International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 8 – Oct 2014
Automatic Number Plate Recognition System
Using Improved Segmentation Method
1
2
Bhavin A Patel , Ashish Singhadia
1
M.Tech (DC) VIT BHOPAL
2
Professor ECE Department, VIT Bhopal
Abstract: Automatic range plate recognition could be a real
time embedded system. It identifiers the characters directly
from the image of the vehicle plate and a district of research.
Vehicle range plate recognition has been studied in several
countries. Because of the various kinds of range plate’s
square measure used, AN automatic range plate recognition
system’s needs square measure totally different for every
country. In this paper, variety plate localization and
recognition system for vehicles in Republic of India is
projected. This technique is developed supported digital
images and might be easily applied to park system for the
utilization of documenting access of parking services, secure
usage of parking homes and additionally to stop automotive
thievery problems. The projected algorithmic program is
predicated on a mixture of morphological operation with
space criteria check for range plate localization.
Segmentation of the plate characters was achieved by region
professionals perform in Matlab, labelling and fill hole
approach. The character recognition was accomplished with
the optical characters by the access method of template
matching.
Keywords: vehicle plate recognition, range plate extraction,
Segmentation, thinning, vertical and horizontal scanning,
region props, Optical character recognition
I. Introduction
The invention of the ANPR system was in 1976 at the
Police Scientific Development Branch in UK [1]. There are
various applications of auto plate recognition systems for
any given country. They embrace road electronic toll
assortment, automatic parking attendant e.g. in hotels,
banks airports and fleet vehicle compounds, shopper
identification enabling personalised service e.g. in leisure
centres, gasoline station investigation, regulation group
action and security. The may be an achieved by a
personality’s agent,
or
by special
intelligent
instrumentality. Varied recognition systems or today
utilized in varied traffic and security application systems,
like parking, access and border managements, or chase of
taken cars. In parking variety plates are accustomed
calculate length of the parking [2].
The earlier strategies use either feature based mostly
approached mistreatment edge detection or Hough remodel
or use artificial neural network that need massive tanning
information [3][4][5]. Shidore, Narote developed variety
plate recognition for Indian vehicles. Number plate
extraction had done by using Sobel filter, morphological
operation and connected element technique [6]. Ltufo,
Morgan and Johnson projected automatic variety plate
recognition mistreatment optical character recognition [7].
Choi and Kim projected the strategy supported vertical
edge mistreatment Hough remodel for extracting the
ISSN: 2231-5381
vehicle plate [8] [9]. Hontani et.al. Projected a technique
for extracting characters while not previous information of
their position and size within the image [10].
In this paper, a straightforward vehicle plate extraction
technique is given. The strategy is essentially supported the
morphological algorithmic program, digital image
labelling and region props technique. Once a vehicle enters
associate input gate, variety plates is mechanically
recognized and keep in information. Once a vehicle later
exits the park through associate output gate, selection plate
is recognized once additional and paired with first-one
keep inside the data. The distinction in time is utilized to
calculate the parking fee. Automatic selection plate
recognition systems square measure usually worker in
access management.
The ANPR system’s most necessary portion is package
model. It uses series of image process techniques that
square measure enforced in MATLAB. The ANPR system
is split into following parts:
 Capture Image
 Pre-Processing
 Extraction of Plate Region
 Character Segmentation
 Character Recognition
 Comparison with Database
 Result
The flow chart of the ANPR system implemented in this
work is shown in figure.
Input Image
Pre-Processing
Extraction of Plate Region
Character Segmentation
Character Recognition
Recognized Character Display
Figure 1: Flow diagram of ANPR System
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 8 – Oct 2014
II. Image acquisition
Image acquisition is that the first innovate recognition
number plate recognition method. Images are going to be
no inheritance using AN analogy camera with a scanner or
victimization a photographic equipment. Image acquisition
through analogy camera is impractical. The reliable and
smart approach is accomplishment footage thorough
photographic equipment. Captured image is shown in
figure 2(a).
median filtering methodology. Median filtering is then
enforced for the effective removal of speckle noise, salt
and pepper noise. After that, we've to search out space of
the actual plate. For this region properties have to be
verified. These can live the properties of image regions.
The region consists of too several properties, in that ‘area,
orientation and therefore the bounding box’ square
measure some vital properties. REGIONPROPS doesn't
accept a binary image as its first input. There square
measure 2 common ways in which to convert a binary
image to a label matrix:
L=bwlabel(BW);
L = double (BW);
Figure 2: Input Image
III. Pre-Processing
After the acquisition of image, pre-processing of image is
completed. Once a picture is nonheritable, there could also
be noises present in image. These noises after the
popularity rate greatly. Therefore these noises ought to be
far away from the pictures. The digital image converting
into gray scale image by gray scale image processing
method. The method is based on different colour
transform. in line with the R, G, B values in the image, it
calculates the price of grey values, and acquire the grey
image at same time. Grey scale pictures area unit cropped
in order that removes boundary regions from the captured
image. The cropping conjointly reduces noise in the image.
Therefore we tend to get a smaller image with lesser noise
to work with is obtained by cropping technique. grey scale
image and cropped pictures area unit shown in figure 3(b)
and figure 3(c).
The first methodology of forming a label matrix, L=
bwlabel (BW), end in a label matrix conation 2 contiguous
region tagged by the upper values one and a pair of. The
second methodology of forming a label matrix, L = double
(BW), end in a label matrix containing one discontinuous
region by the number price one. Since every result is
lawfully fascinating in bound things, REGIONPRPS will
not settle for binary pictures and convert them
victimization either methodology. You ought to convert a
binary pictures to a label matrix victimization one in every
of these methodology before line REGIOINPROPS, we are
able to remove the unwanted region by calculating the one
in every of the region property i.e. area. Once finding the 3
properties the segmental image was obtained with its
options. Extracted variety plate while not noise is shown in
figure 4(c).
Figure 4(a): Binary Image
Figure 4(b): Bwfill Image
Figure 3(b): Gray Scale Image Figure 3(c): Number Plate Area
Figure 4(c): Extracted Number Plate from the Binary Image
without Noise
IV. Plate Region Extraction
V. Character Segmentation
After image acquisition and pre-processing it will given to
the segmentation part. Initial the image was reborn to grey
scale image once that thresolding algorithmic program is
applied on this grey scale image to represent it as a black
and white image. Then black & white fill is applied to
the binary pictures. Binary image is shown in figure 4(a).
BWFILL differs from several alternative binary image
operation in this it operation in this it operation on
background pixels, instead of foreground pixels. If the
foreground is 8-connected the background is 4-connected,
and contrariwise, this is called segmentation image is
shown in figure 4(b).The segmental image is filtered by
Character Segmentation is that the procedure of extracting
the characters ad numbers from the vehicle plate image.
Numerous aspects produce the character segmentation
task, difficult, like image noise, plate frame, space mark,
plate’s rotation and lightweight variance. Selection of
procedure is projected for character segmentation to beat
these aspects. Propose the vertical and horizontal scanning
for character segmentation.
Vertical Scanning: Vertical scanning technique is used to
dig out each character from the image found on one st and
last column part. It’s into the image by part vertically from
[0,0] until [width, height] that is dead in columns by
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 8 – Oct 2014
column scanning. as a result of the input image can be a
binary image that comprises one and 0 values, vertical
scanning theme is simple to be dead. The scale between
each first and last column area unit about to be computed.
At last, every character or varieties area unit about to be
slice to separate it from the plate background. Every
component goes to be kept in array individually for next
horizontal scanning method.
Horizontal Scanning: One each component is saved
individually in preceding step, horizontal scanning will
verify the first and last rows of the image. The intention is
to eradicate additional higher and lower region from the
image. To conclude, the tip results of this technique area
unit about to be an image with filled with character or vary
elements with none spare areas. The segmented characters
are shown in Figure 5.
Figure 5: Segmentated Horizontal and Vertical
Scanned Images
VI. Database
Database is assortment information of knowledge or data
that it's being orderly organized; therefore it will be
accessed simply and updated. Database can be in the form
of text, contents and pictures. Info is required to create
certain that the image area will be contained enough
characters that have been extracted and the vehicle license
plated range hold on in the pad for the purpose of
comparison. Database of alphabet and numerical characters
are shown in Figure 6.
Figure 6: Database of Alphabet and Numerical Characters
VII. Recognition
The OCR is currently accustomed compare the every
individual character against the alphanumerical
information. The OCR use correlation coefficients
technique to match individual character and at last the
quantity is known in string format in a very variable. The
string is compared with the keep information for the
vehicle authentication and recognized range plate string is
compare with attested information file, if the each worth is
same means that it'll show the authorize otherwise it'll
show the unauthorized. The output string and
authentication result are shown in figure 7(a) and figure
7(b).
Figure 7(a): Recognized number plate
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 8 – Oct 2014
DSP’s ” IEEE International Conference on Real-Time
Technology and Application Symposium Chicago, USA, pp.
58-59,2005.
Figure 7(b): Result
4.
S.H. Park, K.I. Kim, K. Jung and H.J. Kim, “Locating car
license plate using Neural Network,” Electronics Letters, Vol.
35, No.17 ,pp. 1474-1477,1999.
5.
K.K. KIM, K.I., KIM, J.B KIM, and H.J. KIM, “LearningBased Approach for License Plate Recognition” Proceeding of
IEEE Signal Processing Society Workshop, Vol. 2, pp 614623, 2000.
6.
Shidore, Narote, “Number Plate Recognition for Indian
Vehicles”, IJCSNS International Journal of Computer Science
and Network Security, 11, No.2, and pp. 143-146, 2011.
7.
R.A Lotufo, A.D. Morgan, and AS. Johnson,1990, “Automatic
Number-Plate Recognition”, Processing of the IEEE
Colloquium on Image analysis for Transport Application, Vol.
1.035, pp.6/1-6/6, February 16, 1990.
8.
H.J. Choi,1987, “A Study on the Extraction and Recognition
of a Car Number Plate by Image Processing”, Journal of the
Korea Institute of Telecommunication and Electronics, Vol.24,
pp. 309-3 15,1987.
9.
H.S. Kim, et al., 1991, “Recognition of a car Number Plate by
a Neural Network”, Processing of the Korea Information
Science Society Fall Conference, Vol. 18, pp. 259-262, 1991.
10.
Hontani, H., AND Koga, T., 2001, “Character extraction
method without prior knowledge on size and information”,
Proceeding of the IEEE International Vehicle Electronics
Conference IVEC’01, PP. 67-72.
VIII. Experimental Setup and Result
Experiments have been performed to test the proposed
algorithm and to measure its accuracy. The system is
simulated in MATLAB for Indian license plates for the
extraction, segmentation and recognition of range plate.
Colour pictures were used for testing the techniques with
size of 2048 x 1536. the pictures were taken of various
colour and variable sized range plates. The gap between
the camera and therefore the vehicle varied from 3 to 7
meter.
TABLE 1. RESULT OF THE TESTES
Units of ANPR
System
Extraction
Segmentation
Recognition
Percentage of
Accuracy
96%
94%
95%
It is shown that accuracy for the extraction of plate region
is 96%, 94%% for segmentation of the character and 95%
looking forward to recognized characters. The system
performance may be outlined because the product of all
units’ accuracy rates. Thus overall accuracy of our system
is ninety fifth.
IX. Conclusion
We have enforced Automatic range plate recognition. Our
rule with success detects the amount plate pictures of
Indians country. The system consists of extraction of
image, character segmentation and recognition. We’ve got
applied the algorithm on many images and located that it
successfully recognition. The automated range plate
system is enforced in Matlab and it performance is tested
on a true pictures. The result shows that robustly detects
and recognizes the vehicle using vehicle plate against
different lighting condition and might be implemented on
the doorway of highly restricted areas.
REFERENCES
1.
Muhammad Tahir Qadri, Muhammad Asif, “Automatic
Number Plate Recognition System for Vehicle Identification
Using Optical Character Recognition” IEEE 2009
2.
Optasia System Pte Ltd, “The World Leader in License Plate
Recognition
Technology”
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from:www.singaporegateway.com/optasia,
Accessed
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November 2008.
3.
V. Kasmat, and S. Ganesan, “An efficient implementation of
the Hough transform for detecting vehicle license plate using
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