Detection And Recognition Of Car License Plate Using ... of this, various recognition techniques have been developed and number plate recognition ... The main disadvantages of these

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www.ijifr.com Copyright © IJIFR 2015

Research Paper

International Journal of Informative & Futuristic Research ISSN (Online): 2347-1697

Volume 2 Issue 8 April 2015

Abstract

Car license plate detection and recognition is an important part of vehicle

monitoring system in the present scenario of increasing accidents and crimes. This

paper suggests a novel technique for license plate detection and recognition. The

car license plate is captured with a web camera and it is converted to gray scale

image. After adaptive thresholding process, an unwanted line present on the

image is eliminated and vertical edge is extracted using Vertical Edge Detection

Algorithm. Then the car license plate is extracted and compared with already

stored ones in the database which produces the recognized output. The main

advantage of this technique is that it works even better with low quality images

produced by a web camera. Future forecasts are given at the end.

Detection And Recognition Of Car

License Plate Using Vertical Edge

Detection Algorithm Paper ID IJIFR/ V2/ E8/ 082 Page No. 2761-2769 Research Area

Computer Sci.

& Engineering

Key Words Car License Plate Detection And Recognition (CLPDR), Adaptive

Thresholding, Vertical Edge Detection Algorithm (VEDA)

Karthika J S 1

M.Tech. Scholar

Department of Computer Engineering

Musaliar College of Engineering and Technology, Pathanamthitta-Kerala

Jan Mary Thomas 2

Assistant Professor


Musaliar College of Engineering and Technology,

Pathanamthitta-Kerala

Anju Mariyam Zacharia 3

M.Tech. Scholar


Musaliar College of Engineering and Technology,

Pathanamthitta-Kerala

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ISSN (Online): 2347-1697 International Journal of Informative & Futuristic Research (IJIFR)

Volume - 2, Issue - 8, April 2015 20th Edition, Page No: 2761- 2769

Karthika J S, Jan Mary Thomas2 , Anju Mariyam Zacharia :: Detection And Recognition Of Car License Plate Using Vertical Edge Detection Algorithm

1. Introduction In the present scenario of increasing number of accidents and crimes, car license plate detection and

recognition has numerous applications. Massive integration of information technologies into all

aspects of modern life caused demand for processing vehicles as conceptual resources in information

systems. Because a standalone information system without any data has no sense, there was also a

need to transform information about vehicles between the reality and information systems. This can

be achieved by a human agent, or by special intelligent equipment which is be able to recognize

vehicles by their number plates in a real environment and reflect it into conceptual resources.

Because of this, various recognition techniques have been developed and number plate recognition

systems are today used in various traffic and security applications, such as parking, access and

border control, or tracking of stolen cars.

In parking, number plates are used to calculate duration of the parking. When a vehicle

enters an input gate, number plate is automatically recognized and stored in database. When a

vehicle later exits the parking area through an output gate, number plate is recognized again and

paired with the first-one stored in the database. The difference in time is used to calculate the

parking fee. Car License Plate Detection and Recognition Systems can be used in access control. For

example, this technology is used in many companies to grant access only to vehicles of authorized

personnel. In some countries, CLPDR systems installed on country borders automatically detect and

monitor border crossings. Each vehicle can be registered in a central database and compared to a

black list of stolen vehicles. In traffic control, vehicles can be directed to different lanes for a better

congestion control in busy urban communications during the rush hours.

The factors affecting the accuracy of the system are size of the license plate, orientation of

image taken, and environmental conditions. The proposed system uses web cameras with low

resolution of 352x288 to capture the images. This reduces the implementation cost to a certain extent

and increases the overall system performance.Up and down edge removal and detection is usually an

important step in the CLPDRS because it has effects on the system‟s reliability and computation

time. For this reason, a brand new vertical edge detection algorithm (VEDA) will be offered right

here to cut back the computation time with the entire CLPD method.

2. Existing System

In the existing system only license plate detection is done, recognition is not performed. Many

algorithms have been used for detection of car license plates. Some of them are Sobel algorithm,

Canny algorithm, Zimmermann and Matas algorithm.

The main disadvantages of these algorithms are:

High complexity.

High processing time.

Works under certain conditions such as fixed backgrounds and known color.

High resolution cameras are needed.

High cost.

3. Proposed System

The proposed system performs both car license plate detection and recognition. It uses a web camera

of low resolution to capture the car license plate and thus reduces the cost of implementation. It has

low complexity and detection rate is high compared to the existing systems.

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The captured image is converted to gray image and some thresholding process is done to

enhance the quality of image. The unwanted line occurred due to thresholding process is eliminated

and vertical edge is extracted. Then the desired details are highlighted and the candidate region is

extracted and plate region is selected and detected. Using this detected plate, license plate

recognition is done by comparing this with already stored ones in the database.

Figure 3.1 shows the flowchart of the proposed system.

Figure 3.1 Flowchart of the proposed system

3.1 Image Acquisition

The image of car license plate is captured using web cameras. Usually web cameras with have low

resolution. In the proposed system web cameras with resolution 352x288 is used. The main

advantages of using low resolution images are less memory size, less cost and low computing time.

Image Acquisition

Gray image conversion

Adaptive thresholding

Unwanted Line Elimination Algorithm

Vertical Edge Detection Algorithm

Highlight Desired Details

Candidate Region Extraction

Plate Detection Plate Region Selection

Car License Plate Recognition

Display Output

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3.2 Gray Image Conversion

For better storage and processing, color image is converted to gray image by using in-built

MATLAB functions.

3.3 Adaptive Thresholding

This process is done to enhance the image quality. It is a simple extension of Wellner‟s method. To

begin with, the actual summation of the pixel values for every column jth by way of many strip

values we are computed applying in which g(x, y) represents the actual feedback values, along with

sum(i)|jth represents many cumulative gray values of g(x, y) for the column jth by way of many

rows of image I = 0, 1,... top.

sum(i) = ∑ (1)

And then, the integral image are able to end up being computed for every pixel such as (2):

IntgrlImg(i, j) = {

(2)

in which IntgrlImg(i, j) represents the actual essential image intended for pixel (i, j).

The next phase is to do thresholding per pixel. To do so, very first, the actual strength summation

per neighborhood screen need to be computed by utilizing a couple of subtraction businesses and

another add-on operation as follows:

= (IngrlImg (i+

, j+

)) – (IngrlImg (i+

, j -

)) – (IngrlImg (i-

, j +

)) +

(IngrlImg (i-

, j -

)) (3)

in which represents the actual summation of the intensities of the gray values for just a

chosen neighborhood screen, that currently binarized pixel will be centering within and s= image

width/8.

As a result, for you to work out the actual adaptive threshold worth for the image, during which g(i,

j) ∈ [0, 255] is the strength of the pixel based at (i, j), threshold t(i, j) per pixel should be computed

very first as follows:

t (i , j) = (1-T) x (4)

in which t (i, j) represents the actual threshold per pixel at location (i, j), along with T is a constant,

T = 0. 15. This value is the optimal worth intended for greatest thresholding overall performance for

the graphics following testing in many graphics and is particularly assessed successfully.

The actual criterion using will be employed in each pixel for you to result the actual threshold worth

of these pixels :

p (i, j) = {

(5)

where p (i, j) represents adaptive threshold output value of each pixel g(i, j) and represents area of

the local window selected.

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3.4 Unwanted Line Elimination Algorithm

Thresholding course of action in general yields a lot of slim lines that not necessarily participate in

the particular LP location. The numerous lengthy foreground collections in addition to small

randomly sounds are generally unwanted collections. These kinds of collections may get in the way

inside LP area As a result, we've suggested a great algorithm to remove them from the particular

image. This may very well be like a morphological operation in addition to development course of

action. You can find 4 circumstances where unwanted collections could be made. Within the initial

event, the particular brand is horizontally through a position comparable to 0◦ as (−). Within the 2nd

event, the particular brand is vertical through a position comparable to 90◦ as (|). Within the third

event, the particular brand is willing through a position comparable to 45◦ as (/). Within the next

event, the particular brand is willing through a position comparable to 135◦ as (\). As a result, the

particular ULEA have been suggested to remove most of these collections. In this move, even

though processing a binary image, the black pixels are classified as the background, plus the white-

colored pixel prices are classified as the foreground. Some sort of 3 × 3 mask is used all through

almost all pixels. Merely black pixel prices inside threshold photograph are generally analyzed. In

order to support the little details of the particular LP, solely the particular collections in whose

widths comparable to 1 pixel are generally checked out. Suppose that b(x, y) are classified as the

prices regarding thresholded photograph. Once, the existing pixel benefit located with the disguise

center is black, the particular eight-neighbour pixel prices are generally analyzed. When a couple of

equivalent prices are generally white-colored jointly, then the current pixel is converted to a white-

colored benefit like a foreground pixel benefit.

3.5 Vertical Edge Detection Algorithm

The main advantage of the particular VEDA is always to separate the particular plate- aspect place,

particularly the start as well as the end of every figure i.e,edges . As a result, the particular denture

specifics will likely be effortlessly detected, as well as the figure reputation practice will likely be

performed quicker. Following thresholding and also ULEA operations, the particular image will

simply get non colored documents places, as well as the VEDA is actually digesting most of these

places. The thought of the particular VEDA specializes in intersections connected with black–white

regions and also white–black regions. For this process a 2 x 4 mask is proposed. This mask begins

going throughout and also via eventually left to proper. Should the four pixels in places (0, 1), (0, 2),

(1, 1), and also (1, 2) are black color, then the various other face mask prices are examined in case

whether or not they are black color or even not necessarily. Should the total prices are black color,

then a couple of places in (0, 1) and also (1, 1) will likely be changed into whitened. Otherwise, in

case line 1 and also any line get various prices, the particular pixel worth connected with line 1 will

likely then be studied. This technique is actually repeated with all the total pixels inside image.

3.6 Highlight Desired Details

Soon after applying VEDA, the next thing is to highlight the required particulars and also top to

bottom sides within the impression. This HDD works NAND–AND operation for each two matching

pixel prices removed from equally ULEA and also VEDA end result images. This method is

determined by VEDA end result throughout displaying your menu region. All the pixels within the

top to bottom side impression will likely be scanned. Any time you can find two neighbour black

pixels and also then just one black pixel, such as VEDA end result style, the 2 sides will likely be

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examined for you to highlight the required particulars simply by drawing black horizontal lines

joining each and every two top to bottom sides. Primary, those two top to bottom sides should be

enclosed by a black track record, such as your ULEA impression throughout . Second, the

significance regarding horizontal distance hd presents the length between two top to bottom sides of

any one target. This hd has become calculated while using test images. This hd importance can be

picked to get suitable for doing away with very long foreground and also hit-or-miss noises sides

who have not really recently been eradicated previously. This specific encoding process begins

transferring from still left for you to suitable and also completely. Of course pixels are generally

scanned, your regions that accurate LP is out there are generally highlighted.

3.7 Extraction of Candidate Region

This process selects the license plate region. For that, it first counts the horizontal lines drawn per

each row and stores it in a matrix variable. The number of rows is huge so it will delay the

processing time. To avoid that rows are gathered to groups.

Total number of groups = total number of image rows / C (6)

C is a constant called Candidate Region Extraction Constant and its value is usually set as 10.

The vast majority of groups aren't elements of the particular denture specifics. Therefore, it really is

practical try using a limit to reduce those unhappy groups and also to maintain satisfied communities

that the LP specifics exist inside. Each and every class will probably be examined; when it offers a

minimum of 15 lines, subsequently it really is regarded as an element of the particular LP location.

Hence, the overall quantity of communities like the elements of LP areas will probably be measured

in addition to kept. The rest of the communities right after thresholding move need the particular LP

specifics. Therefore, their particular areas are kept. The last move here's to be able to remove both

equally higher in addition to reduced limits of every satisfied class by making use of its very own

list. Then horizontal boundaries are drawn above and below each candidate region.

3.8 Selection of Plate Region

In order to select the correct and exact license plate this step is done. Some of the processed graphics

are generally blurry, or maybe this denture spot could possibly be defected. The actual denture spot

may be looked at pixel by pixel, whether or not that is one of the LP spot or maybe not. A new

mathematical formulation will be planned for this purpose, whenever that method will be applied on

just about every pixel, this possibility on the pixel staying some this LP may be determined. Seeing

that aforesaid, for the applicant parts, just about every line is going to be looked at one after the

other. When the line blackness proportion surpasses 50%, then a latest line is one of the LP spot;

thus, that line is going to be swapped out with a vertical dark range inside consequence impression.

Hence, just about every line will be looked at because of the issue in which, when blckPix ≥ 0. 5 ×

colmnHght, then a latest line will be some this LP spot. Here, this blckPix symbolizes the whole

number of dark pixels each just about every line in today's applicant spot, along with the colmnHght

symbolizes this line elevation on the on the applicant spot. Cures using a set price (0. 5) will be used

with nonblurry graphics. On the other hand, many pixels on the applicant parts won't be recognized

in the event that this proportion associated with blackness to the total length (height) on the applicant

spot will be increased than50%. For that reason, the trouble will be modified to get under 50%, good

proportion on the blurry stage or maybe this deformation on the LP. The illness is going to be

revised as follows. blckPix ≥ price will be lowered if the blurry stage will be excessive to help

spotlight far more essential details, and it's also increased if the blurry stage will be much less. For

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that reason, this mathematical portrayal intended for picking out this LP spot may be created as

follows:

= {

(7)

where represents the output value of pixel which is currently processing.

The actual articles in whose leading as well as bottom level neighbors have got excessive quotients

associated with blackness details receive 1 vote. This procedure is finished for many applicant parts.

Hence, this applicant spot which includes the greatest vote beliefs stands out as the decided on spot

because the legitimate LP. Thus the car license plate is detected.

3.9 Recognition of Car License Plate

The detected number plate is then passed to recognition phase where currently detected plate is

compared with those plates that are already stored in the database. The database consists of set of

features of all trained number plates. The features extracted from the detected number plate using

MATLAB functions are compared with that already stored. The plates that have maximum similar

features are identical ones. The position of matched number plate is displayed, otherwise a message

box with the message „stranger‟ is displayed.

4. Results

a) input image

b) adaptive thresholding output

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c) VEDA output

d) HDD output

e) Selection of LP Region f) Detection of license plate

g) Recognition of license plate

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5. Conclusion And Future Scope

The proposed system suggests a fastest and easiest way of detecting and recognizing the car license

plates. It is very cost effective and has high detection rates. As a future enhancement a good

algorithm can be used for license plate recognition.

References

[1] S. N. Huda, K. Marzuki, Y. Rubiyah, and O. Khairuddin, “comparison of feature extractors in license plate

recognition,” in Proc. 1st IEEE AMS, Phuket, Thailand, 2007, pp. 502–506.

[2] S. Thanongsak and C. Kosin, “the recognition of car license plate for automatic parking system,” in Proc.

5th Int. Symp. Signal Process. Appl., Brisbane, QLD, Australia, 1999, pp. 455–457.

[3] H. Bai and C. Liu, “a hybrid license plate extraction method based on edge statistics and morphology,” in

Proc. 17th Int. Conf. Pattern Recognit.,Cambridge, U.K., 2004, pp. 831–834.

[4] M. Fukumi, Y. Takeuchi, H. Fukumoto, Y. Mitsura, and M. Khalid,“neural network based threshold

determination for Malaysia license plate character recognition,” in Proc. 9th Int. Conf. Mechatron. Technol.,

2005, pp. 1–5.

[5] E. R. Lee, K. K. Pyeoung, and J. K. Hang, “automatic recognition of a car license plate using color image

processing,” in Proc. IEEE Int. Conf. Image Process., 1994, pp. 301–305.

[6] R. Parisi, E. D. Di Claudio, G. Lucarelli, and G. Orlandi, “car plate recognition by neural networks and

image processing,” in Proc. IEEE Int.Symp. Circuits Syst., 1998, pp. 195–198.

[7] T. Naito, T. Tsukada, K. Yamada, K. Kozuka, and S. Yamamoto, “robust license-plate recognition method

for passing vehicles under outside environment,” IEEE Trans. Veh. Technol., vol. 49, no. 6, pp. 2309–

2319,Nov. 2000.

Biographies

1st Karthika J S is an M.Tech Computer Science and Engineering student in Musaliar College Of

Engineering And Technology, Pathanamthitta. She received B.Tech degree in Information

Technology from Cochin University of Science And Technology.

2nd Jan Mary Thomas is currently working as an Assistant Professor in department of Computer Science

and Technology at Musaliar College Of Engineering And Technology, Pathanamthitta. She pursed

the B.Tech degree in Computer Science And Engineering from Mahatma Gandhi University and the

M.Tech degree from Annamalai University.

3rd Anju Mariyam Zacharia is currently doing M.Tech in Computer Science and Engineering at Musaliar

College Of Engineering And Technology. She pursed the B.Tech degree in Computer Science And

Engineering from Mahatma Gandhi University.

Documents

Detection And Recognition Of Car License Plate Using ... of this, various recognition techniques have been developed and number plate recognition ... The main disadvantages of these