J. Software Engineering & Applications, 2010, 3, 1163-1166
doi:10.4236/jsea.2010.312136 Published Online December 2010 (http://www.SciRP.org/journal/jsea/)
An Automatic Recognizer for Iraqi License Plates
Using ELMAN Neural Network
Abdulhussein Mohsin1, Abbas H. Hassin1,2, Iman Qais Abdul Jaleel1
1Computer Science Department, Science College, Basra University, Basra, Iraq; 2Atlantic County Institute of Technology, New Jer-
sey, USA.
Received October 11th, 2010; revised November 25th, 2010; accepted December 1st, 2010.
ABSTRACT
License plate recognition system plays an important role in many applications. An automatic recognizer for Iraqi Li-
cense Plates using ELMAN Neural network is proposed in this manuscript. The processing procedures are developed in
several stages. Experimental results are reported in the end of the paper to illustrate the performance of the proposed
method.
Keywords: Four Quadrant (4Q) Converter, Interlacing , Traction Systems, Power Quality Analysis
1. Introduction
Currently, there are more than half a billion vehicles on
the worldwide [1]. All those vehicles have their identifi-
cation number as a primary identifier. The vehicle identi-
fication number is actually a license number which
means a legal license to participate in the public traffic.
Also, should be fixed onto its body (at least at the back-
side).
In fact, the manual methods for dealing with more
than half a billion vehicles are much difficult. Therefore,
an automatic system—called license plate recognition
(LPR)—is strongly needed. In general, LPR problem
could be classified
into two stages [2,3] plate image
processing (including detection of plate from vehicle
image, plate numbers and characters segmentation), and
recognition of the isolated plate number.
In the field of isolated plate number recognition, a lot
of conventional methods are used, for instance template
matching [2,3], MPL neural networks [4,5], mathe-
matical morphology [6], and image frequency analysis
approach (such as DFT) [7].
In this paper, we present a new automatic and simple
algorithm for recognizing Iraqi license plates. Basically,
this algorithm consists of four main stages: 1) image
acquisition. 2) preprocessing including cleaning of plate
region from any noise by using median filter and re-
moving unwanted lines by using morphological algo-
rithms. 3) segmentation of cleaned plate into its charac-
ters and numbers by using horizontal and vertical pro-
jection profiles. 4) recognition of the isolated plate
number.
1.1. Elements of LPR
Normally, LPR consists of the following items:
 Camera, that takes the images of the vehicle (front
or back side).
 Illumination, a controlled light that can bright up
the plate, and allows day and night operation. In
most cases illumination in Infra Red (IR) which is
invisible to the driver.
 Computer, normally a PC, includes software and
hardware requirements.
 Database, includes the vehicle information and rec-
ognition result.
1.2. Typical Application of LPR
There are several applications where automatic license
plate recognition can be used. Those include the fol-
lowing:
 Parking: the plate number is used to automatically
enter pre-paid members and calculate parking fee
for non-members (by comparing the exit and entry
times).
 Access Control: a gate automatically opens for
authorized members in a secured area, thus re-
placing or assisting the security guard. The events
are logged on a database and could be used to
search the history of events.

An Automatic Recognizer for Iraqi License Plates Using ELMAN Neural Network 1164
 Border Control: the car number is registered in
the entry or exit to the country, and used to mon-
itor the border crossings.
 Stolen cars: a list of stolen cars or unpaid fines is
used to alert on a passing ‘hot’ cars. The ‘black
list’ can be updated in real time and provide
immediate alarm to the police force.
 Enforcement: the plate number is used to produce
a violation fine on speed or red-light systems. The
manual process of preparing a violation fine is re-
placed by an automated process which reduces the
overhead and turnaround time. The fines can be
viewed and paid on-line.
 Traffic control: the vehicles can be directed to
different lanes according to their entry permits
(such as in University complex projects).The sys-
tem effectively reduces traffic congestions and the
number of attendants.
 Marketing tool: his car plates may be used to
compile a list of frequent visitors for marketing
purposes, or to build a traffic profile (such as the
frequency of entry verses the hour or day).
 Travel : a number of LPR units are installed in
different locations in city routes and the passing ve-
hicle plate numbers are matched between the points.
The average speed and travel time between these
points can be calculated and presented in order to
monitor municipal traffic loads. Additionally, the
average speed may be used to issue a speeding
ticket.
 Airport Parking: in order to reduce ticket fraud or
mistakes, the LPR unit is used to capture the plate
number and image of the cars. The information
may be used to calculate the parking time or pro-
vide a proof of parking in case of a lost ticket—a
typical problem in airport parking which have
relatively long (and expensive) parking durations.
2. License Plate Image Detection
In this stage, the license plate of vehicles are detected
using digital camera infixed in anywhere of the street
or in selected another place. When a vehicle passes in
front of the camera, an image of vehicle are captured and
stored for processing in next stage.
Unfortunately, captured image of Iraqi license plates
has different colors (back-ground and foreground), dif-
ferent font styles for each plate in the same city, and
noisy (see Figure 1), this makes the recognition of
characters and numbers more difficult.
3. Preprocessing
In order to recognize the elements of the license plate
accurately, image enhancements are required. Those en-
hanceements include cleaning of plate region from any
noise by using median filter and removing unwanted
lines.
This stage starts by converting the color image into
binary image. By thresholding the pixel values of 0 for
all pixels in the input image with luminance less than
threshold value and 1 for all other pixels (see Figure 2).
Median filter was used to remove significant noise
then Sobel operator has been used, which is composed
of two 3 × 3 masks, to determine the edge of the li-
cense plate. The plate may contains hard noise that the
median filter couldn’t remove because it is so enor-
mously large, for that reason we calculate the number of
columns, number of rows and the rate between them for
each character found in the filtered binary image. By
thresholding, we can determine whether the character
stays or to be removed from the license plate.
Median filter was used to remove significant noise
Figure 1. S amples of Iraqi license plate.
Figure 2. Captured and binary images.

An Automatic Recognizer for Iraqi License Plates Using ELMAN Neural Network 1165
then Sobel operator has been used, which is composed
of two 3 × 3 masks, to determine the edge of the li-
cense plate. The plate may contains hard noise that the
median filter couldn’t remove because it is so enor-
mously large, for that reason we calculate the number of
columns, number of rows and the rate between them for
each character found in the filtered binary image. By
thresholding, we can determine whether the character
stays or to be removed from the license plate.
4. Segmentation
Segmentation is an essential step in any practical LPR
system. There are many reasons that make the segmen-
tation task a difficult one, such as image noise, plate
frame rivet, and illumination variance. Before starting the
segmentation stage we determine whether the license
plate is written horizontally or vertically as shown in
Figure 3.
After determining the plate style, an algorithm has
been applied. If the plate style is vertical we determine
the values of the pixels in each column of the entered
plate, and then count the number of pixels of value 1 in
each column to determine the following:
1) The beginning and the end of the segment that con-
tains the numbers and characters by removing the sum
of columns with number of pixels < T (experimentally
T = 10).
2) Edge that separates the numbers from characters by
determining the column with number of pixels less than
its neighbors from left and right. Figure 4 shows this
process.
If the plate style is horizontal we use the vertical
search to find the sum of pixels of the plate characters.
To determine the edge between the plate numbers and
(a)
(b)
Figure 3. Th e style of Iraq i license plate. ( a) vertical style; (b)
horizontal style.
Figure 4. Example of segmentation algorithm.
other character, we use the smaller value between any
two maximum values.
5. Recognition
For accurate discrimination template should be retained
each number from 0 to 9 as well as the template for
each word represents the Iraqi provinces, such as Basra,
Najaf, and Baghdad as well as word that represents the
name of the state, i.e., Iraq and other words that appear
on the plate. In order to minimize the data used as in-
puts to the system of discrimination, we use the longest
section of the text in any word in the word “ةﺮﺼﺒﻟا” that
consists of three sections “ا” , “ﺮﺼﺒﻟ” and“ة”. In this
case, we use the section “ ﺮﺼﺒﻟ ” since the other province
names have no such section.
Two of Elman neural networks (ENN) topologies are
used in recognition stage. One for recognizing 10 num-
bers (0.9), another for recognizing 9 province names. A
function is developed in designing this ENN has three
layers consisting of input layer, hidden layer and output
layer. In the ENNs there will be 256, 20, 10 neurons
for input, hid- den, and output layers respectively as
shown in Figure 5 and Figure 6.
The value of element of each targets are all zero
except one element on specific position which represent
the number or text segment. For example, the first ele-
ment is 1 in number’s target, represent 0. To recognize,
output should be processed first by converted the highest
value to be 1 and other will be 0.Then, the location of
element which has value 1 will be founded and the
result will represent number or governorate name.

An Automatic Recognizer for Iraqi License Plates Using ELMAN Neural Network 1166
Figure 5. lman neural network of recognize numbers in
lraqi license plate.
Figure 6. Elman neural network of recognize characters in
Iraqi license plate.
Figure 7. Results of proposed algorithm.
Table 1. Experimental results.
Applied Stage Correct Detection Accuracy (%)
Segmentation 18/21 85%
Recognition 18/21 76%
6. Conclusions
In this paper, we introduced a new algorithm for Iraqi
license plate number recognition.
The test images were taken under various illuminations,
size and types of license plate conditions. The experi-
mental results for the LPR system were performed
under different illuminations, different distances and var-
ious types of license plate. The experimental results (see
Figure 7), show that the algorithm achieved about 85%
correct segmentation and 76% correct recognition for 21
samples as shown in Table 1.
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