This document presents a Matlab-based automatic number plate recognition (ANPR) system that uses template matching for optical character recognition of license plates. The system captures images using a webcam, processes the images in Matlab to isolate characters, and recognizes the characters by comparing them to templates. It then communicates the results to a PIC microcontroller to control a motorized barrier. The system achieved a 90% character recognition success rate under optimal lighting conditions using this approach. Recommendations for improving processing speed include implementing the system on dedicated hardware like an FPGA or ASIC.

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Abstract — The unavailability of adequate technologies to
constantly monitor the roads has led to many road traffic
violations going unpunished such as traffic light violations and
disregard of speed limits. Security in private premises is also
continuously seeking improvement. This has prompted many
authorities in many countries to introduce surveillance through
use of Video Image Processors. One very useful form of video
surveillance is identification by means of license plate recognition
also known as Automatic Number Plate Recognition (ANPR) and
it can be implemented in highway tolling, vehicle tracking and
access control. This document presents a Matlab based ANPR
system using the technique of template matching for optical
character recognition of the characters that appear on vehicle
number plates. Matlab was used to carry out morphological
image processing of captured images through a USB interfaced
camera on a PC. A PIC microcontroller was also implemented
and communication was established between Matlab and the
microcontroller through serial port communication to enable
access to successfully recognised number plates and manually as
well through a GUI. The project was able to successfully capture
images and process them to isolate and extract characters
through the template matching technique and communication
was established between the PIC microcontroller circuit and
Matlab.
Index Terms — ANPR, Matlab, OCR, Template Matching.
I. INTRODUCTION
icense Plate Recognition System (LPRS) or more
commonly referred to as Automatic Number Plate
Recognition (ANPR) is a very useful tool in the field of
machine vision and automation. Generally, license plate
recognition consists of two separate parts. First, plates have to
be located within the image, (license plate detection), followed
by license plate character recognition, (determining the plate
number). Different techniques or algorithms are used for
various systems of this nature. Some systems use a
combination of edge detection and vertical pixel projection;
others may make use of Mean-Shift algorithm or the Hough
Transform algorithm to identify the number plate and for the
optical character recognition. Most similar technologies that
use these mentioned techniques typically make use of DSPs,
ASIC and FPGA.
II. RELATED WORK
A lot of research work has been done in the field of ANPR,
but most of them make use of neural networks for the
identification purpose and cater to specific dimensions, font,
lighting and other conditions (Sudip Roy Kharagpur, 2007).
Cemil Oz and Fikret Ercal from Sakarya University, Computer
Engineering Department, Turkey; presented an Artificial
Neural Network based computer vision system which analysed
the image of a car taken by camera, locate the license plate
and recognise the number. An ANN tries to do the recognition
of characters which make up the plate. Other techniques
similar to the ones used in this project have also been
undertaken. Er. Kavneet Kaur and Vijay Kumar Banga, E.C.E
Department, A.C.E.T, Amritsar, Punjab, India; presented an
ANPR system based on template matching and tested it on
different ambient illumination conditions.
III. ANPR AND TEMPLATE MATCHING
The acronym ANPR stands for automatic number plate
recognition. CCTV cameras equipped with ANPR software
are mostly employed for this. They take pictures of vehicles as
Automatic Number Plate Recognition
(XtraSurv)
Mungofa Tanaka Ronald
Electronic Engineering Department
Harare Institute of Technology
Harare, Zimbabwe
tanakamungofa@gmail.com
L

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they travel on roads and motorways or as they request access
into restricted areas. Either artificial neural networks are
employed or various other image processing algorithms to
extract the characters off number plates for the desired use,
whether it is cross reference to a database or just keeping
record of vehicles passing a particular point. There are seven
primary algorithms that the software requires for identifying a
license plate (Steps 2, 3 and 4 shown in Figure 2.2.1):
1. Plate localization – responsible for 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.
6. Syntactical/Geometrical analysis – check characters
and positions against country-specific rules.
7. The averaging of the recognised value over multiple
fields/images to produce a more reliable or confident
result. Especially since any single image may contain a
reflected light flare, be partially obscured or other
temporary effect.
The complexity of each of these subsections of the program
determines the accuracy of the system. During the third phase
(normalization), some systems use edge detection techniques
to increase the picture difference between the letters and the
plate backing. A median filter may also be used to reduce the
visual noise on the image.
The main technique used for this project was Optical
Character Recognition (OCR). Optical Character Recognition
is the conversion of printed image, handwritten or typewritten
document into machine - editable form (International Journal
of Emerging Trends in Electrical and Electronics, May-2013.).
It has wide application in capturing documents through
various representations like scanned images or ancient scripts
in various languages into high level semantic descriptions of
the documents. It therefore involves image processing and
character recognition. The Optical Character Recognition
system can be subdivided into five major categories some of
which include System Architecture, Optical Mark Reader or
Intelligent Character Recognition (ICR) which is used to
describe the process of interpreting image data, in particular
alphanumeric text which is commonly used in license plate
recognition.
IV. MORPHOLOGICAL IMAGE PROCESSING
License plate recognition has had some previous
work done which involves morphological image processing
which is implemented in this project. Morphological image
processing is a collection of non-linear operations related to
the shape or morphology of features in an image (Nick Efford,
2000). It is used mostly for identifying particular defined
features in images hence is suitable for applications like facial
recognition and number plate recognition. Morphological
operations rely only on the relative ordering of pixel values
and not on their numerical values, therefore are especially
suited to the processing of binary images. Morphological
operations can also be applied to greyscale images such that
their light transfer functions are unknown and therefore their
absolute pixel values are of no or minor interest.
Morphological techniques probe an image with a small shape
or template called a structuring element or structural element
(Figure 1). The structuring element is positioned at all possible
locations in the image and it is compared with the
corresponding neighbourhood of pixels. Some operations test
whether the element "fits" within the neighbourhood, while
others test whether it "hits" or intersects the neighbourhood.
The structuring element is a small binary image, i.e. a small
matrix of pixels, each with a value of zero or one. The matrix
dimensions specify the size of the structuring element while
the pattern of ones and zeros specifies the shape of the
structuring element. An origin of the structuring element is
usually one of its pixels, although generally the origin can be
outside the structuring element.

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Figure 1. Probing of an image with structuring
element
V. SYSTEM OVERVIEW
This project was aimed at developing a system which is
able to capture images of vehicle number plates, identify the
number plate and extract, through optical character
recognition, the characters on the plate to display them in
digital form. The system that was developed uses the method
of template matching to identify characters off number plate
images via reference to a database of character templates.
The main constituents are an image capture device in the
form of a generic 8MP CMOS webcam which is triggered to
capture images by a Matlab program. Images are then sent to a
computer (Matlab) for image processing using algorithms
defined in Matlab. Consequent to the image processing, OCR
is carried out through the aforementioned template matching
and resulting characters are printed on a text file and
displayed. The Matlab side is also controlled by a customised
graphical user interface (GUI). Peripherals to the Matlab
ANPR system other than the image capture device include a
PIC microcontroller which receives a serially transmitted
signal through its UART port. The PIC microcontroller then
actuates the opening and closing of a motorised barrier
through an H-bridge motor driver.
Figure 2. System Block Diagram
The camera captures a full colour (Y’CbCr or R’G’B’)
image and streams it to the computer for processing with
Matlab. The Matlab code (algorithm) begins with image
conversion (pre-processing) which involves processes such as
those indicated in the block diagram; resizing the image,
converting from RGB to greyscale, eroding, dilating to
remove noise etc. Morphological processing then follows to
identify the plate region and segmentation allows isolation of
the individual characters. The template matching algorithm is
then applied resulting in the OCR. After all image processing
steps are carried out successfully, the realised alphanumeric
characters are the written to a text file which is opened to
display the characters. The serial port of the PIC
microcontroller then receives a signal from the computer to
actuate the opening of a motorised barrier in the event of
successful character extraction. The system is battery powered
with the computer using its rechargeable battery and the
peripheral control circuit also powered by a 12V rechargeable
battery. Regulators enable appropriate voltage levels to reach
every component in the system.
The project was aimed at developing an automatic number
plate recognition system. The system that was produced is
PC

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controlled by a GUI from a PC for demonstrative purposes of
each crucial stage in the number plate extraction. An image of
an incoming vehicle is captured by the image capture device
(camera), the image tries to focus mostly on the number plate
of the vehicle. This image is then transmitted to the PC for
processing in Matlab. Morphological image processing and
OCR are carried out to output the characters that are on the
vehicles number plate. This is followed by a signal being
serially transmitted to the PIC microcontroller (control
circuitry) which allows entry by opening a motorised barrier.
This is only done after successful plate extraction.
Figure 3. System Flow Chart
VI. PERIPHERAL CONTROL CIRCUIT
The system was developed to incorporate an automated
barrier opening and closing circuit and this was achieved by
linking Matlab GUI to a PIC16f877 microcontroller. The
choice of microcontroller was due to its ability to connect to
the RS232 protocol through its UART port. Any
microcontroller with UART functionality could have been
implemented in the same way. This microcontroller circuitry
design will now be looked at in this section.
Communication was established between Matlab and the
UART of the PIC via the computer’s RS232 port. However,
since later Windows versions after XP do not come with an
available RS232 port, a USB-to-Serial chip had to be installed
to enable serial transmission. Serial data was passed through
to the PIC via a MAX232 for CMOS to TTL conversion. The
PIC microcontroller was loaded with a program developed in
MikroC which enabled output pins on the PIC to send high or
low signal levels to an H-bridge motor driver (L293D).
According to delays defined within the PIC, the signals sent to
the H-bridge motor driver would turn the motor of the barrier
clockwise to open it and then anti-clockwise to close it. The
PIC’s UART was then configured to wait for the next string
from Matlab to repeat this action.
VII. RESULTS
A. Optical Character Recognition
The system gave output of characters both from the test
images that were saved as well as those captured for
processing. A variety of images taken from different lighting
settings, varied distance of capture and randomized numbers
were used for testing. The type of camera implemented
appeared to be largely affected by light conditions hence
images captured under intense light were not processed well.
This is probably because CMOS image sensors are very
susceptible to noise. Dim to average lighting gave the most
accurate results. Distant images would often include several
other objects within the frame hence the inaccuracies.
Elevated angle of capture would skew the characters on the
number plate and therefore the shape of characters would be a
mismatch compared with templates.
B. Overall System Performance
The entire system, when run under optimal conditions which
were in favour of the hardware that was implemented,
produced accurate character recognition of captured images of
vehicle number plates. Failed extraction showed the defined
NO
YES

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error message on a note pad and successful extraction
displayed the characters of the vehicle number plate on a
notepad together with the date of capture. An image without
any recognisable features (particularly a number plate or any
characters) returned a Matlab image processing error as there
were no characters to segment and match with templates.
Figure 4. Successful character recognition
C. Peripheral Circuit Performance
The PIC controlled circuit which was implemented to
control a motorised barrier behaved as desired. The
microcontroller program that was developed initiated
accurate motor triggers and delays through the H-bridge
driver with the barrier opening and closing as anticipated.
During testing using LEDs, Serial communication also
showed to have been executing appropriately with
successful interfacing of Matlab with the Microcontroller.
The H-bridge controlled motor ran clockwise for specified
duration of 2seconds and waited through a specified delay
of 4seconds and the rotated counter-clockwise for 2seconds
hence driving the barrier (gate) open and closed soon after
successful number plate extraction.
VIII. CONCLUSION
The ANPR system was successfully designed and
developed and both the hardware and software of the system
function with the purpose of meeting the objectives. Image
capture was successfully triggered by Matlab through calling
of the external camera. The images were also successfully
retrieved from storage for processing by the Matlab program.
Character extraction was also successfully done using the
template matching method of optical character recognition
with a 90% success rate under optimal conditions. The
interface of Matlab to a PIC microcontroller yielded a
successful communication link between the two and automatic
operation of a motorised barrier was realised.
IX. FUTURE RECOMMENDATIONS
This system was designed to be implemented on a PC and
due to other programs running concurrently on the computer,
processing speeds seemed to be relatively slow. A
recommended approach for such a system would be
implementation on a dedicated processor such as Field
Programmable Gate Array (FPGA) or Application Specific
Integrated Circuit (ASIC) for greater processing speeds. This
would entail designing the program to suit conversion to either
VERILOG or VHDL which are compatible with FPGAs and
ASICs.
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