and adding ANPR software to our digital units was a natural step.

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PERCENT GROWTH
The ANPR market will grow by around 25-30 per cent in the next five
years. Enforcement solutions combining, for example, speed measurement, camera and
ANPR will drive future growth, according to ErnoSzucs of ARH, Inc.Electronic identification
of vehicles using DSRC onboard units will always require some complementary, non-
intrusive technique such as ANPR. The reason is obvious: assuring road administrations that
every vehicle can be identified electronically would require complete agreement between all
countries and vehicle manufacturers. That's far from being possible at the moment.
4.2 APPLICATIONS
Automatic Number Plate Recognition has a wide range of applications since the
license number is the primary, most widely accepted, human readable, mandatory identifier
of motor vehicles.ANPR provides automated access of the content of the number plate for
computer systems managing databases and processing information of vehicle
movements.Below we indicated some of the major applications, without the demand of
completeness.
PARKING
One of the main applications of ANPR is parking automation and parking security:
ticketless parking fee management, parking access automation, vehicle location guidance,
car theft prevention, "lost ticket" fraud, fraud by changing tickets, simplified, partially or
fully automated payment process, among many others.
Access Control
Access control in general is a mechanism for limiting access to areas and resources
based on users' identities and their membership in various predefined groups. Access to
limited zones, however, may also be managed based on the accessing vehicles alone, or
together with personal identity. Number plate recognition brings automation of vehicle
access control management, providing increased security, car pool management for logistics,
security guide assistance, event logging, event management, keeping access diary,
possibilities for analysis and data mining.

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MOTORWAY ROAD TOLLING
Road Tolling means, that motorists pay directly for the usage of particular segment of
road infrastructures. Tolls are a common way of funding the improvements of highways,
motorways, roads and bridges: tolls are fees for services. Efficient road tolling increases the
level of related road services by reducing travel time overhead, congestion and improve
roadways quality. Also, efficient road tolling reduces fraud related to non-payment, makes
charging effective, reduces required manpower to process events of exceptions. Number plate
recognition is mostly used as a very efficient enforcement tool, while there are road tolling
systems based solely on number plate recognition too.
BORDER CONTROL
Border Control is an established state-coordinated effort to achieve operational
control of the country's state border with the priority mission of supporting the homeland's
security against terrorism, illegal cross border traffic, smuggling and criminal activities.
Efficient border control significantly decreases the rate of violent crime and increases the
society's security. Automatic number plate recognition adds significant value by event
logging, establishing investigate-able databases of border crossings, alarming on suspicious
passing’s, at many more.
JOURNEY TIME MEASUREMENT
Journey Time Measurement is a very efficient and widely usable method of
understanding traffic, detecting conspicuous situations and events, etc. A computer vision
based system has its well known downfalls in Journey Time Measurement, while Automatic
Number Plate Recognition has provided its viability: vehicle journey times can be measured
reliably by automatic number plate recognition-based systems. Data collected by number
plate recognition systems can be used in many ways after processing: feeding back
information to road users to increase traffic security, helping efficient law enforcement,
optimising traffic routes, reducing costs and time, etc.
LAW ENFORCEMENT
Automatic number plate recognition is an ideal technology to be used for law
enforcement purposes. It is able to automatically identify stolen cars based on the up-to date
blacklist. Other very common law enforcement applications are red-light enforcement and
over speed charging and bus lane control.

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CONCLUSION
The objective of this paper was to study and resolve algorithmic and mathematical
aspects of the automatic number plate recognition systems, such as problematic of machine
vision, pattern recognition, OCR and neural networks. The problematic has been divided into
several chapters, according to a logical sequence of the individual recognition steps. Even
though there is a strong succession of algorithms applied during the recognition process,
chapters can be studied independently.
Law enforcement agencies throughout the nation are increasingly adopting automated
license plate recognition (ALPR) technologies to enhance their enforcement and investigative
capabilities, expand their collection of relevant data, and expedite the tedious and time
consuming process of manually comparing vehicle license plates with lists of stolen, wanted,
and other vehicles of interest. Police officers, sheriff’s deputies, and other law enforcement
practitioners are often on the lookout for vehicles that have been reported stolen, are wanted
in connection with a crime or traffic violation, are suspected of being involved in criminal or
terrorist activities, are parking violation scofflaws, have failed to maintain current registration
or to comply with statutory insurance requirements, or any of a number of other legitimate
reasons.
ALPR systems function to automatically capture an image of the vehicle’s number
plate, transform that image into alphanumeric characters using optical character recognition
or similar software, compare the plate number acquired to one or more databases of vehicles
of interest to law enforcement and other agencies, and to alert the officer when a vehicle of
interest has been observed.
ANPR solution has been tested on static snapshots of vehicles, which has been
divided into several sets according to difficultness. Sets of blurry and skewed snapshots give
worse recognition rates than a set of snapshots which has been captured clearly. The
objective of the tests was not to find a one hundred percent recognizable set of snapshots, but
to test the invariance of the algorithms on random snapshots systematically classified to the
sets according to their properties.

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REFERENCE
[1] Shan Du ; IntelliView Technol., Inc., Calgary, AB, Canada ; Ibrahim, M. ; Shehata, M. ;
Badawy, Wael; Automatic License Plate Recognition (ALPR): A State-of-the-Art Review".
IEEE. 1 Feb 2013. Retrieved 2014-01-09.
[2] C.N.E. Anagnostopoulos, I.E. Anagnostopoulos, I.D. Psoroulas, V. Loumos and E.
Kayafas, License Plate Recognition From Still Images and Video Sequences: A Survey, IEEE
Transactions on Intelligent Transportation Systems 9 (2008), no. 3, 377–391.
[3] C.N.E. Anagnostopoulos, I.E. Anagnostopoulos, V. Loumos and E. Kayafas, A License
PlateRecognition Algorithm for Intelligent Transportation System Applications, IEEE
Transactions on Intelligent Transportation Systems 7 (2006), no. 3, 377–392.
[4]S.-L. Chang; L.-S. Chen; Y.-C. Chung; S.-W. Chen, Automatic License Plate Recognition,
IEEE Transactions on Intelligent Transportation Systems 5 (2004), no. 1, 42–53.
[5]S. Draghici, A neural network based artificial vision system for licence plate recognition,
International Journal of Neural Systems 8 (1997), no. 1, 113–126.
[6]B. Enyedi, L. Konyha, C. Szombathy and K. Fazekas, Strategies for fast license plate
number localization, Proceedings of the 46th International Symposium Electronics in Marine,
Elmar 2004, Zadar, Croatia, June 16-18, 2004, IEEE Press (2004), 579–584 .
[7]J.-W. Hsieh, S.-H. Yu and S.-H. Yu, Morphology-based License Plate Detection from
Complex Scenes, IEEE Proceedings of the 16th International Conference on Pattern
Recognition, Qubec City, Canada, August 11-15, 2002, Vol. 3 (2002), 176-179.
[8]O. Martinsky, Algorithmic And Mathematical Principles Of Automatic Number Plate
Recognition Systems, B.Sc. thesis, Department of Intelligent Systems, Faculty of Information
Technology, Brno University of Technology (2007).
[9]J.R. Parker and P. Federl, An Approach To Licence Plate Recognition, University of
Calgary 1996.
[10]H. Sarukhanyan, S. Alaverdyan, and G. Petrosyan, Automatic Number Plate Recognition
System, Proceedings of the 7th International Conference on Computer Science and
Information Technologies, Yerevan, Armenia, September 28-2 October, 2009, Electronic
Copy of the CSIT 2009 Proceedings, 347-350.

Automatic Number Plate Recognition

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