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International Conference on Recent Advances in Management, Architecture, Technology and Engineering
(ICRAMATE’16)
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(IJARMATE)
21
st
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21
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ANPR for Developing Countries
K.Kalaiselvi
1
, S.Selvakani
2
, P.Sorimuthu Iyan
3
, Christo Ananth
4
P.G. Scholars, Department of M.E. Communication Systems,
Francis Xavier Engineering College, Tirunelveli
1,2,3
Associate Professor, Department of ECE,
Francis Xavier Engineering College, Tirunelveli
4
Abstract
Automatic Number Plate
Recognition (ANPR) is a real time
embedded system which automatically
recognizes the license number of
vehicles. In this paper, the task of
recognizing number plate for Indian
conditions is considered, where
number plate standards are rarely
followed.
The system consists of integration of
algorithms like: ‘Feature-based
number plate Localization’ for
locating the number plate, ‘Image
Scissoring’ for character segmentation
and statistical feature extraction for
character recognition; which are
specifically designed for Indian
number plates.
The system can recognize single and
double line number plates under
widely varying illumination conditions
with a success rate of about 82%.
Introduction
ANPR is a mass surveillance system
that captures the image of vehicles and
recognizes their license number. Some
applications of an ANPR system are,
automated traffic surveillance and
tracking system, automated high
way/parking toll collection systems,
automation of petrol stations, journey
time monitoring.
Such systems automate the process of
recognizing the license number of
vehicles, making it fast, time efficient
and cost-effective.
1.1 Existing system
ANPR systems have been implemented
in many countries like Australia, Korea
and few others.Strict implementation of
license plate standards in these countries
has helped the early development of
ANPR systems. These systems use
standard features of the license plates
such as: dimensions of plate, border for
the plate, color and font of characters,
etc. help to localize the number plate
easily and identify the license number of
the vehicle.
In India, number plate standards are
rarely followed. Wide variations are
found in terms of font types, script, size,
placement and color of the number
plates. In few cases, other unwanted
decorations are present on the number
plate. Also, unlike other countries, no
special features areavailable on Indian
number plates to ease their recognition
process. Hence, currently only manual
recording systems are used and ANPR
has not been commercially implemented
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st
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in India.
Christo Ananth et al. [1]
proposed a system about Efficient
Sensor Network for Vehicle Security.
Today vehicle theft rate is very high,
greater challenges are coming from
thieves thus tracking/ alarming systems
are being deployed with an increasingly
popularity .As per as security is
concerned today most of the vehicles are
running on the LPG so it is necessary to
monitor any leakage or level of LPG in
order to provide safety to passenger.
Also in this fast running world
everybody is in hurry so it is required to
provide fully automated maintenance
system to make the journey of the
passenger safe, comfortable and
economical. To make the system more
intelligent and advanced it is required to
introduce some important developments
that can help to promote not only the
luxurious but also safety drive to the
owner. The system “Efficient Sensor
Network for Vehicle Security”,
introduces a new trend in automobile
industry. Christo Ananth et al. [2]
discussed about Intelligent Sensor
Network for Vehicle Maintenance
System. Modern automobiles are no
longer mere mechanical devices; they
are pervasively monitored through
various sensor networks & using
integrated circuits and microprocessor
based design and control techniques
while this transformation has driven
major advancements in efficiency and
safety. In the existing system the stress
was given on the safety of the vehicle,
modification in the physical structure of
the vehicle but the proposed system
introduces essential concept in the field
of automobile industry. It is an
interfacing of the advanced technologies
like Embedded Systems and the
Automobile world. This “Intelligent
Sensor Network for Vehicle
Maintenance System” is best suitable for
vehicle security as well as for vehicle’s
maintenance. Further it also supports
advanced feature of GSM module
interfacing. Through this concept in case
of any emergency or accident the system
will automatically sense and records the
different parameters like LPG gas level,
Engine Temperature, present speed and
etc. so that at the time of investigation
this parameters may play important role
to find out the possible reasons of the
accident.
Further, in case of accident & in
case of stealing of vehicle GSM module
will send SMS to the Police, insurance
company as well as to the family
members. Christo Ananth et al. [3]
discussed about an eye blinking sensor.
Nowadays heart attack patients are
increasing day by day."Though it is
tough to save the heart attack patients,
we can increase the statistics of saving
the life of patients & the life of others
whom they are responsible for. The main
design of this project is to track the heart
attack of patients who are suffering from
any attacks during driving and send them
a medical need & thereby to stop the
vehicle to ensure that the persons along
them are safe from accident. Here, an
eye blinking sensor is used to sense the
blinking of the eye. spO2 sensor checks
the pulse rate of the patient. Both are
connected to micro controller.If eye
blinking gets stopped then the signal is
sent to the controller to make an alarm
through the buffer. If spO2 sensor senses
a variation in pulse or low oxygen
content in blood, it may results in heart
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st
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failure and therefore the controller stops
the motor of the vehicle. Then Tarang F4
transmitter is used to send the vehicle
number & the mobile number of the
patient to a nearest medical station
within 25 km for medical aid. The pulse
rate monitored via LCD .The Tarang F4
receiver receives the signal and passes
through controller and the number gets
displayed in the LCD screen and an
alarm is produced through a buzzer as
soon the signal is received. Christo
Ananth et al. [4] discussed about a
system, GSM based AMR has low
infrastructure cost and it reduces man
power. The system is fully automatic,
hence the probability of error is reduced.
The data is highly secured and it not
only solve the problem of traditional
meter reading system but also provides
additional features such as power
disconnection, reconnection and the
concept of power management. The
database stores the current month and
also all the previous month data for the
future use. Hence the system saves a lot
amount of time and energy. Due to the
power fluctuations, there might be a
damage in the home appliances.
Hence to avoid such damages
and to protect the appliances, the voltage
controlling method can be implemented.
Christo Ananth et al. [5] discussed about
a system, in this project an automatic
meter reading system is designed using
GSM Technology. The embedded micro
controller is interfaced with the GSM
Module. This setup is fitted in home.
The energy meter is attached to the
micro controller. This controller reads
the data from the meter output and
transfers that data to GSM Module
through the serial port. The embedded
micro controller has the knowledge of
sending message to the system through
the GSM module. Another system is
placed in EB office, which is the
authority office. When they send “unit
request” to the microcontroller which is
placed in home. Then the unit value is
sent to the EB office PC through GSM
module. According to the readings, the
authority officer will send the
information about the bill to the
customer. If the customer doesn’t pay
bill on-time, the power supply to the
corresponding home power unit is cut,
by sending the command through to the
microcontroller. Once the payment of
bill is done the power supply is given to
the customer. Power management
concept is introduced, in which during
the restriction mode only limited amount
of power supply can be used by the
customer.
1.2 Proposed system
Fig.1. Software flow of the system
INPUT IMAGE
FROM CAMERA
O/
P ascii
CHARACER
CHARACTER
SEGMENTATION
PREPROCES
SIN
G
NUMBER PLATE
LOCALIZATION
CHARACTER
RECOGNISATION
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st
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In designing this system (fig. 1), various
Image Processing algorithms were
designed in Matlab and implemented on
the Digital Signal Processor
TMS320DM6437 which is optimized for
video and image processing applications.
A rear image of a vehicle is captured
and processed using various algorithms.
Initially, the number plate area is
localized using a novel ‘feature-based
number plate localization’ method which
consists of many algorithms. This
algorithm satisfactorily eliminates all the
background noise and preserves only the
number plate area in the image. This
area is then segmented into individual
characters using ‘Image Scissoring’
algorithm. After this step, the characters
are extracted from the gray-sale image
and each character is enhanced using
some character enhancement techniques.
These characters are given to the
character recognition module, which
uses statistical feature extraction to
recognize the characters.
2. SYSTEM OVERVIEW
Fig.2. Output of step 1
Vehicle detection module detects the
presence of vehicle by using inductive
sensors in which metal wire loop is
placed beneath the road. When a vehicle
crosses the loop, there is change in
induced current which detects presence
of vehicle. As a result the DSP is
interrupted and it triggers the IR camera
to capture the image (fig. 2). The
captured image is processed by DSP to
recognize license number of vehicle by
employing various image processing
algorithms, as mentioned earlier. The
DSP gives the license number in ASCII
format, using which all relevant details
about the vehicle are obtained from a
centralized database.
Christo Ananth et al. [6]
proposed a method in which the
minimization is per-formed in a
sequential manner by the fusion move
algorithm that uses the QPBO min-cut
algorithm. Multi-shape GCs are proven
to be more beneficial than single-shape
GCs. Hence, the segmentation methods
are validated by calculating statistical
measures. The false positive (FP) is
reduced and sensitivity and specificity
improved by multiple MTANN. Christo
Ananth et al. [7] proposed a system, this
system has concentrated on finding a fast
and interactive segmentation method for
liver and tumor segmentation. In the pre-
processing stage, Mean shift filter is
applied to CT image process and
statistical thresholding method is applied
for reducing processing area with
improving detections rate. In the Second
stage, the liver region has been
segmented using the algorithm of the
proposed method. Next, the tumor
region has been segmented using
Geodesic Graph cut method. Results
International Conference on Recent Advances in Management, Architecture, Technology and Engineering
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st
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show that the proposed method is less
prone to shortcutting than typical graph
cut methods while being less sensitive to
seed placement and better at edge
localization than geodesic methods.
This leads to increased
segmentation accuracy and reduced
effort on the part of the user. Finally
Segmented Liver and Tumor Regions
were shown from the abdominal
Computed Tomographic image. Christo
Ananth et al. [8] proposed a system, in
which a predicate is defined for
measuring the evidence for a boundary
between two regions using Geodesic
Graph-based representation of the
image. The algorithm is applied to image
segmentation using two different kinds
of local neighborhoods in constructing
the graph. Liver and hepatic tumor
segmentation can be automatically
processed by the Geodesic graph-cut
based method. This system has
concentrated on finding a fast and
interactive segmentation method for
liver and tumor segmentation. In the
preprocessing stage, the CT image
process is carried over with mean shift
filter and statistical thresholding method
for reducing processing area with
improving detections rate. Second stage
is liver segmentation; the liver region
has been segmented using the algorithm
of the proposed method. The next stage
tumor segmentation also followed the
same steps.
Finally the liver and tumor
regions are separately segmented from
the computer tomography image. Christo
Ananth et al. [9] proposed a system in
which the cross-diamond search
algorithm employs two diamond search
patterns (a large and small) and a
halfway-stop technique. It finds small
motion vectors with fewer search points
than the DS algorithm while maintaining
similar or even better search quality. The
efficient Three Step Search (E3SS)
algorithm requires less computation and
performs better in terms of PSNR.
Modified objected block-base vector
search algorithm (MOBS) fully utilizes
the correlations existing in motion
vectors to reduce the computations. Fast
Objected - Base Efficient (FOBE) Three
Step Search algorithm combines E3SS
and MOBS. By combining these two
existing algorithms CDS and MOBS, a
new algorithm is proposed with reduced
computational complexity without
degradation in quality. Christo Ananth et
al. [10] proposed a system in which this
study presented the implementation of
two fully automatic liver and tumors
segmentation techniques and their
comparative assessment. The described
adaptive initialization method enabled
fully automatic liver surface
segmentation with both GVF active
contour and graph-cut techniques,
demonstrating the feasibility of two
different approaches. The comparative
assessment showed that the graph-cut
method provided superior results in
terms of accuracy and did not present the
described main limitations related to the
GVF method. The proposed image
processing method will improve
computerized CT-based 3-D
visualizations enabling noninvasive
diagnosis of hepatic tumors. The
described imaging approach might be
valuable also for monitoring of
postoperative outcomes through CT-
volumetric assessments.
International Conference on Recent Advances in Management, Architecture, Technology and Engineering
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st
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Processing time is an important
feature for any computer-aided diagnosis
system, especially in the intra-operative
phase. Christo Ananth et al. [11]
proposed a system in which an automatic
anatomy segmentation method is
proposed which effectively combines the
Active Appearance Model, Live Wire
and Graph Cut (ALG) ideas to exploit
their complementary strengths. It
consists of three main parts: model
building, initialization, and delineation.
For the initialization (recognition) part, a
pseudo strategy is employed and the
organs are segmented slice by slice via
the OAAM (Oriented Active
Appearance method). The purpose of
initialization is to provide rough object
localization and shape constraints for a
latter GC method, which will produce
refined delineation. It is better to have a
fast and robust method than a slow and
more accurate technique for
initialization. Christo Ananth et al. [12]
proposed a system which uses
intermediate features of maximum
overlap wavelet transform (IMOWT) as
a pre-processing step. The coefficients
derived from IMOWT are subjected to
2D histogram Grouping. This method is
simple, fast and unsupervised. 2D
histograms are used to obtain Grouping
of color image. This Grouping output
gives three segmentation maps which are
fused together to get the final segmented
output. This method produces good
segmentation results when compared to
the direct application of 2D Histogram
Grouping.
IMOWT is the efficient
transform in which a set of wavelet
features of the same size of various
levels of resolutions and different local
window sizes for different levels are
used. IMOWT is efficient because of its
time effectiveness, flexibility and
translation invariance which are useful
for good segmentation results. Christo
Ananth et al. [13] proposed a system in
which OWT extracts wavelet features
which give a good separation of different
patterns. Moreover the proposed
algorithm uses morphological operators
for effective segmentation. From the
qualitative and quantitative results, it is
concluded that our proposed method has
improved segmentation quality and it is
reliable, fast and can be used with
reduced computational complexity than
direct applications of Histogram
Clustering. The main advantage of this
method is the use of single parameter
and also very faster. While comparing
with five color spaces, segmentation
scheme produces results noticeably
better in RGB color space compared to
all other color spaces. Christo Ananth et
al. [14] presented an automatic
segmentation method which effectively
combines Active Contour Model, Live
Wire method and Graph Cut approach
(CLG). The aim of Live wire method is
to provide control to the user on
segmentation process during execution.
Active Contour Model provides a
statistical model of object shape and
appearance to a new image which are
built during a training phase. In the
graph cut technique, each pixel is
represented as a node and the distance
between those nodes is represented as
edges. In graph theory, a cut is a
partition of the nodes that divides the
graph into two disjoint subsets. For
initialization, a pseudo strategy is
employed and the organs are segmented
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st
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slice by slice through the OACAM
(Oriented Active Contour Appearance
Model). Initialization provides rough
object localization and shape constraints
which produce refined delineation.
This method is tested with
different set of images including CT and
MR images especially 3D images and
produced perfect segmentation results.
Christo Ananth et al. [15] proposed a
work, in this work, a framework of
feature distribution scheme is proposed
for object matching. In this approach,
information is distributed in such a way
that each individual node maintains only
a small amount of information about the
objects seen by the network.
Nevertheless, this amount is sufficient to
efficiently route queries through the
network without any degradation of the
matching performance. Digital image
processing approaches have been
investigated to reconstruct a high
resolution image from aliased low
resolution images. The accurate
registrations between low resolution
images are very important to the
reconstruction of a high resolution
image. The proposed feature distribution
scheme results in far lower network
traffic load.
To achieve the maximum
performance as with the full distribution
of feature vectors, a set of requirements
regarding abstraction, storage space,
similarity metric and convergence has
been proposed to implement this work in
C++ and QT. Christo Ananth et al. [16]
discussed about an important work
which presents a metal detecting robot
using RF communication with wireless
audio and video transmission and it is
designed and implemented with Atmel
89C51 MCU in embedded system
domain. The robot is moved in particular
direction using switches and the images
are captured along with the audio and
images are watched on the television
.Experimental work has been carried out
carefully. The result shows that higher
efficiency is indeed achieved using the
embedded system. The proposed method
is verified to be highly beneficial for the
security purpose and industrial purpose.
The mine sensor worked at a constant
speed without any problem despite its
extension, meeting the specification
required for the mine detection sensor. It
contributed to the improvement of
detection rate, while enhancing the
operability as evidenced by completion
of all the detection work as scheduled.
The tests demonstrated that the robot
would not pose any performance
problem for installation of the mine
detection sensor. On the other hand,
however, the tests also clearly indicated
areas where improvement, modification,
specification change and additional
features to the robot are required to serve
better for the intended purpose. Valuable
data and hints were obtained in
connection with such issues as control
method with the mine detection robot
tilted, merits and drawbacks of mounting
the sensor, cost, handling the cable
between the robot and support vehicle,
maintainability, serviceability and
easiness of adjustments.
These issues became identified as a
result of our engineers conducting both
the domestic tests and the overseas tests
by themselves, and in this respect the
findings were all the more practical.
Christo Ananth et al. [17] discussed
about Vision based Path Planning and
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Tracking control using Mobile Robot.
This paper proposes a novel
methodology for autonomous mobile
robot navigation utilizing the concept of
tracking control. Vision-based path
planning and subsequent tracking are
performed by utilizing proposed stable
adaptive state feedback fuzzy tracking
controllers designed using the Lyapunov
theory and particle-swarm-optimization
(PSO)-based hybrid approaches. The
objective is to design two self-adaptive
fuzzy controllers, for x-direction and y-
direction movements, optimizing both its
structures and free parameters, such that
the designed controllers can guarantee
desired stability and, simultaneously, can
provide satisfactory tracking
performance for the vision-based
navigation of mobile robot.
The design methodology for the
controllers simultaneously utilizes the
global search capability of PSO and
Lyapunovtheory-based local search
method, thus providing a high degree of
automation. Two different variants of
hybrid approaches have been employed
in this work. The proposed schemes
have been implemented in both
simulation and experimentations with a
real robot, and the results demonstrate
the usefulness of the proposed concept.
Christo Ananth et al. [18] discussed
about a model, a new model is designed
for boundary detection and applied it to
object segmentation problem in medical
images. Our edge following technique
incorporates a vector image model and
the edge map information. The proposed
technique was applied to detect the
object boundaries in several types of
noisy images where the ill-defined edges
were encountered. The proposed
techniques performances on object
segmentation and computation time were
evaluated by comparing with the popular
methods, i.e., the ACM, GVF snake
models. Several synthetic noisy images
were created and tested.
The method is successfully tested
in different types of medical images
including aortas in cardiovascular MR
images, and heart in CT images. Christo
Ananth et al. [19] discussed about the
issue of intuitive frontal area/foundation
division in still pictures is of awesome
down to earth significance in picture
altering. They maintain a strategic
distance from the limit length
predisposition of chart cut strategies and
results in expanded affectability to seed
situation. Another proposed technique
for completely programmed handling
structures is given taking into account
Graph-cut and Geodesic Graph cut
calculations. This paper addresses the
issue of dividing liver and tumor locales
from the stomach CT pictures. The
absence of edge displaying in geodesic
or comparable methodologies confines
their capacity to exactly restrict object
limits, something at which chart cut
strategies by and large exceed
expectations. A predicate is
characterized for measuring the
confirmation for a limit between two
locales utilizing Geodesic Graph-based
representation of the picture. The
calculation is connected to picture
division utilizing two various types of
nearby neighborhoods in building the
chart. Liver and hepatic tumor division
can be naturally prepared by the
Geodesic chart cut based strategy. This
framework has focused on finding a
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quick and intuitive division strategy for
liver and tumor division.
In the pre-handling stage, Mean
movement channel is connected to CT
picture process and factual thresholding
technique is connected for diminishing
preparing zone with enhancing
discoveries rate. In the Second stage, the
liver area has been divided utilizing the
calculation of the proposed strategy.
Next, the tumor district has been
portioned utilizing Geodesic Graph cut
strategy. Results demonstrate that the
proposed strategy is less inclined to
shortcutting than run of the mill diagram
cut techniques while being less delicate
to seed position and preferable at edge
restriction over geodesic strategies. This
prompts expanded division exactness
and decreased exertion with respect to
the client. At long last Segmented Liver
and Tumor Regions were appeared from
the stomach Computed Tomographic
picture. Christo Ananth et al. [20]
discussed about efficient content-based
medical image retrieval, dignified
according to the Patterns for Next
generation Database systems (PANDA)
framework for pattern representation and
management. The proposed scheme use
2-D Wavelet Transform that involves
block-based low-level feature extraction
from images. An expectation–
maximization algorithm is used to
cluster the feature space to form higher
level, semantically meaningful patterns.
Then, the 2-component property of
PANDA is exploited: the similarity
between two clusters is estimated as a
function of the similarity of both their
structures and the measure components.
Experiments were performed on a large
set of reference radiographic images,
using different kinds of features to
encode the low-level image content.
Through this experimentation, it is
shown that the proposed scheme can be
efficiently and effectively applied for
medical image retrieval from large
databases, providing unsupervised
semantic interpretation of the results,
which can be further extended by
knowledge representation
methodologies.
3. Pre-processing and Number
Plate Localization
.
Fig.3. Input gray-scale image.
A number of algorithms are
suggested for number plate localization
such as: multiple interlacing algorithm,
Fourier domain filtering,and color image
processing. These algorithms however
do not satisfactorily work for Indian
number plates since they assume
features like: border for the plate, color
of plate and color of characters to be
present on the number plate. Christo
Ananth et al. [21] discussed about E-
plane and H-plane waveguides
(Microwave Engineering) applicable to
image processing. Christo Ananth et al.
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[22] discussed about principles of
electronic devices to explain this feature.
Hence, we designed and implemented
‘Feature-based number plate
localization’ method well suited for
Indian conditions. This approach
consists of number of algorithms
developed on the basis of general
features of both, characters and
number plate.
For pre-processing, the input gray-
scale image (fig.3) is adaptively
converted into binary image (fig. 4)
using Ostu’s method. This method is
better suited for our application
compared to other adaptive binarizartion
methods like the Niblack’s method.
Fig.4. Adaptively binarized image:
Ostu’s method
Step 1: A mask having shape of inverted
‘L’ and size equal to maximum possible
character dimensions is rolled
throughout the binary image. At every
increment, a position is shortlisted as
possible character location if: There is at
least a single white pixel on the mask
and there is at least a single white pixel
on the immediate next row and column
of the mask (fig 5).
Step 2: Size of each shortlisted character
calculated. If it is less than half of
maximum possible character size that
location discarded (fig.6)
Fig.5. Output of step 1
Fig.6. Output of step 2
Step 3: White pixel density of each
probable character is calculated. If it is
above 40% of total number of pixels,
only then the location is preserved (fig.
7).
Fig.7. Output of step 3.
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All the preceding steps are carried out in
a single iteration to achieve time
optimization.
Step 4: For a set of rows having height
equal to maximum possible number
plate height, white pixel density is
calculated. If it is not above certain
threshold, that area is discarded (fig. 8).
Christo Ananth et al. [23] gave a brief
outline on Electronic devices and
circuits which is the basis for formation
of patterns.
Fig.8. Output of step 4.
Fig.9. Output of step 5.
Step 5: For a set of columns having
width equal to maximum possible
number plate width, white pixel
density is calculated. If it is not above
certain threshold, that area is again
discarded (fig. 9).
Step 6: Number of characters in the
finalized number plate areas is
calculated. If number of characters is
less than four, then that area is
discarded. If two number plate areas
with nearly same number of characters
are found in close vicinity of each other,
then those areas are merged together.
After applying these steps, the number
plate within the image is exactly located
and all other background noise is
eliminated. Number plate is now
extracted (fig. 10) from the input binary
image and is then eroded using square of
size 2X2 which eliminates overlapping
of characters before segmentation.
4. Character Segmentation
Various methods like blob coloring,
peak-to-valley method are suggested for
character segmentation. However, these
methods are not suitable for Indian
number plates since they do not provide
good results in cases where the
characters are overlapping and are also
timeconsuming.
Fig.10. Sample output of Image
Scissoring
To have reliability and time-
optimization, a new ‘Image Scissoring’
algorithm is developed. In thisalgorithm,
the number plate is vertically scanned
and scissored at the row on which there
is no white pixel (i.e., a blank row) and
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the scissored area is copied into new
matrix. This scanning procedure
proceeds further in search of a blank row
and thus different scissored areas are
obtained in different matrices. Indian
number plates can have either single or
double rows. Hence, maximum two
matrices must co-exist. To discard false
matrices, heights of the matrices are
compared. If the height of any of matrix
is less than 1/4th of the height of
tallest matrix, then the prior matrix is
discarded. The same procedure is
repeated horizontally on each matrix and
using width as a threshold, individual
characters are segmented (fig. 10).
5. Pre-recognition character
enhancement
In this step, segmented characters are
extracted from input grayscale image.
Then each character is adaptively
binarized using Ostu’s method. After
that, the binary character is scissored
centered. These steps help to optimize
the further recognition process (fig. 11).
Fig.11. Sample output after character
enhancement
6. Character Recognition and
Syntax Checking
This is the most critical stage of the
ANPR system. Direct template matching
can be used to identify characters.
However, this method yields a very
low success rate for font variations
which are commonly found in Indian
number plates.
Artificial Neural Networks like
BPNNs can be used to classify the
characters. However, they do not
provide hardware and time optimization.
Therefore statistical feature extraction
has been used. In this method, initially
the character is divided into twelve equal
parts and fourteen features are extracted
from every part. The features used are
binary edges (2X2) of fourteen types.
The feature vector is thus formed is
compared with feature vectors of all the
stored templates (fig. 13) and the
maximum value of correlation is
calculated to give the right character.
Lastly syntax checking is done
to ensure that any false characters are
not recognized as a valid license
number.
7. Experiments and Testing
The system was tested with a set of
images not used during testing, having
wide variations in illumination
conditions. The complete
recognition process takes an average of 2
seconds. This can be further improved
by optimizing the code. If cases where
the number plate script is non-English or
the number plate is badly distorted
are excluded then, 82% of the plates
were recognized correctly. The
performance of individual sections is:
87% for number plate localization, 95%
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for character segmentation and 85% for
character recognition.
8. Conclusions and Future
Research
The system works satisfactorily for wide
variations in illumination conditions and
different types of number plates
commonly found in India. It is definitely
a better alternative to the existing
manual systems in India.
Currently there are certain restrictions
on parameters like speed of the vehicle,
script on the number plate, skew in the
image which can be aptly removed by
enhancing the algorithms further.
References
[1] Christo Ananth, I.Uma Sankari, A.Vidhya,
M.Vickneshwari, P.Karthiga, “Efficient Sensor Network for
Vehicle Security”, International Journal of Advanced
Scientific and Technical Research (IJASR), Volume 2, Issue
4, March-April 2014,pp – 871-877
[2] Christo Ananth, C.Sudalai@UtchiMahali, N.Ebenesar
Jebadurai, S.Sankari@Saranya, T.Archana, “Intelligent
sensor Network for Vehicle Maintenance system”,
International Journal of Emerging Trends in Engineering and
Development (IJETED), Vol.3, Issue 4, May 2014, pp-
361-369
[3] Christo Ananth, S.Shafiqa Shalaysha, M.Vaishnavi, J.Sasi
Rabiyathul Sabena, A.P.L.Sangeetha, M.Santhi, “Realtime
Monitoring Of Cardiac Patients At Distance Using Tarang
Communication”, International Journal of Innovative
Research in Engineering & Science (IJIRES), Volume 9,
Issue 3,September 2014,pp-15-20
[4] Christo Ananth, G.Poncelina, M.Poolammal, S.Priyanka,
M.Rakshana, Praghash.K., “GSM Based AMR”,
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Ecology, Science and Technology (IJARBEST), Volume
1,Issue 4,July 2015, pp:26-28
[5] Christo Ananth, Kanthimathi, Krishnammal, Jeyabala,
Jothi Monika, Muthu Veni, “GSM Based Automatic
Electricity Billing System”, International Journal Of
Advanced Research Trends In Engineering And Technology
(IJARTET), Volume 2, Issue 7, July 2015), pp:16-21
[6] Christo Ananth, G.Gayathri, M.Majitha Barvin, N.Juki
Parsana, M.Parvin Banu, “Image Segmentation by Multi-
shape GC-OAAM”, American Journal of Sustainable Cities
and Society (AJSCS), Vol. 1, Issue 3, January 2014, pp 274-
280
[7] Christo Ananth, D.L.Roshni Bai , K.Renuka, C.Savithra,
A.Vidhya, “Interactive Automatic Hepatic Tumor CT Image
Segmentation”, International Journal of Emerging Research
in Management &Technology (IJERMT), Volume-3, Issue-1,
January 2014,pp 16-20
[8]Christo Ananth, D.L.Roshni Bai, K.Renuka, A.Vidhya,
C.Savithra, “Liver and Hepatic Tumor Segmentation in 3D
CT Images”, International Journal of Advanced Research in
Computer Engineering & Technology (IJARCET), Volume
3,Issue-2, February 2014,pp 496-503
[9] Christo Ananth, A.Sujitha Nandhini, A.Subha Shree,
S.V.Ramyaa, J.Princess, “Fobe Algorithm for Video
Processing”, International Journal of Advanced Research in
Electrical, Electronics and Instrumentation Engineering
(IJAREEIE), Vol. 3, Issue 3,March 2014 , pp 7569-7574
[10] Christo Ananth, Karthika.S, Shivangi Singh, Jennifer
Christa.J, Gracelyn Ida.I, “Graph Cutting Tumor Images”,
International Journal of Advanced Research in Computer
Science and Software Engineering (IJARCSSE), Volume 4,
Issue 3, March 2014,pp 309-314
[11] Christo Ananth, G.Gayathri, I.Uma Sankari, A.Vidhya,
P.Karthiga, “Automatic Image Segmentation method based
on ALG”, International Journal of Innovative Research in
Computer and Communication Engineering (IJIRCCE), Vol.
2, Issue 4, April 2014,pp- 3716-3721
[12] Christo Ananth, A.S.Senthilkani, S.Kamala Gomathy,
J.Arockia Renilda, G.Blesslin Jebitha, Sankari @Saranya.S.,
“Color Image Segmentation using IMOWT with 2D
Histogram Grouping”, International Journal of Computer
Science and Mobile Computing (IJCSMC), Vol. 3, Issue. 5,
May 2014, pp-1 – 7
[13] Christo Ananth, A.S.Senthilkani, Praghash.K, Chakka
Raja.M., Jerrin John, I.Annadurai, “Overlap Wavelet
Transform for Image Segmentation”, International Journal of
Electronics Communication and Computer Technology
(IJECCT), Volume 4, Issue 3 (May 2014), pp-656-658
[14] Christo Ananth, S.Santhana Priya, S.Manisha, T.Ezhil
Jothi, M.S.Ramasubhaeswari, “CLG for Automatic Image
Segmentation”, International Journal of Electrical and
Electronics Research (IJEER), Vol. 2, Issue 3, Month: July -
September 2014, pp: 51-57
[15] Christo Ananth, R.Nikitha, C.K.Sankavi, H.Mehnaz,
N.Rajalakshmi, “High Resolution Image Reconstruction with
Smart Camera Network”, International Journal of Advanced
Research in Biology, Ecology, Science and Technology
(IJARBEST), Volume 1,Issue 4,July 2015, pp:1-5
[16] Christo Ananth, B.Prem Kumar, M.Sai Suman, D.Paul
Samuel, V.Pillai Vishal Vadivel, Praghash.K., “Autonomous
Mobile Robot Navigation System”, International Journal of
Advanced Research in Biology, Ecology, Science and
Technology (IJARBEST), Volume 1,Issue 4,July
2015,pp:15-19
[17] Christo Ananth , Mersi Jesintha.R., Jeba Roslin.R.,
Sahaya Nithya.S., Niveda V.C.Mani, Praghash.K., “Vision
based Path Planning and Tracking control using Mobile
Robot”, International Journal of Advanced Research in
Biology, Ecology, Science and Technology (IJARBEST),
Volume 1,Issue 4,July 2015, pp:20-25
[18] Christo Ananth, S.Suryakala, I.V.Sushmitha Dani,
I.Shibiya Sherlin, S.Sheba Monic, A.Sushma Thavakumari,
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“Vector Image Model to Object Boundary Detection in Noisy
Images”, International Journal of Advanced Research in
Management, Architecture, Technology and Engineering
(IJARMATE), Volume 1,Issue 2,September 2015, pp:13-15
[19] Christo Ananth,” Geo-cutting Liver Tumor”,
International Journal of Advanced Research in Management,
Architecture, Technology and Engineering (IJARMATE),
Volume 2,Issue 3, March 2016,pp:122-128
[20] Christo Ananth, K.Kalaiselvi, C.Kavya, S.Selvakani,
P.Sorimuthu Iyan, “Patterns for Next generation Database
Systems - A study”, International Journal of Advanced
Research in Management, Architecture, Technology and
Engineering (IJARMATE), Volume 2, Issue 4, April 2016,
pp: 114-119
[21] Christo Ananth, S.Esakki Rajavel, S.Allwin Devaraj,
M.Suresh Chinnathampy. "RF and Microwave Engineering
(Microwave Engineering)." (2014): 300,ACES Publishers
[22] Christo Ananth, S.Esakki Rajavel, S.Allwin Devaraj,
P.Kannan. "Electronic Devices." (2014): 300, ACES
Publishers.
[23] Christo Ananth,W.Stalin Jacob,P.Jenifer Darling Rosita.
"A Brief Outline On ELECTRONIC DEVICES &
CIRCUITS." (2016): 300.
[24] Leonard G. C. Hamey, Colin Priest, “Automatic Number
Plate Recognition for Australian Conditions”, Proceedings of
the Digital Imaging Computing: Techniques and
Applications (DICTA), pp. 14- 21, December 2005.