The review on automatic license plate recognition (alpr)

IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
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Volume: 02 Issue: 02 | Feb-2013, Available @ http://www.ijret.org 178
THE REVIEW ON AUTOMATIC LICENSE PLATE RECOGNITION
Rinku solanki1
, Naitik kapadia2
, Viral patel3
1
Post graduate student, M.Tech (digital communication), N.R.I. College, Bhopal, India
2
Post graduate student, M.Tech (digital communication), R.K.D.F. College, Bhopal, India
3
lecturer, Elect & comm., Parul polytechnic institute, Gujarat, India,
rinkusolanki86@gmail.com, naitikkapadia@gmail.com, viralrare@gmail.com
Abstract
Nowadays vehicles play a very big role in transportation. Also the use of vehicles has been increasing because of population growth
and human needs in recent years. Therefore, control of vehicles is becoming a big problem and much more difficult to solve . The
presence of noise, blurring in the image, uneven illumination, dim light and foggy conditions make the task even more difficult.
Nowadays, intelligent transportation systems (ITSs) have a significant impact on people’s lives. ITSs include intelligent infrastructure
systems and intelligent vehicle systems. In the current information technology era, the use of automations and intelligent systems is
becoming more and more widespread. Automatic license plate recognition (ALPR) has turned out to be an important research issue.
ALPR has many applications in traffic monitoring system, including controlling the traffic volume, ticketing vehicles without the
human control, vehicle tracking, policing, security, and so on. In this paper categorize different ALPR techniques according to the
features they used for each stage, and compare them in terms of pros, cons, recognition accuracy and processing speed.
Index Terms: Automatic license plate recognition (ALPR) system, literature review, reference
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1. INTRODUCTION
The purpose of this paper is to provide researchers a
systematic survey of existing ALPR research by categorizing
existing methods according to the features they used, by
analyzing the pros/cons of these features, and by comparing
them in terms of recognition performance and processing
speed, and to open some issues for the future research.
Basic block diagram of the ALPR system is shown in fig 2.for
above steps different techniques used by different author
which are studied in literature review. An example of the
number plate extraction is given by figure (1).by this figure
block diagram is easily understand, in this figure all steps
of block diagram is shown by indicating number A,B,C,D,E.
figure(1): an example of extraction of number plate
Automatic license plate recognition (ALPR) applies image
processing and character recognition technology to identify
vehicles by automatically reading their number plates. and this
system mainly divide in three steps:
figure(2):basic block diagram of alpr system
Step(1) acquiring an image, in image acquisition explained
that from where images are acquire Image can be input to the
system by different methods by analog camera, or by digital
cameras, but nowadays digital technology has their advantages
so better input method is by digital cameras or by direct
digital photos.

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figure(a): captured image
Step(2) license plate extraction by whole capturing image we
having license plate covered by background of vehicle body,so
by this step only plate are is extracted from whole body. our
task now is to identify the region containing the license plate.
In this experiment, two features are defined and extracted in
order to decide if a candidate region contains a license plate or
not , these features are
1. Aspect ratio: The aspect ratio is defined as the ratio of the
width to the height of the region.
Aspect Ratio = width/height
Since the minimum enclosing rectangle(MER) of the object
region can be computed via rotating the region in previous
section, the dimension of the objects MER can be taken as the
width and the height of the region
2. Edge Density:
Applying the above feature to filter the segmented regions, a
lot of no license plate regions can be removed. However, there
are still many candidate regions left which take similar
rectangularity and aspect ratio features as the license plate
regions do, such as often the head lights. Considering that the
license plate regions generally take higher local variance in its
pixels‟ values due to the presence of characters, an important
feature to describe license plate region is local variance, which
is quantized using the edge density. so extracted plate region
example shown in figure(b).
figure(b): license plate extraction
Step(3) character isolation: by this step characters on license
plate are segmented and identify. This step is the most
important step in license plate recognition because all further
steps rely on it. This is the second major part of the License
Plate detection algorithm. There are many factors that cause
the character segmentation task difficult, such as image noise,
plate frame, rivet, space mark, plate rotation and illumination
variance. We here propose the algorithm that is quite robust
and gives significantly good results on images having the
above mentioned problems. for the segmentation pre-
processing is required by conversion to gray scale and
binarization. Different algorithms are used for segmentation
which are explained further later in literature review.
segmented license plate example is given in figure(c).
figure(c): character isolation
Step(4) character identification: by this step final result is
founded. Consider figure(d) as an final extracted license
plates
figure(d): character identification
The variations of the plate types or environments cause
challenges in the detection and recognition of license plates.
They are summarized as follows:
1) Location: Plates exist in different locations of an image.
2) Quantity: An image may contain no or many plates.
3) Size: Plates may have different sizes due to the camera
distance and the zoom factor.
4) Colour: Plates may have various characters and background
colours due to different plate types or capturing devices.
5) Font: Plates of different nations may be written in different
fonts and language.

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6) Occlusion: Plates may be obscured by dirt.
7) Inclination: Plates may be tilted.
8) Other: In addition to characters, a plate may contain frames
and screws.
Environment variations:
1) Illumination: Input images may have different types of
illumination, mainly due to environmental lighting and vehicle
headlights.
2) Background: The image background may contain patterns
similar to plates, such as numbers stamped on a vehicle,
bumper with vertical patterns, and textured floors.
Figure(e): Example images in the different condition:(a)
Complex scenes. (b) Various environments. (c) Different
illuminations. (d). Damaged license plates.
2. LITERATURE REVIEW
In the literature, many license plate detection algorithms have
been proposed. Although license plate detection has been
studied for many years, it is still a challenging task to detect
license plates from different angles, partial occlusion, or
multiple instances. License plate detection investigates an
input image to identify some local patches containing license
plates. Since a plate can exist anywhere in an image with
various sizes, it is infeasible to check every pixel to locate it.
Generally, it is preferable to extract some features from
images and focus only on those pixels characterized by the
license plate. Based on the involved features, traditional
license plate detection methods can be classified into three
categories: colour-based, edge-based, and texture-based. In
what follows, we will review the related work in each category
Colour-based approaches are based on the observation that
some countries have specific colours in their license plates. It
is intuitive to extract license plates by locating their colours in
the images the collocation of license plate colour and character
colour is used to generate an edge image. Then, it checks
neighbours of pixels with a value within the license plate
colour range to find candidate license plate regions Edge-
based approaches are the most popular, with reliable
performance in license plate detection. Generally, as a prior,
license plate is characterized by a rectangular shape with a
specific aspect ratio, and can be extracted by checking all
possible rectangles in the image kinds of traditional locating
methods, some other approaches based on local features have
been proposed recently. A brief description of some of
previous works is demonstrated in section of literature
introduction.
In [1] ,license plate recognition methods are:(1)Image
Acquisition: By digital camera (2) License Plate Extraction:
*vertical edge detection by sobel algorithm *filtering by seed
filling algorithm *vertical edge matching (3) Segmentation:
(4) Character Recognition: * Normalization * Template
matching using hamming distance approach. and by this
paper referenced getting the result like: License Plate
Extraction: 587/610, 96.22% License Plate Segmentation:
574/610, 94.04% License Plate Recognition :581/610,
95.24%,and over all system efficiency: 95%.this approach
having some problem in extracting the plate, diplomatic cars
and military vehicles, are not addressed since they are rarely
seen. Detection only for white, black, red, and green colour
plate or numbers.
By[2], (1)Extraction of plate region: edge detection algorithms
and smearing algorithms (2)segmentation of Characters:
smearing algorithms, filtering and some morphological
algorithms (3)recognition of plate characters : template
matching. Final output it is proved to be %97.6 for the
extraction of plate region, %96 for the segmentation of the
characters and %98.8 for the recognition unit accurate, giving
the overall system performance %92.57 recognition rate .it
having some limitation like it recognition of car license plate
only, and This system is designed for the identification of
Turkish license plates.
In[3], recognition steps are as follow:(1)Image Enhancement:
by histogram equalization method (2)Structuring Elements :

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by thickening, (3) Hat transformations: which is use for
contrast, enhancement(top has & bottom has)setting (4)
Morphological Operations like dilation and erosion (5) Plate
region confirmation (6) Character Segmentation and
Recognition by neuron implementation model .by this
reference 250 colour images were used for testing the
technique, These results report a high accuracy rate of above
95%.Although the technique is quite efficient enough to work
very well in the real time environment but currently the
technique proposed lays more emphasis on the accuracy of the
overall system, while the some more work is to be done to
make the technique more efficient.
In[4],recognition by(1) Target recognition: by using feature-
salience theory, features of license plates( include shape,
symmetry , height-to-width ratio , colour, texture, and spatial
frequency, Character features include lines, blobs , aspect ratio
of characters, distribution of intervals between characters ,and
alignment of characters) (2license plate locating by Hough
transform (HT). (3) recognizing license characters by different
steps like binarization, noise removal, and orientation
adjustment, Optical Character Recognition. in this paper , the
success rate for the identification with the set of 1144 license
plates is 95.7%. Combining this rate with the location success
(97.3%), the overall rate of success for our LPR algorithm is
93.1%.As pointed out in the preceding sections, although this
system is intended for the recognition of Chinese license
plates only
By[5], mainly focused on Edge Detection(Sobel Edge
Detection)technique and then filtering of noise by Median
Filter, Smoothing, Connector, Masking, ,and then Colour
Conversation is done. we can see that the detection is not that
clear and proper, which we find , is due to improper light
segment or varying illumination effects. and all over system
result is not mention in this paper.
In the reference[6], The proposed algorithm consists of three
major parts: Extraction of plate region, segmentation of
characters and recognition of plate characters. For
(1)extracting the Plate region, edge detection algorithm and
vertical projection method are used.(2) in segmentation part
filtering, thinning and vertical and horizontal projection are
used. And finally, (3)chain code concept with different
parameter is used for recognition of the characters. The
performance of the proposed algorithm has been tested on real
images. Total Vehicles Images 150(tested under sunny,
cloudy, daytime, nigh time, rainy days...etc atmosphere),
Extracted license plates147 Unsuccessful Extraction 3 .and
final system Efficiency : 98%. The proposed method is mainly
designed for real-time Malaysian license plate, and can be
readily extended to cope with license plates of other countries,
especially those using Latin characters.
By[7],involve three approaches: (1)in plate localization Noise
alleviation, Changing color space, Intensity dynamic range
modification, Edge detection ,Separating objects from
background, Finding connected component ,Candidate
selection, all above process are used (2) in segmentation part
multistage model are used.(Improvement, Rotation,
Binarization, Segmentation, Preparation,) (3) for the
recognition artificial l 1Feed forward neural network is used.
The method achieved accuracy over 91% for localizing plates.
The recognition system implemented by neural networks after
segmentation of characters in image plate identify alphabets
and numbers separately and achieve an accuracy over 97%
and 94% respectively for each. Advantage of this approach is
The image database includes images of various vehicles with
different background and slop under varying illumination
condition and the disadvantage is detection only for English
and Parisian number plate.
In[8] For the Number plate recognition first image conversion
in binary and apply to neural network, and apply mpl
algorithm, then detection individual symbol, by matrix
mapping, and Training by this approach obtained 96.53%
average recognition rate using double hidden layer and 94%
using single hidden layer. The captured image 2-3 meters
taken away from the cameras.
By[9] (1)Pre-processing of Image by histogram
equalization(2)Extraction of plate region by edge detection
algorithm( canny operator) and Plate Area Detection by
various morphological operations (3)Segmentation of
characters by *connected component *bounding box method ,
*Median filter, all above methods. and observed final result as
Extraction :71/78 which gives 91.02% efficiency
,Segmentation 69/78 which gives 88.46%efficiency. overall
accuracy of our system is 89.74%.proposed method is
sensitive to the angle of view, physical appearance and
environment conditions.
By[10] Given All 3 process by 2D Haar after the discrete
wavelet Transform technique : (1)locate and extract the
license-plate (2)train of the license-plate (3)real time scan
recognize of the license-plate .by this paper result shown are
as given, Vehicle recognition number:100,Recognition
number of successful:93 ,Recognition number failed :7
,Recognition rates (%) :93.0%.advantage of this approach is
Haar Discrete Wavelet Transform are that it each time
transform only needs 1/4 of the original image. Hence, this
method can fast execution speed. and the Disadvantage is that
in this paper only specified cameras used like Using the
CASIO EXILIM, 10.1 MEGA PIXELS DIGITAL CAMERA
EX-S10, adjusting the resolution 480 x 640 for photography
vehicle license plates.
In[11] detection steps are :(1)Image acquisition by capturing
an image of a vehicle from video (2)License plate detection
extraction, by Spectral Analysis Approach and Connected
Component Analysis (3)extract the region of license plate
process use spectral analysis (4)Character segmentation use

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Connected component analysis approach and SVM feature
extraction techniques. the advantage of this approach is
success full recognition of a moving vehicle.
By[12] (1)PVW approach is used in this orientation, ratio of
scale to character height, and relative position in the character
region are done by clustering (2)visual word matching by
comparing the extracted SIFT features and histogram
approach is used (3) for license plate location A bounding box
will be estimated to encompass license plate by determining
the upper, lower, left, and right bounding lines sequentially.
This technique achieves a 93.2% “true” detection rate. This
approach can also be extended to the detection of logos and
trademarks .The weakness of this approach is that it may fail
when the license plate resolution is too low, or when the
distortion from the observation angle is too severe.
Approach explained in[13] that, (1) detect a license plate
region by vertical or a horizontal edge based method (2) pre-
processing: is also needed in this approach .so first converted
in to binary image then eliminate noise using morphological
operation (3) character segmentation by thresholding method
(4) feature extraction and character recognition by Euler
number formation .Advantage of this approach that skew ness
is not present in the detected vehicle number plate compare to
other methods and Disadvantage is it limits the efficiency of
the total system.
By[14] (1)plate extraction by Mathematical Morphology
approach and then ,Dilation and Erosion is apply to
image,(2)segmentation by Structuring Elements approach,
Meredian Filter technique and Edge Detection Methods are
use,(3)character extraction by Preprocessing, Text/non-text
classification approach .result derived by this paper is as
given, Real Time Data:100,Images correctly
Detected:93,Results Accuracy 93%.and it says that Very much
damaged plate cannot be recognize.
YEAR/MONTH % EFFICIENCY
2003 95
2005NOV 92.57
2008JUNE 95
2009SEPT 93.1
2010 98
2011JULY 94
2011JULY 95
2011DEC 89.74
2012APRL 93
2012EPT 93.2
2012OCT 93
Figure(3): table of %efficiency/month or year
figure(4): over all system % efficiency/year
As per literature study all over system efficiency per
month/year as given by chart figure(4) which is vary
according to methods for different steps for reconditioning of
license plate.by the given plot we can observe percentage
efficiency of system as per given data.As per given data in
studied references making of table which shows %efficiency
per month/year ,so consider the table for plot.
CONCLUSIONS
In general, an ALPR system consists of four processing stages.
In the image acquisition stage, some points have to be
considered when choosing the ALPR system camera, such as
the camera resolution and the shutter speed. In the license
plate extraction stage, the license plate is extracted based on
some features such as the colour, the boundary, or the
existence of the characters. In the license plate segmentation
stage, the characters are extracted by projecting their colour
information, by labelling them, or by matching their positions
with template. Finally, the characters are recognized in the
character recognition stage by template matching, or by
classifiers such as neural networks and fuzzy classifiers.
Automatic license plate recognition is quite challenging due to
the different license plate formats and the varying
environmental conditions. There are numerous ALPR
techniques have been proposed in recent years.
ACKNOWLEDGEMENTS
We would like to thank Mr. N.D. Kapadia who is lecturer in
our college they give us proper and perfect guidance in this
literature paper .another person who give me a direction is Mr.
Rajesh nema sir ,He is H.O.D. of our collge.by the strict way
they take work regularly from us and that’s why I lots of
thankful to them.we can say that because of strict nature of
nema sir we can complete our work properly.

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REFERENCES:
[1] Muhammad Sarfraz, Mohammed Jameel Ahmed, and
Syed A. Ghazi ,“Saudi arebian licence plate recognition
system”, International Conference on Geometric Modeling
and Graphics (GMAG’03), 2003.
[2] Serkan Ozbay, and Ergun Ercelebi,” Automatic Vehicle
Identification by Plate Recognition”, Processing of world
academy of science engineering and technology vol9, ISSN
1307-6884, november 2005.
[3] Humayun Karim Sulehria, Ye Zhang, Danish Irfan, Atif
Karim Sulehria,” Vehicle Number Plate Recognition Using
Mathematical Morphology and Neural Networks”, WSEAS
TRANSACTIONS on COMPUTERS, Volume 7,ISSN: 1109-
2750, Issue 6, June 2008.
[4] Zhen-Xue Chen, Cheng-Yun Liu, Fa-Liang Chang, and
Guo-You Wang,” Automatic License-Plate Location and
Recognition Based on Feature Salience”, IEEE Transaction on
vehicle technology, VOL. 58, NO. 7, september 2009.
[5] Dr. P.K.Suri, Dr. Ekta Walia, Er. Amit Verma,” Vehicle
Number Plate Detection using Sobel Edge Detection
Technique”, International Journal of Computer Science and
Technology, ISSN : 2229 – 4333, IJCST Vol. 1, Issue 2,
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[6] Kumar Parasuraman, Member, IEEE and P.Vasantha
Kumar, “ An Efficient Method for Indian Vehicle License
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International Conference on Computational Intelligence and
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[7] Muhammad H Dashtban, Zahra Dashtban, Hassan Bevrani,
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[13]P.Sandhya Rani1, Vara Prasad,“License plate character
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[14] R.Radha1 and C.P.Sumathi2, “A Novel approach to
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August 2012 .
BIOGRAPHIES:
Rinku Solanki
Post graduate student
M.tech (Digital communication)
NRI, Bhopal, India
Naitik kapadia
Post graduate student
M.tech (Digital communication),
RKDF , Bhopal, India

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