Alhamzawi, Hussein Ali Mezher

University of Debrecen Electronic Archive

IJCSN - International Journal of Computer Science and Network, Volume 6, Issue 6, December 2017         
ISSN (Online) : 2277-5420        
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Impact Factor: 1.5 
 
 
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Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved. 
 
Automatics Vehicle License Plate Recognition 
using MATLAB 
 
 
Alhamzawi Hussein Ali mezher  
 
Faculty of Informatics/University of Debrecen 
 Kassai ut 26, 4028 Debrecen, Hungary. 
 
 
Abstract - The objective of this paper is to be able to detect the area of a car license in a photograph. Using computer vision and 
digital image processing techniques. The different processes to which a photograph of a car is subjected will be explained in order to 
detect its registration. It will be indicated under what conditions the presented algorithm is effective and correctly detects the 
registration and under what conditions it can produce false positives. Indicate that the algorithm will only detect the area where the 
license plate is located, adjusting as much as possible the size of this zone to the size of the license plate. 
 
Keywords - Median filter , canny edge detection, image convolution , image binarization. 
 
1. Introduction 
 
he ANPR ( Automatic Number Plate Recognition 
or ANPR in English ) is a method of mass 
surveillance which uses optical character 
recognition on images to read the license plates of 
vehicles. In 2005, systems can scan license plates 
with an approximate frequency of one per second in 
vehicles with speeds of up to 160 km / h. They can use the 
existing closed circuit television or radars, or ones 
designed specifically for this task. They are used by the 
various police forces and as a method of electronic toll 
collection on the payment highways, and to monitor 
traffic activity as a red light at an intersection. 
 
The ANPR can be used to store the images captured by 
the cameras, as well as the license plate text, and some 
can be configured to store a photograph of the driver. 
These systems often use infrared illumination to make it 
possible for the camera to take pictures at any time of the 
day. At least one camera version for intersection 
monitoring includes a powerful flash, which serves to 
illuminate the scene and make the offender aware of his 
error. The ANPR technology tends to be region-specific, 
due to the variation between license plates from one place 
to another.  
 
The system software runs on a standard PC hardware and 
can be linked with other applications or databases. First, it 
uses a series of image manipulation techniques to detect, 
normalize and enhance the image of the license plate 
number, and finally, optical character recognition to 
extract the alphanumeric from the license plate. The 
ANPR / ALPR systems can be used in two ways; one 
allows the process to be performed in its entirety in the 
place of the shot in real time, while the other transmits all 
images from many cameras to a remote computer where  
 
the OCR process is performed later. When done in situ, 
the information captured from the alphanumeric 
registration, date and time, identification of the place and 
any other information that is required is completed in 
about 250 milliseconds. This information, now converted 
into small data packets, can be easily transferred to a 
remote computer for further processing if necessary or 
stored in the place to be retrieved later.  
 
In the other arrangement, there is typically a large number 
of PCs used in a server farm to handle high workloads, 
such as those found in the London Congestion Charge 
project. Often in such systems, there is a need to send 
images to the remote server and this may require high 
bandwidth transmission means. The drawbacks of these 
systems are centered on the fear regarding the privacy of 
citizens' movements and media reports about 
misidentification and high error rates. However, as they 
have been developed, these systems have been much more 
accurate and reliable. 
 
1.1. Methodology 
 
The proposed algorithm is designed to recognize license 
plates of cars currently used in Europe , both those located 
at the front and at the rear. This algorithm will take as 
input an image of a vehicle, perform a pre-processing 
process to adjust the quality of the image, then mark the 
area of the image where the license plate is located, be 
making segmentation, and finally proceed to the location 
and extraction of the license plate.  In summary, the steps 
of the algorithm are: 
 
1-Pre-processed image 
2-Segmentation of the image. 
3-Location and extraction of the license plate 
T
IJCSN - International Journal of Computer Science and Network, Volume 6, Issue 6, December 2017          
ISSN (Online) : 2277-5420        
www.IJCSN.org 
Impact Factor: 1.5 
 
 
678 
 
Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved. 
 
Captured image from the camera 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                              Output Image 
 
                 Fig.1 License Plate Recognition diagram 
 
1.2-Pre-processed image 
 
The input image will consist, as a general rule, of a color 
RGB image. The first step will be to scale the image to a 
suitable size for further treatment. In this case, the image 
is scaled to a size of 480 × 640. Then, we proceed to the 
conversion of the image to a grayscale, eliminating the 
information of tone and saturation but maintaining the 
luminance. The result of these two processes is the 
following: 
 
 
 
Fig. 2 scale the image to a suitable size 
 
Once the image is converted, it is subjected to a 
smoothing process in order to eliminate the noise. For this 
purpose, an order filter is applied, in this case, the median 
filtering has been used. Order filters are based on methods 
that use image statistics, known as order statistics. In the 
case of filtering the median for each pixel, the intensities 
are ordered by their neighboring pixels, the median is 
determined and this value is assigned to the pixel. The 
size of the neighborhood that has been used is 5 × 5. The 
result of this process is an image with less noise and 
greater smoothing. 
 
 
 
Fig. 3  using median filter to smooth the image 
 
2. Segmentation of the image 
 
The next step is to segment the image to locate the 
possible area where the license plate is located. This step 
does not pretend to detect the registration, but only to 
segment the image to stay with the best area of the image 
where the registration is possible.  
The steps to be taken in this process are: 
 
1- Binarization of the pre-processed image. 
2- Calculation of intensities by rows. 
3- Crop the image. 
 
2.1 Binarization of the pre-processed image 
 
A process of binarization is applied to the resulting image 
of the pre-processing in order to detect its edges. In this 
case, the image is processed using the Canny algorithm. 
This algorithm is based on the theory of first derivative 
operators and is especially useful since, in addition to 
extracting edges, it closes the contours avoiding possible 
ruptures. The threshold used for this algorithm was 50. 
The result of this process is the following: 
 
 
 
Fig.4 binarization the plate region 
 
2.2 Calculation of intensities by rows 
 
Once binarized the image proceeds to perform a 
calculation of intensities by rows. This calculation is 
based on several assumptions: 
 
1- The area where the registration is will have a 
very intense row  (1's) zones very close to each 
Pre-processing image 
Segmentation of the 
image 
 
 
 
 
 
 
 
 
 
 
 
Binarization of the 
pre-processed image 
Calculation of 
intensities by rows 
Crop the image 
Location and 
extraction of the 
license plate 
 
 
 
 
 
 
 
 
 
 
Morphological 
transformations 
Edge detection 
Image convulsion 
Calculation and 
extraction of regions 
Removal of the license 
plate 
IJCSN - International Journal of Computer Science and Network, Volume 6, Issue 6, December 2017          
ISSN (Online) : 2277-5420        
www.IJCSN.org 
Impact Factor: 1.5 
 
 
679 
 
Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved. 
 
other. Keep in mind that the registration will 
occupy several rows. 
2- A row containing a piece of the license plate will 
follow a certain pattern. That is, the numbers will 
have blank space between them and in their 
region. 
 
The algorithm consists of traversing all the rows of the 
binarized image one by one. For each row, its value will 
be increased by 1 (starting from 0) as long as we meet 
some of the following patterns in their pixels: (0, 0, 1) or 
(1, 0, 1). The idea is to penalize those areas with 
continuous borders or without borders and favors those 
rows with discontinuous edges, a pattern that will follow 
the numbers of a license plate.  
 
The result of this process is the following graphic: 
 
 
Fig.5 intensities of the plate region 
 
If we look at the graph we can see that in the area of the 
registration is where the rows are concentrated with 
greater intensity. 
 
2.3 Crop the image 
 
From the calculation of intensities, the row with greater 
intensity is obtained. Once this index is obtained, the 
image 81 is trimmed up and down, size enough to contain 
the license plate. 
 
 
 
Fig.6 crop the plate region 
 
3. Location and extraction of the license plate 
 
Once the image is trimmed, the last step is to locate and 
extract the license plate. To do this, the image is subjected 
to several processes: 
 
1- Morphological transformations 
2- Edge detection 
3- Image convulsion 
4- Calculation and extraction of regions 
5- Removal of the license plate 
 
3.1 Morphological transformations 
 
This first step is to perform a series of morphological 
transformations in the image. These transformations will 
allow us to soften the edges of the different regions of the 
image (specifically the region of the license plate), 
separate the different regions and facilitate their 
computation. 
 
Three types of transformations will be used: 
 
1- First of all, an erosion of the image is applied. 
This morphological transformation combines 
two sets using vector subtraction. One of these 
sets will be the image resulting from the previous 
processes and the other will be the following: [0 
0 1 0 0; 0 1 1 1 0; 1 1 1 1 1; 0 1 1 1 0; 0 0 1 0 0]. 
2- Then the transformation called opening is 
calculated. To calculate this transformation to the 
result of applying erosion to the image, a dilation 
process is applied. This transformation, dilation, 
combines two sets using the addition of vectors. 
3- Finally, the image is closed. For this the result of 
the erosion is subtracted from the result of the 
opening. 
 
 
The result is an image with perfectly defined edges and 
closed regions, which will allow detecting the position of 
the license plate. 
 
 
 
Fig.7 showing the edge and position of the license plate. 
 
3.2 Edge detection 
 
the image is then subjected to a binarization process in 
order to detect its edges. The Canny algorithm is reused 
with the same parameters as in the previous steps. 
 
 
 
Fig.8 canny algorithm for the license plate. 
 
 
IJCSN - International Journal of Computer Science and Network, Volume 6, Issue 6, December 2017          
ISSN (Online) : 2277-5420        
www.IJCSN.org 
Impact Factor: 1.5 
 
 
680 
 
Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved. 
 
 3.3 Image convulsion 
 
In order to facilitate the detection of the regions, a 
convulsion process is performed in the binarized image. 
This convulsion will serve to soften the image and reduce 
the number of connected components, in order to 
minimize false positives. The following filter will be 
used: 
 
msk = [0 0 0 0 0; 
0 1 1 1 0; 
 
0 1 1 1 0; 
 
0 1 1 1 0; 
 
0 0 0 0 0;]; 
 
    The result is as follows: 
 
 
 
Fig.10 convulsion the license plate 
 
3.4 Calculation and extraction of regions 
 
The next step is to calculate the existing regions. This will 
allow us to later identify the enrollment since it will be a 
region itself. The process consists of several steps: 
 
1- We label the connected regions. In our case, we 
also indicate that the connected regions must 
have at least 8 connected objects. 
2- The second step is to fill in the connected 
regions. 
3- Finally, the regions are identified. 
 
Below are the images resulting from steps 2 and 3. 
 
 
 
Fig.11  the result for 1 and 2 steps in Calculation and extraction of 
regions 
 
In the previous image, we can see the filling of connected 
regions from the image obtained in the previous steps. 
 
 
 
Fig.12 final step from Calculation and extraction of regions to detect the 
number 
 
From the image with the filled regions each one is 
identified. 
 
3.5 Removal of the license plate 
 
As we saw in the previous image, the region of the license 
plate is the one with the largest area. To obtain the 
registration, the regions are sorted by area in descending 
order. The first region should correspond with the 
registration. 
 
From the coordinates of the region of the position 
registration is calculated and extracted from the image. 
 
 
 
Fig.13 region of license plate is detect 
 
Acknowledgment 
 
The work of H.AlHAMZAWI was supported by the 
Stipendium Hungaricum Scholarship. 
 
4. Conclusion  
 
The method described in this communication has shown a 
relatively high success rate when used in sequences of 
images. The phase of locating license plates handles 
complex scenes quite well, demonstrating a high 
discriminatory power. However, it would be desirable to 
improve the character recognition in the future as well as 
the location of the license plate sides. 
 
Reference  
 
[1] Sunghoon Kim, Daechul Kim, Younbok Ryu, 
Gyeonghwan Kim (2002). “A Robust LicensePlate 
Extraction Method under Complex Image Conditions”, 
ICPR’02, p216-219 vol.3. 
[2] Shyang-Lih C., Li-Shien C., Yun-Chung C., and Sei-
Wan C. (2004) “Automatic License Plate Recognition”. 
IEEE Trans. On Intelligent Transportation Systems, Vol. 
5, No. 1, pp.42-53. 
[3] Transport for London, http://www.tfl.gov.uk 
IJCSN - International Journal of Computer Science and Network, Volume 6, Issue 6, December 2017          
ISSN (Online) : 2277-5420        
www.IJCSN.org 
Impact Factor: 1.5 
 
 
681 
 
Copyright (c) 2017 International Journal of Computer Science and Network. All Rights Reserved. 
 
[4] Borba, B., Vasconcelos, C., Albuquerque, M. et 
al.(2002), “Localization of Brazilian Vehicles Plates 
Using Frequency Analysis”. SIBGRAPI’02, p 408-. 
[5] Martín, F., Borges, D., (2003). “Automatic car plate 
recognition using partial segmentation algorithm”, 
SPPRA 2003, 404-045. 
 
 
 
 
 


Automatics vehicle license plate recognition using MATLAB

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Automatics vehicle license plate recognition using MATLAB

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