International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 4 - Mar 2014
Localization of Multistyle License Plate Number
using Dynamic Image Processing and Genetic
Algorithm
Mrs Suganya.T1, Mr Prasanth.P2, Mr Vignesh.D.M3, Mr Vignesh.D4
1.Assistant Professor,CSE,SNS college of Engineering,Coimbatore
2.Student,CSE,SNS college of Engineering,Coimbatore
3. Student,CSE,SNS college of Engineering,Coimbatore
4. Student,CSE,SNS college of Engineering,Coimbatore
Abstract— Genetic Algorithm and dynamic image processing
techniques are used for localizing the license plate number in an
image. Recognition of LP symbols in a picture with high
immunity for changes in illumination is done using this process.
The proposed system finds multistyle number plates in an
image. Implementation is done using MATLAB with sample
images for proving the proposed model’s distinction.
Keywords—genetic algorithm;
processing; Localization;
License
plate;
image
I.
INTRODUCTION
In automatic License plate recognition
the most important step is to localize the license plate in the
particular captured image.In order to recognize the license
number, the number plate area has to be first located in the
image. The goal of localization is to eliminate all the
background and preserve only the number plate area from the
input image. From this number plate area, the individual
characters are then segmented out and recognized.
A new technique is introduced in this
paper which detects LP symbols without using any
information associated with the plate’s outer shape or internal
colors to allow for the detection of the license numbers in
case of shape or color distortion either physically or due to
capturing conditions such as poor lighting, shadows and
camera position and orientation.
Regular NTSC method for converting the
image into grey scale has been used.In the binarization phase
we use the Niblack’s algorithm. After Morphological
operations we use the Connected Component Analysis (CCA)
for identifying the numbers in the image.Genetic algorithm is
used for optimizing the result.
ISSN: 2231-5381
The Digital image processing is the use of
computer algorithms to perform image processing on digital
images. As a subfield of digital signal processing, digital
image processing has many advantages over analog image
processing; it allows a much wider range of algorithms to be
applied to the input data, and can avoid problems such as the
build-up of noise and signal distortion during processing.
The various basic steps are as follows
Image
Acquisition,
Image
preprocessing,Image
segmentation, Image Representation and Description ,Image
Recognition and Interpretation and Knowledge base.
MATLAB is a good performing language
for technical computing. It consists of computation,
visualization, and programming in an easy environment
where problems and solutions are expressed in known
mathematical notation.
MATLAB is an interaction based system
whose basic data element is an array. It requires no
dimensioning. It allows you to solve lots of technical
computing problems, including those with matrix and vector
formulations, in a fraction of the time it would take to write a
program in a language such as C or FORTRAN.
II.
SYSTEM OVERVIEW
The system consists of image processing techniques and
the genetic algorithm.GA selects the optimum LP symbol
locations in the concerned LP.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 4 - Mar 2014
environment around it resulting in several illumination
problems.
Hence binarization of the image according to a fixed global
threshold is not suitable to overcome these problems. IN our
system Niblack’s algorithm is used for it provides better
results than other algorithms(reference: A Feature Based
Approach for Localization of Indian Number Plates Prathamesh Kulkarni (Student Member, IEEE))
The output of Niblack's method is significant and has
most acceptable result out of all thresholding techniques in
segmenting text documents.
Color to Greyscale conversion
The input image is captured as a color image
taking into account further processing of the image to
extract other information relevant to the concerned vehicle.
Color (RGB) to grayscale (gs) conversion is performed
using the standard NTSC method by eliminating the hue
and saturation information while retaining the luminance as
follows: Y=0.299*R+0.587*G+0.114*
The given coefficients represent the
measured
intensity
perception
of
an
average trichromat humans. In particular, human vision is
most sensitive to green and least sensitive to blue. To
encode grayscale intensity in linear RGB, each of the three
primaries can be set to equal the calculated linear
luminance Y (replacing R,G,B by Y,Y,Y to get this linear
grayscale).
A. Adaptive Binarization
Converting the input image into a binary image is one of
the most sensitive stages in localizing LPs due to spatial and
temporal variations encountered in the plate itself and the
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B. Morphological Operations
Morphological operations such as dilation and
erosion are important processes needed for most pattern
recognition systems to eliminate noisy objects and retain only
objects expected to represent the targeted patterns. In LP
detection, closing operation (dilation followed by erosion) is
performed to fill noisy holes inside candidate objects and to
connect broken symbols. On the other hand, opening (erosion
followed by dilation) is applied to remove objects that are
thinner than the LP symbols.
In our system, closing is applied to fill spaces
that break the bodies of symbols using a 3-pixel-disk element
in the first experiment. This process is very important
especially for the recent Saudi LP layout where a light gray
watermark is used for authentication purposes. This
watermark becomes white after the binarization process and
breaks down most of the bodies of the LP symbols.
C. Connected Component Analysis
CCA is a well-known technique in image
processing which scans an image and groups pixels into
labeled components based on pixel connectivity. A 4-point
CCA stage is performed to locate all the objects inside the
binary image produced from the previous stage. The output of
this stage is an array of N objects.
Connected component
labeling , region
labeling, alternatively connected component analysis, blob
extraction blob discovery, or region extraction is an
algorithmic application of graph theory, where groups
of connected components are uniquely labeled based on a
given particular heuristic. Connected-component labeling is
not to be confused with segmentation because they are
different.
Connected-component labeling is usually used
in computer
vision to
detect
connected regions in binary digital images, although many
color images and data with higher dimensions can also be
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 4 - Mar 2014
processed. When integrated into an image recognition system
or human-computer
interaction interface,
connected
component labeling can operate on a variety of data.
Blob extraction is usually performed on binary
images which are processed from a thresholding step. Blobs
may be counted, filtered, and tracked. Blob extraction is
related to but distinct from blob detection.
E. Genetic Algorithm
The proposed fitness is selected as the inverse of
the calculated objective distance between the prototype
chromosome and the current chromosome. Before clarifying
how the objective distance is measured, we will show first
how the geometric relationships between the objects inside a
compound object are represented, followed by a discussion of
parameter adaption in case of various LP detection layouts.
Compound object representation:
For any two objects, we will use two types of
geometrical relationships that can be defined as follows: 1.
Position relationship: The position relationship will be
represented by the relative distances between the bounding
boxes of the two objects in the X and Y directions. 2. Size
relationship: The size relationship will be represented as the
relative differences in their bounding boxes’ heights and
widths.
D. Size Filtering
The objects extracted from the CCA stage are
filtered on the basis of their widths Wobj and heights Hobj
such that the dimensions of the LP symbols lie between their
respective thresholds as follows:
Wmin ≤ Wobj ≤Wmax
In the above relationships, relativity is achieved by
dividing on the height or width of the first object depending
on which is more stable for practical reasons although it is
logically to divide differences in heights on height and
differences in widths on width to compensate for scale
changes in the general case. For most LPs , the heights of
symbols are almost equal for both digits and letters while
some symbols have different widths than others. Hence,
normalized relationships between any two objects can be
based on the height of the first object.
The position relationship is defined in the two
directions
by
the
following
formulas:
and
Hmin ≤Hobj ≤ Hmax
Where Hmin and Wmin are the values below
which a symbol cannot be recognized (8 pixels for example)
and Wmax can be set to the image width divided by the
number of symbols in the license number. Hmax is calculated
as Wmax divided by the aspect ratio of the used font. The
ranges of these values can be narrowed in the case of a
mounted camera to speed up the process of detection but for a
moving camera, the ranges depend on the required object to
camera distance range. The output of this stage is an array of
M
objects.
The size relationship is defined by the following formulas:
F.
The selection method
In our system, the Stochastic Universal Sampling (SUS)
method has been adopted for the selection of offspring in the
new generation. In SUS method each individual is mapped to
a continuous segment of a line equal in size to its fitness as in
roulette-wheel selection. Then, a number of equally spaced
pointers are placed over the line depending on the percentage
of individuals to be selected. In our system, individuals of
ninety percent of the population size are selected to be
exposed to mutation and crossover operators.
Mutation operators
Mutation is needed because successive removal of less fit
members in genetic iterations may eliminate some aspects of
genetic material forever. By performing mutation in the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 4 - Mar 2014
chromosomes, GAs ensure that new parts of the search space
are reached to maintain the mating pool variety [32]. We
have implemented two types of interchangeably used
mutation operators; substitution operator and swap operator
as follows:
Substitution operator
In this type of operators, a random position in the
chromosome is selected and the corresponding allele is
changed by a new random object from the M available
objects. The new object should be legitimate which means it
does not belong to the current mutated chromosome.
Swap operator
In this operator, we implemented the reciprocal exchange
mutation that selects two genes randomly and swaps them
.This operator has the advantage of rearrangement of the
mutated chromosome in a way that may improve its fitness
by reordering of the internal objects to match the prototype’s
order.
There are many methods to implement the crossover
operator. For instance, single point crossover, two point
crossover, n-point crossover, uniform crossover, three parent
crossover and, alternating crossover , etc. These operators are
not suitable for our problem because the resultant children
will not be valid because of repeated genes that may be
produced in the generated chromosomes. Also, if we prevent
repetition, the resultant children‘s fitness will be enhanced
slowly because of the randomness of these mechanisms. An
alternative solution is to design a suitable crossover operator
that insures enhancement of the generated offspring. Since,
in case of LP detection problem, GA is used to search for a
sequence of objects having nearly the same y-position and
placed in order according to their x-positions, then the
problem can be gradually solved by dividing the recombined
chromosomes’ objects according to their y-positions into two
groups and then sorting each group (constituting a
chromosome) according to the x-positions.
V. MINIMIZING CCAT PROBLEMS
The main drawback of all systems relying on CCAT is the
sensitivity to negative or positive noise that may cause some
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symbols to be connected to other objects or broken into
smaller objects. Connectivity is also affected by many causes
such as bullets, dirt, aging, occlusion, shadows, or due to
image processing operations like dilation and erosion. The
effect of connected symbols either to their neighbors or to the
frame of the LP is the introduction of high objective distance
when matched with the corresponding GRM. This surely, will
cause the system to select another near symbol or to report
absence of LP in the current image. The same is said in the
case of broken bodies, where a large error is detected in the
relative width/height terms. The proposed solution for this
problem is to introduce a new argument in the genetic
algorithm which indicates the number of symbols to skip
(NS) during the evaluation of the objective distance. This
number is initialized to zero in the first run of the GA and
according to the optimum OD threshold value (ODT), a
decision is made either to accept the selected chromosome or
to increase the NS argument and execute a further run of the
GA.
III.
CONCLUSION
Hence in this paper we prove that with effective
combination of the dynamic image processing and the genetic
algorithm we have identified the multi-style number plates in
an image by optimization. The procedure is useful in finding
multiple number plates in the given image also. Various
images are tested and the system finds the localized number
plate with effectiveness.
IV.
REFERENCES
[1] Y. Qiu, M. Sun, and W. Zhou, “License Plate Extraction
Based on Vertical Edge Detection and Mathematical
Morphology,” International Conference on Computational
Intelligence and Software Engineering, pp. 1-5., 11-13 Dec.
2009.
[2] A. Ahmadyfard and V. Abolghasemi,“ Detecting License
Plate Using Texture and Color Information,” IST 2008,
International Symposium on Telecommunications, pp. 804–
808, 2008.
[3] G. Li, R. Yuan, Z. Yang, and X. Huang, “A Yellow
License Plate Location Method Based on RGB Model of
Color Image and Texture of Plate,” Second Workshop on
Digital Media and its Application in Museum & Heritages,
pp. 42–46, 2007.
[4] X. Shi, W. Zhao, Y. Shen, and O. Gervasi, "Automatic
License Plate Recognition System Based on Color Image
Processing," Lecture Notes on Computer Science, SpringerVerlag, vol. 3483, pp.1159 -1168, 2005.
[5] M. Deriche, “ GCC License Plates Detection and
Recognition Using Morphological Filtering and Neural
Networks,” Int J. on Comp. Sci. and Info Security, IJCSIS,
vol. 8, No. 8, pp. 263-269, Dec, 2010.
[6] O. Villegas, D. Balderrama, H. Domínguez and V.
Sánchez, ” License Plate Recognition Using a Novel Fuzzy
Multilayer Neural Network,” International Journal of
Computers”, Issue 1,vol. 3, 2009.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 4 - Mar 2014
[7] S.H. Mohades Kasaei, S.M. Mohades Kasaei and S.A.
Monadjemi, “A Novel Morphological Method for Detection
and Recognition of Vehicle License Plate,” American
Journal of Applied Science, vol.6 no.12, pp. 2066-2070,
2009.
[8] Prathamesh Kulkarni,A Feature Based Approach for
Localization of Indian
Number Plates.
[9]Chu Due Nguyen,robust Car License Plate Localization
using a Novel Texture Descriptor
[10] V. Abolghasemi, and A. Ahmadyfard, ” A Fast
Algorithm for License Plate Detection,” International
Conference on Visual Information Systems, Springer, Vol.
4781, pp. 468-477, 2007.
V.
AUTHORS PROFILE
Suganya T. obtained her
bachelor’s
degree in Computer science and
Engineering from Anna University
Chennai, TamilNadu, India and Masters
Degree in Computer science and
Engineering from Anna University
Chennai, TamilNadu, India. She has more
than 7 years of teaching experience and
currently, she is working as Assistant Professor in
Department of Computer Science and Engineering, in SNS
College of Engineering, Coimbatore, TamilNadu. Her areas
of interest include Pattern Analysis, Operating Systems and
Image Processing. She has published 8 papers in reput ed
international, national level conferences and International
journals.
Prasanth.P is a student of SNS college of
Engineering pursuing his BE Computer
Science Engineering.He has published a
paper on mathematical models at Madura
University.his interests are Artificial
Intelligence and Evolutionary computing.
Vignesh.D.M is a student of SNS college of
Engineering pursuing his BE Computer
Science Engineering.He has presented a
paper on advancements in cloud computing
at PSG college of Engineering
Vignesh.D is a student of SNS college of
Engineering pursuing his BE Computer
Science Engineering.He has his interests in
Image processing and Genetic Algorithm.
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