In this research, the design of a new genetic algorithm (GA) is introduced to detect the locations of license plate (LP) symbols. An adaptive threshold method is applied to overcome the dynamic changes of illumination conditions when Converting the image into binary. Connected component analysis technique (CCAT) is used to detect candidate objects inside the unknown image. A scale-invariant geometric relationship matrix is introduced to model the layout of symbols in any LP that simplifies system adaptability when applied in different Countries. Moreover, two new crossover operators, based on sorting, are introduced, which greatly improve the convergence speed of the system. Most of the CCAT problems, such as touching or broken bodies, are minimized by modifying the GA to perform partial match until reaching an acceptable fitness Value. The system is implemented using MATLAB and various image samples are experimented with to verify the distinction of the proposed system. Encouraging results with 98.4% overall accuracy are reported for two different datasets having variability in orientation, scaling, plate location, illumination, and complex Background. Examples of distorted plate images are successfully detected due to the independency on the shape, color, or location of the plate. Index Terms-Genetic algorithms (GAs), image processing, image representations, license plate detection, machine vision, road vehicle identification, sorting crossover. http://kaashivinfotech.com/ http://inplanttrainingchennai.com/ http://inplanttraining-in-chennai.com/ http://internshipinchennai.in/ http://inplant-training.org/ http://kernelmind.com/ http://inplanttraining-in-chennai.com/ http://inplanttrainingchennai.com/

1. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 18, NO. 2, APRIL 2014
Localization of License Plate Number Using Dynamic Image Processing
Techniques and Genetic Algorithms

2. Abstract
 In this research, the design of a new genetic algorithm (GA) is introduced to
detect the locations of license plate (LP) symbols. An adaptive threshold
method is applied to overcome the dynamic changes of illumination conditions
when
 Converting the image into binary. Connected component analysis technique
(CCAT) is used to detect candidate objects inside the unknown image. A scale-invariant
geometric relationship matrix is introduced to model the layout of
symbols in any LP that simplifies system adaptability when applied in different
 Countries. Moreover, two new crossover operators, based on sorting, are
introduced, which greatly improve the convergence speed of the system. Most
of the CCAT problems, such as touching or broken bodies, are minimized by
modifying the GA to perform partial match until reaching an acceptable fitness
 Value. The system is implemented using MATLAB and various image samples
are experimented with to verify the distinction of the proposed system.
Encouraging results with 98.4% overall accuracy are reported for two different
datasets having variability in orientation, scaling, plate location, illumination,
and complex
 Background. Examples of distorted plate images are successfully detected due
to the independency on the shape, color, or location of the plate.
 Index Terms-Genetic algorithms (GAs), image processing, image
representations, license plate detection, machine vision, road vehicle
identification, sorting crossover.

3. Proposed System
 In the projected system, the image are cleansed up before
process and when cleanup up or enhancing the image it'll
be fed into the system for Pattern recognition.
 Individual things are separated humor divided and later
the weather is scanned through our pattern recognition
system.
 After finding the individual object, our skilled system can
establish the actual object owner (In our case, vehicle
owner).
 The image could have extra information’s, our system can
undertake and discard the digressive information’s and
take a look at to retrieve the possible meaningful
information’s.

4. Existing System
 In the existing system, pictures the photographs were
mechanically recognized supported the individual
components within the images.
 Automatic comparison of the known objects with the
present trained objects can modify the user to
recognize the objects
 Amending the individual objects into a wholesome of
objects enable the system to identify the particular
owner from the database.
 Everything is machine-driven within the system

5. System Requirements
 Hardware Requirements:
System : Pentium IV 2.4 GHz.
Hard Disk : 80 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 1 GB or Above
 Software Requirements:
Operating system : Windows 7
Front End : Dotnet 4.0 (VS 2010)
Backend : SQL Server 2008 R2

6. Architecture Diagram