Car Number Plate Recognition

DECLARATION

I hereby declare that the results embodied in this dissertation entitled “CAR

NUMBER PLATE RECOGNITION” is carried out by me during the year 2008-09 in

partial fulfillment of the requirements of the award of degree, Bachelor of Engineering in

Electronics and Communication Engineering from M.V.S.R Engineering College,

affiliated to Osmania University, Hyderabad.

I have not been submitted the same to any other university or organization for

award of any other degree or diploma.

D. Keerthi K. Aruna Kumari B. Spandana

ACKNOWLEDGEMENTS

The satisfaction and euphoria that accompany the successful completion of any

task would be, but incomplete without the mention of the people who made it possible,

whose constant guidance and encouragement have crowned our efforts with success.

We express our sincere thanks and gratitude to our external guide

Mr. K. Rambabu Director of Laser systems (RESEARCH CENTRE IMARAT).

We are extremely indebted to our internal guide, Mr. Sudhir Dakey for his

valuable help, and would like to express our profound gratitude towards his for providing

all facilities to implement this project in the college and for his sagacious guidance

through out the project in the college and also through out our college period.

We would like express our deep felt gratitude to Dr. P.A. Sastry, Principal of

M.V.S.R Engineering College for his kind co-operation and valuable suggestions.

We would like to take this opportunity to express our heart-felt gratitude to our

H.O.D. Dr. B. Hari Kumar for providing the necessary infrastructure to complete this

project.

We also thank the staff of the ECE department who helped us morally for the

successful completion of this project.

RCI PROFILE

The Research Centre Imarat (RCI) was established by A.P.J. Abdul Kalam in

1988 on a campus 8 km from Defence Research and Development Laboratory [DRDL] at

Kanchanbagh, Hyderabad. The center's state-of-the-art facilities are dedicated to work in

advanced missile technologies.

The Research Center Imarat complex is primarily associated with research and

development of technologies for missiles, aircraft and other advanced weapons,

Development activities at RCI include the Light Combat Aircraft as well as the missile

programs. The research conducted at RCI in areas such as materials, electronics and

software provided the base on which an ambitious missile program could be successfully

constructed.

Work conducted at RCI also includes specialties such as automated test systems,

data acquisition, analysis, control electronics, digital signal processing, quality control of

high reliability electronic packages, gyro systems, environmental testing, and integration

of multistage aerospace vehicles. Activities range from software operations, navigational

checks, sensor interface and calibration, to environmental and integration check-outs.

Typical projects include development of a data acquisition system for surface to air

missile ground segment.

The RCI is one of the nodal laboratories that are involved in developing light-

weight composite material for the missiles developed by the Defence Research and

Development Organization. RCI's Composite Product and Development Centre (CPDC)

is also working on uses of high-tech material for social applications, such as light-weight

artificial limbs.

ABSTRACT

Car Number Plate Recognition System means to extract the number plate

characters in ASCII format from the image. The aim of the project is to provide traffic

management, control and other related applications

This system takes a vehicle image of any size breaks it into smaller image pieces.

These pieces are then analyzed to locate the exact location of number plate in the image.

Once the area of the number plate (its x and y coordinates) is found the plate is parsed to

extract the character from it. These characters are then given to the Optical Character

Recognition (OCR) module. OCR program recognizes those characters and converts

them to ASCII format.

The technique used in finding the exact location of number plate in the vehicle

image is signature technique. The plate thus obtained is given to the OCR module. The

goal of Optical Character Recognition is to classify optical patterns (often contained in a

digital image) corresponding to alphanumeric or other characters. The process of OCR

involves several steps including segmentation, feature extraction, and classification.

Templates are 2D correlated to extract the characters.

CAR NUMBER PLATE RECOGNITION

CONTENTS

LIST OF FIGURES iv

LIST OF TABLES vi

I. INTRODUCTION 1

1.1 Introduction 1

1.2 Aim Of The Project 2

1.3 History 3

1.4 Working 3

1.5 Algorithm 4

1.6 Conclusion 4

II. IMPLEMENTATION TOOL – MATLAB 5

2.1 Introduction 5

2.2 MATLAB 5

2.3 MATLAB Systems 6

2.4 Graphics and Visualization 9

2.5 Features 9

2.6 M files 11

2.7 Images in MATLAB 11

2.8 Working with Images in MATLAB 12

2.9 Graphical User Interface 12

2.10 GUI Working 13

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2.11 Defining User Interface Controls 14

2.12 Guide: An Overview 23

2.13 Guide Tools Summary 24

2.14 Conclusion 24

III. PROJECT DESCRIPTION 25

3.1 Introduction 25

3.2 Digital Image 25

3.3 Digital Image Processing 25

3.4 Key Steps Involved in the Project 26

3.5 Techniques Used In the Project 27

3.6 Signature Technique 27

3.7 Optical Character Recognition (OCR) 28

3.8 Template Matching 29

3.9 Conclusion 31

IV. WORKING DESCRIPTION 32

4.1 Introduction 32

4.2 Algorithm 32

4.3 Flowchart Depicting the Operations of the System 33

4.4 Flow of Function Calls in the program 34

4.5 MATLAB Code 35

4.6 GUI Code 46

ii

4.7 Conclusion 51

V. ADVANTAGES, DISADVATAGES AND APPLICATIONS 52

5.1 Advantages 52

5.2 Disadvantages 52

5.3 Applications 53

VI. RESULTS & CONCLUSION 57

6.1 Results 57

6.2 Conclusion 63

6.3 Future Advancements 63

APPENDIX A 64

BIBLIOGRAPHY 76

LIST OF FIGURES

2.1 MATLAB Development Environment 7

2.2 Simple GUI 13

2.3 Property Inspector 14

iii

2.4 Push Button 15

2.3 Push Button with Image 16

2.6 Slider 16

2.7 Radio Button 17

2.8 Check Box 18

2.9 Edit Text Box 19

2.10 Static Text 20

2.11 Pop-up Menu 20

2.12 Toggle Button 21

2.13 Axes 22

2.14 Layout Editor 24

3.1 Segmented Character Of The Number Plate 30

3.2 Template Image 30

3.3 Result Of Correlation 31

4.1 Flowchart Depicting The Operations Of The System 33

4.2 Flow Of Function calls In The Program 34

6.1 Input Image To Tmain 57

6.2 Binarised Image Of Input Image 58

6.3 Row wise Segmentation Of The Binarised Image 59

6.4 High Frequency Areas Of The Image 60

6.5 Number Plate From The Input Image 61

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6.6 Segmented Characters Of The Number Plate 62

A.1 Pixel Coordinate System 66

A.2 Spatial Coordinate System 67

A.3 Binary Image 69

A.4 Intensity Image 70

A.5 Indexed Image 71

A.6 RGB Image 72

LIST OF TABLES

8.1 Data Classes Supported By IPT Functions 68

8.2 Image Types And Data classes 73

8.3 Image Type conversion Functions 74

v

vi

CHAPTER I

INTRODUCTION

1.1 INTRODUCTION

“Car Number Plate Recognition” system uses Optical Character Recognition

(OCR) software to convert images of vehicle registration numbers into information for

real time or retrospective matching with law enforcement and other databases. This

system is an integrated hardware-software device that reads the vehicles number plate

and outputs the number plate number in ASCII - to some data processing system. This

system is also known as License Plate Recognition (LPR) and Automatic License Plate

Recognition (ALPR) systems.

It is believed that there are currently more than half a billion cars on the roads

worldwide. All those vehicles have their Vehicle Identification Number as their primary

identifier. The vehicle identification number is actually a license number which states a

legal license to participate in the public traffic. All vehicle world-wide should have its

license number - written on a license plate - mounted onto its body (at least at the back

side) and no vehicle without properly mounted, well visible and well readable license

plate should run on the roads.

The license number is the most important identification data a computer system

should treat when dealing with vehicles. If the data is already in the computer most of

these tasks are rather easy to be carried out.

Suppose a company's security manager would require a system that precisely tells

at every moment where the cars of the company are: in the garage or out on roads. By

registering every single drive-out from and drive-in to the garage, the system could

always tell which car is out and which is in. The key issue of this task is that the

registration of the movement of the vehicles should be done automatically by the system,

otherwise it would require manpower.

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Concisely, Car Number Plate Recognition is automation of data input, where data

equals the registration number of the vehicle. This system replaces, redeems the task of

manually typing the plate number of the bypassing vehicle into the computer system.

1.2 AIM OF THE PROJECT

The aim of the project is to develop an efficient Car Number Plate Recognition

System for necessary applications.

The scope of the project is broadly categorized into modules as follows:

Number Plate Recognition

Templates

Templates Management

1.3 HISTORY

The Car Number Plate Recognition System was invented in 1976 at the Police

Scientific Development Branch in the UK. Prototype systems were working by 1979 and

contracts were let to produce industrial systems, first at EMI Electronics then at

Computer Recognition Systems (CRS) in Wokingham, UK. Early trial systems were first

used in Britain in 1979 with trial units placed on the A1 road and the Dartford tunnel. To

minimize errors, license plate's character font, positioning and sizes were optimized for

the character recognition systems used. The first arrest due to a detected stolen car was

made in 1981.

Early CNPR systems were unable to read white or silver lettering on black

background, as permitted on UK vehicles built prior to 1973.

There are a number of other possible difficulties that the software must be able to

cope with. These include:

Poor image resolution, usually because the plate is too far away but sometimes

resulting from the use of a low-quality camera.

Blurry images, particularly motion blur

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Poor lighting and low contrast due to overexposure, reflection or shadows

An object obscuring (part of) the plate, quite often a tow bar, or dirt on the plate

A different font, popular for vanity plates (some countries do not allow such plates,

eliminating the problem)

1.4 WORKING

As the vehicle approaches the secured area, and starts the cycle by stepping over a

magnetic loop detector (which is the most popular vehicle sensor). The loop

detector senses the presence of the car.

FIGURE 1.1

Now the illumination is activated and the camera (shown in the left side of the

gate) takes pictures of the front or rear plates. The images of the vehicle include

the plate.

FIGURE 1.2

Our project (CNPR System) reads the pixel information of this vehicle image and

breaks it into smaller image pieces. These pieces are then analyzed to locate the 3

exact location of number plate in the image. Once the area of the number plate (its

x and y coordinates) is found the plate is parsed to extract the character from it.

These characters are then given to the OCR module. OCR program recognizes

those characters and coverts them to text format.

FIGURE 1.3

Once

the car number was obtained it can be used according to the application.

1.5 ALGORITHM

This system takes a vehicle image of any size.

Breaks it into smaller image pieces.

These pieces are then analyzed to locate the exact location of number plate in the

image.

Once the area of the number plate (its x and y coordinates) is found the plate is

parsed to extract the character from it.

These characters are then recognized and converted to ASCII text format.

1.6 CONCLUSION

There are frequent situations in which a system able to recognize registration

numbers. Our project deals with the identification of the characters from the image of the

car (containing number plate). The basic ideas of the project are discussed in brief.

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CHAPTER II

IMPLEMENTATION TOOL - MATLAB

2.1 INTRODUCTION

This chapter discusses the implementation software, MATLAB used in the

project. This project is also implemented in GUI (Graphical User Interface). This chapter

also discusses main features of GUI..

2.2 MATLAB

MATLAB is a high-performance language for technical computing. It integrates

computation, visualization, and programming in an easy-to-use environment where

problems and solutions are expressed in familiar mathematical notation. Typical uses

include:

•Math and computation

•Algorithm development

•Modeling, simulation, and prototyping

•Data analysis, exploration, and visualization

•Scientific and engineering graphics

•Application development, including graphical user interface building

MATLAB is an interactive system whose basic data element is an array that does

not require dimensioning. This allows you to solve many technical computing problems,

especially those with matrix and vector formulations, in a fraction of the time it would

take to write a program in a scalar noninteractive language such as C or FORTRAN. The

name MATLAB stands for Matrix Laboratory. MATLAB was originally written to

provide easy access to matrix software developed by the LINPACK and EISPACK

projects. Today, MATLAB uses software developed by the LAPACK and ARPACK

projects, which together represent the state-of-the-art in software for matrix computation.

5

MATLAB has evolved over a period of years with input from many users. In university

environments, it is the standard instructional tool for introductory and advanced courses

in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for

high-productivity research, development, and analysis.

MATLAB features a family of add-on application-specific solutions called

toolboxes. Very important to most users of MATLAB, toolboxes allow you to learn and

apply specialized technology. Toolboxes are comprehensive collections of MATLAB

functions (M-files) that extend the MATLAB environment to solve particular classes of

problems. Areas in which toolboxes are available include image processing, signal

processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and

many others.

2.3 MATLAB SYSTEM

The MATLAB system consists of five main parts:

Development Environment

MATLAB Mathematical Function Library

MATLAB Language

Handle Graphics

MATLAB Application Program Interface (API).

Development Environment

This is the set of tools and facilities that help you use MATLAB functions and

files. Many of these tools are graphical user interfaces. It includes the MATLAB desktop

and Command Window, a command history, and browsers for viewing help, the

workspace, files, and the search path.

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FIGURE 2.1 MATLAB Development Environment

MATLAB Mathematical Function Library

This is a vast collection of computational algorithms ranging from elementary

functions like sum, sine, cosine and complex arithmetic, to more sophisticated functions

like matrix inverse, matrix eigen values, Bessel functions and Fourier transforms.

7

MATLAB language

This is a high-level matrix/array language with control flow statements,

functions, data structures, input/output, and object-oriented programming features. It

allows both “programming in the small” to rapidly create quick and dirty throw-away

programs, and “programming in the large” to create complete large and complex

application programs.

Handle Graphics

This is the MATLAB graphics system. It includes high-level commands for two-

dimensional and three-dimensional data visualization, image processing, animation, and

presentation graphics. It also includes low-level commands that allow you to fully

customize the appearance of graphics as well as to build complete graphical user

interfaces on your MATLAB applications.

MATLAB Application Program Interface (API)

This is a library that allows you to write C and FORTRAN programs that interact

with MATLAB. It include facilities for calling routines from MATLAB (dynamic

linking), calling MATLAB as a computational engine, and for reading and writing MAT-

files.

Our Image processing laboratory based on MATLAB has the capability to perform the

following tasks:

1. Perform algebraic operations such as addition, subtraction, multiplication, and

division. These operations can be used to perform operations on images such as noise

reduction by using averages, movement detection, and algebraic masking.

2. Geometric transformations such as translation, rotation, and scaling

3. Space domain operations such as histogram modification (scaling, offset, amplitude

change) non linear point operations (absolute value, squaring, square root, log scale

compression, edge detection)

4. Binary image processing (thresholding, logic operations).

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5. Non linear image processing such as morphologic operators (opening, closing).

Structuring element choice. Dilation, erosion.

6. Frequency domain processing. Fourier transform, log compression.

7. Filtering by linear convolution. Filter design (low pass, high pass, band pass, band

reject.). Gaussian Filters. Linear restoration. White noise non linear filtering.

8. Digital Image coding and compression. Compression measures, losses compression,

entropy, optimal coding.

2.4 GRAPHICS & VISUALIZATION

MATLAB has extensive facilities for displaying vectors and matrices as graphs,

as well as annotating and printing these graphs. It includes high-level functions for two

dimensional and three-dimensional data visualization, image processing, animation, and

presentation graphics. It also includes low-level functions that allow you to fully

customize the appearance of graphics as well as to build complete graphical user

interfaces on your MATLAB applications.

2.5 FEATURES

Variables

A MATLAB variable is essentially a tag that you assign to a value while that

value remains in memory. Variables are defined with the assignment operator, '='.

For instance, x=17

defines a variable named x and assigns it a value of 17. Values can come from literal

values such as string constants or numeric immediates, or from the values of other

variables, or from the output of a function.

Matrices

Essentially, the only data objects in MATLAB are rectangular numerical matrices.

There are no restrictions placed on the dimensions of a matrix (except by system

9

resources), but special meaning is sometimes attached to 1*1 matrices (scalars) and

matrices with only one row or column (vectors). The memory required for the storage of

each matrix is automatically allocated by MATLAB.

The easiest way to enter a matrix into MATLAB is to provide an explicit list of

elements enclosed in square brackets [ ]. MATLAB uses the following conventions:

- A matrix element can be any valid MATLAB expression.

- Matrix elements are separated by blanks or commas.

- A semicolon or carriage return is used to indicate the end of a row.

Operators

Relational Operators Logical Operators

< Less than | And

<= Less than or equal to & or

> Greater ~ Not

>= Greater or equal to

== Equal to

~= Not equal to

Strings

String is an array of characters. Each character is represented internally by its

ASCII value.

This is an example of a string

str = 'I am learning MATLAB this semester.'

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2.6 M-FILES

We can create our own matrices using M-files, which are text files containing

MATLAB code. Use the MATLAB Editor or another text editor to create a file

containing the same statements you would type at the MATLAB command line. Save the

file under a name that ends in .m. For example, create a file containing these five lines.

A = [...

16.0 3.0 2.0 13.0

5.0 10.0 11.0 8.0

9.0 6.0 7.0 12.0

4.0 15.0 14.0 1.0];

Store the file under the name magik.m. Then the statement magik reads the file and

creates a variable A, containing our example matrix.

2.7 IMAGES IN MATLAB

The basic data structure in MATLAB is the array, an ordered set of real or

complex elements. This object is naturally suited to the representation of images, real-

valued ordered sets of color or intensity data. MATLAB stores most images as two-

dimensional arrays (i.e., matrices), in which each element of the matrix corresponds to a

single pixel in the displayed image. (Pixel is derived from picture element and usually

denotes a single dot on a computer display.) For example, an image composed of 200

rows and 300 columns of different colored dots would be stored in MATLAB as a 200-

by-300 matrix.

Some images, such as RGB, require a three-dimensional array, where the first

plane in the third dimension represents the red pixel intensities, the second plane

represents the green pixel intensities, and the third plane represents the blue pixel

intensities. This convention makes working with images in MATLAB similar to working

with any other type of matrix data, and makes the full power of MATLAB available for

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image processing applications. For example, you can select a single pixel from an image

matrix using normal matrix subscripting. I (2, 15) This command returns the value of the

pixel at row 2, column 15 of the image I.

2.8 WORKING WITH IMAGES IN MATLAB

Images are most commonly stored in MTALAB using the logical, unit 8, unit 16

and double data types (Detailed information about data types is given in Appendix). We

can perform many standard MATLAB array manipulations on uint8 and uint16 image

data, including

Basic arithmetic operations, such as addition, subtraction, and multiplication.

Indexing, including logical indexing.

Reshaping, reordering, and concatenating.

Reshaping, reordering, and concatenating.

Using relational operators.

Certain MATLAB functions, including the find, all, any, conv2, convn, fft2, fftn,

and sum functions, accept uint8 or uint16 data but return data in double-precision format.

2.9 GRAPHICAL USER INTERFACE (GUI)

A graphical user interface (GUI) is a graphical display that contains devices, or

components, that enable a user to perform interactive tasks. To perform these tasks, the

user of the GUI does not have to create a script or type commands at the command line.

Often, the user does not have to know the details of the task at hand. The GUI

components can be menus, toolbars, push buttons, radio buttons, list boxes, and sliders—

just to name a few. In MATLAB, a GUI can also display data in tabular form or as plots,

and can group related components. The following fig 2-1 illustrates a simple GUI. The

GUI contains

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- An axes component

- A pop-up menu listing three data sets that correspond to MATLAB functions:

peaks, membrane, and sinc

- A static text component to label the pop-up menu

- Three buttons that provide different kinds of plots: surface, mesh, and contour

FIGURE 2.2 Simple GUI

2.10 WORKING OF GUI

Each component, and the GUI itself, are associated with one or more user-written

routines known as callbacks. The execution of each callback is triggered by a particular

user action such as a button push, mouse click, selection of a menu item, or the cursor

passing over a component. We, as the creator of the GUI, provide these callbacks. In the

GUI described in “What Is a GUI?” the user selects a data set from the pop-up menu,

then clicks one of the plot type buttons. Clicking the button triggers the execution of a

callback that plots the selected data in the axes. This kind of programming is often

referred to as event-driven programming. The event in the example is a button click. In

event-driven programming, callback execution is asynchronous, controlled by events

13

external to the software. In the case of MATLAB GUIs, these events usually take the

form of user interactions with the GUI. The writer of a callback has no control over the

sequence of events that leads to its execution or, when the callback does execute, what

other callbacks might be running simultaneously.

2.11 DEFINING USER INTERFACE CONTROLS

User interface controls include push buttons, toggle buttons, sliders, radio buttons,

edit text controls, static text controls, pop-up menus, check boxes, and list boxes. To

define user interface controls, you must set certain properties. To do this:

Use the Property Inspector to modify the appropriate properties. Open the

Property Inspector by selecting Property Inspector from the View menu.

In the layout area, select the component you are defining.

FIGURE 2.3 Property Inspector

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Subsequent topics describe commonly used properties of user interface controls

and offer a simple example for each kind of control:

Push Button

To create a push button with label Button 1, as shown in this figure

FIGURE 2.4 Push Button

Specify the push button label by setting the String property to the desired label, in

this case, Button 1.

To display the character in a label, use two characters in the string. The words

remove, default, and factory (case sensitive) are reserved. To use one of these as a label,

prepend a backslash (\) to the string.

For example, \remove yields remove. The push button accommodates only a

single line of text. If we specify more than one line, only the first line is shown. If we

create a push button that is too narrow to accommodate the specified String, MATLAB

truncates the string with an ellipsis.

If we want to set the position or size of the component to an exact value, then

modify its Position property.

To add an image to a push button, assign the button’s CData property an m-by-n-

by-3 array of RGB values that defines a true color image. You must do this

programmatically in the opening function of the GUI M-file.

For example, the array img defines a 16-by-64-by-3 true color image using

random values between 0 and 1 (generated by rand).

img = rand (16, 64, 3);15

set (handles.pushbutton1,'CData', img);

where pushbutton1 is the push button’s Tag property.

FIGURE 2.5 Push Button with Image

Slider

To create a slider as shown in this figure

FIGURE 2.6 Slider

Specify the range of the slider by setting its Min property to the minimum value

of the slider and its Max property to the maximum value. The Min property must be less

than Max.

Specify the value indicated by the slider when it is created by setting the Value

property to the appropriate number. This number must be less than or equal to Max and

greater than or equal to Min. If we specify Value outside the specified range, the slider is

not displayed.

Control the amount the slider Value changes when a user clicks the arrow button

to produce a minimum step or the slider trough to produce a maximum step by setting the

Slider Step property. Specify Slider Step as a two-element vector, [min_step, max_step],

where each value is in the range [0, 1] to indicate a percentage of the range.

Radio Button

16

To create a radio button with label Indent nested functions, as shown in this

figure

FIGURE 2.7 Radio Button

Specify the radio button label by setting the String property to the desired label, in

this case, Indent nested functions.

To display the & character in a label, use two & characters in the string. The

words remove, default, and factory (case sensitive) are reserved.

To use one of these as a label, prepend a backslash (\) to the string. For example, \

remove yields remove.

The radio button accommodates only a single line of text. If you specify more

than one line, only the first line is shown. If we create a radio button that is too narrow to

accommodate the specified String, MATLAB truncates the string with an ellipsis.

Create the radio button with the button selected by setting its Value property to

the value of its Max property (default is 1). Set Value to Min (default is 0) to leave the

radio button unselected. Correspondingly, when the user selects the radio button,

MATLAB sets Value to Max. MATLAB sets Value to Min when the user deselects it.

Check Box

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To create a check box with label Display file extension that is initially checked,

as shown in this figure

FIGURE 2.8 Check Box

Specify the check box label by setting the String property to the desired label, in

this case, Display file extension.


words remove, default, and factory (case sensitive) are reserved. To use one of these as a

label, prepend a backslash (\) to the string. For example, \remove yields remove. The

check box accommodates only a single line of text. If we specify a component width that

is too small to accommodate the specified String, MATLAB truncates the string with an

ellipsis.

Create the check box with the box checked by setting the Value property to the

value of the Max property (default is 1). Set Value to Min (default is 0) to leave the box

unchecked. Correspondingly, when the user clicks the check box, MATLAB sets Value

to Max when the user checks the box and to Min when the user unchecks it.

Edit Text

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To create an edit text component that displays the initial text Enter your name

here, as shown in this figure

FIGURE 2.9 Edit Text Box

Specify the text to be displayed when the edit text component is created by setting

the String property to the desired string, in this case, Enter your name here.



label, prepend a backslash (\) to the string. For example, \remove yields remove.

To enable multiple-line input, specify the Max and Min properties so that their

difference is greater than 1. For example, Max = 2, Min = 0. Max default is 1, Min

default is 0. MATLAB wraps the string and adds a scroll bar if necessary.

If Max-Min is less than or equal to 1, the edit text component admits only a single

line of input. If you specify a component width that is too small to accommodate the

specified string, MATLAB displays only part of the string. The user can use the arrow

keys to move the cursor over the entire string.

Static Text19

To create a static text component with text Select a data set, as shown in this

figure

FIGURE 2.10 Static Text

Specify the text that appears in the component by setting the component String

property to the desired text, in this case Select a data set.

To display the & character in a list item, use two & characters in the string. The

words remove, default, and factory (case sensitive) are reserved.

To use one of these as a label, prepend a backslash (\) to the string. For example, \

remove yields remove. If our component is not wide enough to accommodate the

specified String, MATLAB wraps the string.

Pop-Up Menu

To create a pop-up menu (also known as a drop-down menu or combo box) with

items one, two, three, and four, as shown in this figure

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FIGURE 2.11 Pop-up Menu

To display the & character in a menu item, use two & characters in the string. The


label, prepend a backslash (\) to the string. For example, \remove yields remove. If the

width of the component is too small to accommodate one or more of the specified strings,

MATLAB truncates those strings with an ellipsis.

To select an item when the component is created, set Value to a scalar that

indicates the index of the selected list item, where 1 corresponds to the first item in the

list. If you set Value to 2, the menu looks like this when it is created.

Toggle Button

To create a toggle button with label Left/Right Tile, as shown in figure

FIGURE 2.12 Toggle Button

Specify the toggle button label by setting its String property to the desired label,

in this case, Left/Right Tile.



label, prepend a backslash (\) to the string. For example, \remove yields remove.

The toggle button accommodates only a single line of text. If you specify more

than one line, only the first line is shown. If we create a toggle button that is too narrow

to accommodate the specified String, MATLAB truncates the string with an ellipsis.

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Axes

To create an axes as shown in the following figure

FIGURE 2.13 Axes

Allow for tick marks to be placed outside the box that appears in the Layout

Editor. The axes above looks like this in the layout editor; placement allows space at the

left and bottom of the axes for tick marks. Functions that draw in the axes update the tick

marks appropriately.

Use the title, xlabel, ylabel, zlabel, and text functions in the GUI M-file to label

an axes component.

For example,

xlh = (axes_handle,'Years')

labels the X-axis as Years. The handle of the X-axis label is xlh. The words remove,

default, and factory (case sensitive) are reserved. To use one of these in component text,

prepend a backslash (\) to the string.

22

For example, \remove yields remove.

2.12 GUIDE: AN OVERVIEW

GUI Layout

Guide, the MATLAB graphical user interface development environment, provides

a set of tools for creating graphical user interfaces (GUIs). These tools simplify the

process of laying out and programming GUIs. Using the GUIDE Layout Editor, you can

populate a GUI by clicking and dragging GUI components—such as axes, panels,

buttons, text fields, sliders, and so on—into the layout area. We can also create menus

and context menus for the GUI. From the Layout Editor, you can size the GUI, modify

component look and feel, align components, set tab order, view a hierarchical list of the

component objects, and set GUI options.

GUI Programming

GUIDE automatically generates an M-file that controls how the GUI operates.

This M-file provides code to initialize the GUI and contains a framework for the GUI

callbacks—the routines that execute when a user interacts with a GUI component. Using

the M-file editor, you can add code to the callbacks to perform the functions you want.

23

2.13 GUIDE TOOLS SUMMARY

The GUIDE tools are available from the Layout Editor shown in the figure below.

The tools are called out in the figure and described briefly below.

.

FIGURE 2.14 Layout Editor

2.14 CONCLUSION

24

The project is implemented using the tool MATALAB. All the important features

of the MATALAB are discussed. MATLAB provides toolboxes for various fields. We

are implementing this project using Image Processing Toolbox. The detailed discussion

about the toolbox is given in Appendix.

CHAPTER III

PROJECT DESCRIPTION

3.1 INTRODUCTION

In the previous chapter, the tools used for implementing the project and their

details are discussed. In this chapter we discuss the basics of digital image processing. It

also discusses in detail the techniques used for implementing the project. Some important

notes regarding number plate recognition system are also explained. The technique used

is signature technique to find the location of number plate .Optical Character Recognition

is used for recognizing the characters to get the characters in ASCII text format.

3.2 DIGITAL IMAGE

Vision is the most advanced of our senses, so it is not surprising that images play

the single most important role in human perception. However, unlike humans, who are

limited to visual band of electromagnetic (EM) spectrum, imaging machines cover almost

the entire EM spectrum, ranging from gamma to radio waves. They can operate also on

images generated by sources that humans are not accustomed to associating with images.

These include ultrasound, electron microscopy, and computer-generated images.

3.1.1 Definition

An image may be defined as a two dimensional function, f(x, y), where x and y

are spatial coordinates and the amplitude of any pair of coordinates is called the intensity

or gray level of the image at that point. When x, y and the amplitude of the image are all

finite, discrete quantities, we call the image a Digital Image.

3.3 DIGITAL IMAGE PROCESSING (DIP)

25

The field of digital image processing refers to processing the digital images by

means of a digital computer. The digital image is composed of a finite number of

elements, each of which has a particular location and value. These elements are referred

to as picture elements, image elements, pels and pixels. Pixel is the term most widely

used to denote the elements of a digital image. There is no general agreement regarding

where image processing stops and other related areas, such as image analysis and

computer vision start. Sometimes a distinction is made by defining image processing as a

discipline in which both the input and output of a process are images.

One useful paradigm is to consider three types of computerized processes in this

continuum: low-, mid-, and high-level processes.

Low-level processes involve primitive operations such as preprocessing to reduce

noise, contrast enhancement and image sharpening. A low-level process is

characterized by the fact that both its inputs and outputs are images.

Mid-level processes on images involve tasks such as segmentation (partitioning

an image into regions or objects), description of those objects to reduce them to a

form suitable for computer processing, and classification (recognition) of

individual objects. A mid-level process is characterized by the fact that its inputs

generally are images, but its outputs are attributes extracted from those images

(e.g., edges, contours, and the identity of individual objects).

Higher-level processing involves “making sense” of an ensemble of recognized

objects, as in image analysis, and, at the far end of the continuum, performing the

cognitive functions normally associated with human vision.

3.4 KEY STEPS INVOLVED IN THE PROJECT

Car number plate recognition system has three main components in it.

1. Converting the Bitmap image to Binary image.

2. Breaking the image into smaller pieces of image which are high frequency areas

of the original image

3. Choosing the number plate from the image pieces returned by the above module,

and parsing the plate to extract out the character part.

26

4. Recognizing the characters in the image pieces

3.5 TECHNIQUES USED IN THE PROJECT

Signature technique: To detect the number plate from the image and parsing it to

extract the characters.

OCR by 2D Correlation: recognize the extracted characters and covert them to

text format.

3.6 SIGNATURE TECHNIQUE

Signature technique helps in locating high frequency areas. If the image is

binarised then most of the detail is lost from the image, leaving our area of interest more

prominent. Taking row wise or column wise signature of an image gives the information

about the less detail and more detail areas of the image. So it becomes easy to find out the

areas with high frequencies.

3.6.1 Finding Probable Number Plate in the Image

Once the image is binarised its row wise histogram (sum of white or black pixels

in each row) or signature is taken to find out which number of rows is showing ridges.

These ridges are basically high frequency areas and one of these ridges will definitely be

a number plate. A threshold value is used to indicate the starting and ending point of the

ridge. The best results were shown by taking the average of the minimum point and the

median of the row signature as the threshold.

Once the ridges in the row signature of the image are obtained column wise

signature of those row ridges is calculated. This will further refine the candidate image by

removing those columns from the row ridge which do not possess much detail. This is

27

done by choosing the ridges our of the column histogram of the ridge area in the row

histogram.

3.6.2 Recognizing Number Plate from the Candidate Images

The x and y coordinates of all the high frequency pieces which are the candidates

of number plate are known. As we can see that on the number plate there would be four

to ten characters. So each character will show a ridge in the row signature of the image

piece, secondly most of the information is lost because of binarising the image so only

number plate area will show maximum number of ridges. Now if we take the row wise

histogram of those binarised pieces we can see that number plate image shows more

number of ridges as compared to any other candidate image. So image with maximum

number of ridges in its row signature is chosen as the number plate.

3.6.3 Extraction of characters

Then the same signature technique is applied to extract the numbers from the

number plate image. The difference was in the threshold value. Because here we needed

to pick each ridge in the histogram therefore the minimum value of the histogram was

chosen as the thresholding value. And the reason is that all characters might not show

ridges with equal peak (highest point in the ridge). Or a character like ‘X’ might be

broken into two ridges. As it is obvious that the center of the character X will show very

small peak.

The extracted characters are then given to OCR module.

3.7 OPTICAL CHARACTER RECOGNITION (OCR)

3.7.1 What is OCR?

The goal of Optical Character Recognition (OCR) is to classify optical patterns

(often contained in a digital image) corresponding to alphanumeric or other characters.

The process of OCR involves several steps including segmentation, feature extraction,

and classification.

3.7.2 Character Recognition: OCR by 2D Correlation

Description of method

28

Given the chosen license plate and the coordinates that indicate where the

characters are, we begin the OCR process by 2D correlation. We correlate each character

with either the alphabet or the numeral templates then choose the value of each character

based on the result of the correlation. The first three characters on the standard License

Plates are alphabets; therefore, we correlate each one of them with the 26 alphabet

templates. The latter four characters on the standard License Plates are numerals;

therefore, we correlate each one of them with the 10 numeral templates. The result OCR

is chosen based on the maximum values of the correlation for each character. However,

we realized that certain characters are frequently confused. As a quick solution, we

implanted a scheme to display possible alternatives. Those characters that are identified

as easily misinterpreted are subjected to a correlation comparison with an array of other

characters that is historically known to be easily confused with the originals. If there is a

possibility that a letter or number can be confused with more than one character, the

characters are listed in the output in the order of decreasing likelihood. This likelihood is

based on the correlation values of the character with the various templates, where high

correlation denotes a good possibility.

However, this scheme only occurs if the given letter does not have a very high

correlation value (does not land above a nominal threshold).

3.7.3 Rationale

OCR by 2D correlation is the option that seems to strike the best balance between

performance and difficulty in implementation. Details can be observed in the character

isolation portion of the source code.

3.7.4. Possible problems/Weaknesses

OCR by 2D correlation is sensitive to the size of the license plate, which meant

bigger or smaller alphabets and numbers in the picture. The 2-d correlation was very

sensitive to this and frequently gave back wrong results due to different size license

plates.

3.8 TEMPLATE MATCHING

The steps for this process are: 29

1. Build a template for each of the letters to be recognized. A good first-

approximation for a template is to the intersection of all instances of that letter in

the number plate. However, more fine-tuning of this template must be done for

good performance.

2. Erode the original image using this template as structuring element. All 1 pixels in

the resulting image correspond to all matches found for the given template.

3. Find the objects in the original image corresponding to these 1 pixels (e.g., using

the function 'bwselect' in MATLAB).

Sample segmented character from the number plate which is to be recognized

Can be represented as f(x, y)

FIGURE 3.1 Segmented Character of the number plate

Template Image which is required to be recognized from number plate.

Can be represented as h(x, y)

30

FIGURE 3.2 Template Image

Result of Correlation of f(x, y) and h(x, y).

G(x, y) = f(x, y) * (h*(x, y)).

FIGURE 3.3 Result Of Correlation

3.9 CONCLUSION

The important techniques like signature, correlation and template matching are

explained. Signature technique is used for locate the high frequency areas in the image.

Correlation technique is used for identification of segmented characters from the number

plate.

31

CHAPTER IV

WORKING DESCRIPTION

4.1 INTRODUCTION

This chapter deals with algorithm which we have implemented for extraction of

characters from the image containing car number plate. Flowchart depicting the flow of

processes involving in the project and the function flow diagrams, and code with neat

comments.

4.2 ALGORITHM

This system takes a vehicle image of any size.

Breaks it into smaller image pieces.

These pieces are then analyzed to locate the exact location of number plate in the

image.

Once the area of the number plate (its x and y coordinates) is found the plate is

parsed to extract the character from it.

These characters are then recognized and converted to ASCII text format.

32

33

FIGURE 4.1

START

Take a vehicle image of any size

STOP

Parsing the number plate to extract characters

Converting the recognized characters to text format

Analyzing pieces to locate the number plate

Break image into

smaller segments

Recognition of characters

34

4.3 FLOWCHART DEPICTING THE OPERATIONS OF THE SYSTEM

4.4 FLOW OF FUNCTION CALLS IN THE PROGRAM

This flowchart shows the flow of function calls to the m files in the program starting with the main function ‘Tmain’

FIGURE 4.2

PlateNoBlackImBinLoadBmp

Tmain

CntPkperRowCntPk

OCRNoBlackImBin

TemplateCntPk CntPkperRow

CntPk

CntPk

35

4.5 MATLAB CODE

Tmain.m

function Tmain()

% Description about the function:

% Main function of the program.

clc; % Clears matlab command window

close all; % Closes figure windows

file = 'image2.bmp'; % Input image

x = LoadBmp(file); % Calling "LoadBmp" function

figure,colormap(gray(256)),image(x); % Displays input image in gray scale

y = ImBin(x,0); % Calling ImBin function

Sel = NoBlack(y,0); % Calling NoBlack function

noPlate = Plate(x,Sel,y); % Calling Plate function

LoadBmp.m

function x = LoadBmp(fileName)

%______________________________________________________________________

% Description about the function LoadBmp:

% Loads the input image, converts it into double data type.

%______________________________________________________________________

x= imread(fileName,'bmp'); % Reads image from the file specified by the string

%"fileName", which is of the format "bmp".

x = double(x); % Converts the datatype of x to double precision.

36

ImBin.m

function y=ImBin(x,InvFlg)

%______________________________________________________________________

% Descrption about the function ImBin:

% This functions coverts the image stored in "x" to binary image (black and white)

%______________________________________________________________________

temp = median(x); % Finds the median value of the pixcels in each column of "x"

% and returns it in a row vector ("temp")

med = median(temp); % Finds median of the row vector temp

y = gt(x,med); % Binary image is obtained

h = imhist(y);

sz = size(h); % Returns the sizes of each dimension of array "h" in a vector "sz"

if InvFlg == 1

if(h(end) < h(1))

y = 1 - y; % Inverts the image

end

else

y = 1 - y; % Inverts the image

figure,imshow(y); % Displays the binarised image in a window

end

NoBlack.m

function pkVlyCnt = NoBlack(img,CharFlg);

%______________________________________________________________________

% Descrption about the function NoBlack:

% This function takes binarised image as input and it returns the high

37

% frequency areas of the image (one of them will be a number plate)

%______________________________________________________________________

pkVlyCnt = zeros(1,4);

siz = size(img);

if(siz(2) >2)

rowHist = sum(img.'); % Signature for white pixels

rowHist = siz(2) - rowHist; % Signature for black pixels

rowmin2 = min(rowHist);

rowmax2 = median(rowHist);

binX = gt(rowHist,(rowmax2 + rowmin2)/2); % Keeping the sensativity.

pkValy = CntPk(binX); % Calling the function CntPk

pkVlyCnt = CntPkperRow(img, pkValy(2:end),CharFlg);

% Calling the function CntPkperRow

else

pkVlyCnt = 0;

end

CntPk.m

function cnt = CntPk(x)

%______________________________________________________________________

% Descrpition about the function CntPk

% This function returns the co-ordinates of the image which corresponds to

% high frequency areas and also the number of high frequency ares

%______________________________________________________________________

sz = size(x);

38

if(sz(2) > 2)

index = 1;

st = 1;

nd = sz(2);

prev = x(1);

cnt = prev;

for i = 2 : sz(2)

cur = x(i);

if((prev == 0) & (cur == 1)) % Enters in the beginning of the peak (high frequency)

st = i; % Reads the corresponding co-ordinate

end

if((prev == 1) & (cur == 0)) % Enters in the end of the peak (high frequency)

cnt = cnt + 1; % Updates the count

rig(index) = st; % Stores the starting co-ordinate of the peak

rig(index + 1) = i; % Stores the ending co-ordinate of the peak

index = index + 2;

end

prev = cur;

end

if(( i == sz(2)-1)&(st > rig(index-1)))

cnt = cnt + 1;

rig(index) = st;

rig(index + 1) = i;

index = index + 2;

end

39

if(cnt > 1)

cnt = cnt - 1;

cnt = [cnt rig]; % Places the number of peaks in the first position of array("cnt")

% and remaining positions with co-ordinates of the image

end

else

cnt = 0;

end

CntPkperRow.m

function Sel=cntPkperRow(img, x,CharFlg);

%______________________________________________________________________% Description about the function CntPkperRow:

% Depending on CharFlg

% This function senses the high frequency areas (column wise)in the co-ordinats given

% by CntPk.and gives them back to CntPk to locate the co-ordinates of them (OR)

% Once the number plate is found this gives the segmented characters of it.

%______________________________________________________________________

index = 1;

Sel = zeros(1,4);

saveIndex = 1;

isz = size(img);

sz = size(x);

i=1;

while(i < sz(2))

cnt1 = x(i);

cnt2 = x(i+1);

40

if(cnt2 - cnt1 > 3)

colHist = sum(img(cnt1:cnt2,:)); % Signature for white pixels(column)

colHist = isz(1) - colHist; % Signature for black pixels(column)

if(CharFlg == 0)

colmin2 = min(colHist);

colmax2 = median(colHist);

binY = gt(colHist, (colmax2 + colmin2)/2);

% Sensing the high frequency areas

else

binY = gt(colHist, min(colHist)+1);

% Sensing the high frequency ares (once the number plate is found)

end

%'cntPkperRow while next ->'

temp = cntPk(binY);

% Calling the function CntPk to find the co-ordinates of the high frequency areas

pkVlyCnt(index) = temp(1);

index = index + 1;

sztemp = size(temp);

for ind=1:temp(1)

if(sztemp(2) > 1)

st = 2 * ind;

en = (2 * ind) + 1;

41

if((temp(en) - temp(st)) > 5)

Sel(saveIndex,1:4) = [cnt1 cnt2 temp(st) temp(en)];

% Storing the co-ordinates of row and column high frequency ares

saveIndex = saveIndex + 1;

end

end

st = 0;

en = 0;

end

end

i = i + 2;

end

Plate.m

function noPlate = Plate(img,Sel,image)

%______________________________________________________________________

% Description about the function Plate:

%This function takes the high frequency ares of the image as inpput and

%finds the probable number plate and also extracts the charaters from it.

%these characters are then given to "OCR" module for identification

%______________________________________________________________________

szSel = size(Sel);

selPlate = zeros(2,4);

szPlate = size(selPlate);

figure;

for index = 1:szSel(1)

42

subplot(szSel(1),1,index),imshow(image(Sel(index,1):Sel(index,2),Sel(index,3):Sel(index,4)));

% Displays the high frquency ares of the image

end

for i=1:szSel(1)

P =imBin(img((Sel(i,1)):(Sel(i,2)),(Sel(i,3)):(Sel(i,4))),1);

charSel = noBlack(P,1);

szTemp = size(charSel);

if((szTemp(1)>1)&(szTemp(1) > szPlate(1)))

selPlate = charSel; %co-ordinates of the extracted characters from the image

binImg = P;

end

end

figure,imshow(binImg); %displays the probable number plate from the image

figure;

szSel = size(selPlate);

if(szSel(1) > 1)

for ind = 1:szSel(1)

subplot(szSel(1),1,ind),imshow(binImg(selPlate(ind,1):selPlate(ind,2),selPlate(ind,3):selPlate(ind,4)));

match = OCR(binImg(selPlate(ind,1):selPlate(ind,2),selPlate(ind,3):selPlate(ind,4)));

% Calling "OCR" function (passes the segmented characters to the OCR)

switch match

case 1

lnum(ind) = '3';

43

case 2

lnum(ind) = '5';

case 3

lnum(ind) = '6';

case 4

lnum(ind) = 'G';

case 5

lnum(ind) = 'H';

case 6

lnum(ind) = 'K';

case 7

lnum(ind) = 'W';

case 8

lnum(ind) = '8';

case 9

lnum(ind) = '1';

case 10

lnum(ind) = 'A';

case 11

lnum(ind) = 'D';

end

end

end

fid = fopen('text.txt', 'wt');

fprintf(fid,'%s\n',lnum);

44

% Displays characters of number plate in ASCII format in a text file

fclose(fid);

winopen('text.txt')

noPlate = selPlate;

OCR.m

function tempInd = OCR(img)

%______________________________________________________________________

% Desription about the function OCR:

% This function sends the segmented characters from the number plate and the templates

% to "Template" function, which returns the correlated results.From these results this

% finds the best match and passes the template number to "Plate" function.

%______________________________________________________________________

filename = ['3.bmp';'5.bmp';'6.bmp';'g.bmp';'h.bmp';'k.bmp';'w.bmp';'8.bmp';'1.bmp';'A.bmp';'D.bmp' ]; % templates from database

szf = size(filename);

tempInd = 0;

for i = 1:szf(1)

temp = loadBmp(filename(i,:)); % Reads the templates from "filename"

num = template(img,temp);

% Calling function "Template".Pases the segmented image and a template for matching

dstort(i) = num; % Stores the correlated results

end

match = 1;

sz = size(dstort);

match = max(dstort); % Stores the maximum correlated value

45

for i=1:sz(2)

if match == dstort(i)

tempInd = i;

% stores the template number which has the maximum correlated value

end

end

Template.m

function maximum=template(bw, a);

%______________________________________________________________________% Description about the function Template:

% This program inputs two images.

% 1) Segmented Image on which a character is required to be recognized

% 2) Image of character template which is required to be recognized on

% the given image.

% This outputs the correlated results to "template" function

%______________________________________________________________________

bw=bwmorph(bw,'clean'); %performs removal of isolated pixels from image.

bw=bwmorph(bw,'spur'); %performs removal of end points from image.

a=bwmorph(a,'clean'); %performs removal of isolated pixels from image.

a=bwmorph(a,'spur'); %performs removal of end points from image.

bw=imresize(bw,[42 32],'nearest');

% returns an image bw that is of size [42 32], using nearest-neighbor interpolation.

a=imresize(a,[42 32],'nearest');

selected = bw;

result=real(ifft2(fft2(selected,70,324) .* fft2(rot90(a,2),70,324)));

%correlation of the segmented image and the template

46

maximum=max(result(:));

% Stores the maximum result obtained from correlation.

4.6 GUI FIGURE CODE

function varargout = Project_gui(varargin)

% PROJECT_GUI M-file for Project_gui.fig

% PROJECT_GUI, by itself, creates a new PROJECT_GUI or raises the existing

% singleton*.

% H = PROJECT_GUI returns the handle to a new PROJECT_GUI or the handle to

% the existing singleton*.

% PROJECT_GUI('CALLBACK',hObject,eventData,handles,...) calls the local

% function named CALLBACK in PROJECT_GUI.M with the given input arguments

% PROJECT_GUI('Property','Value',...) creates a new PROJECT_GUI or raises the

% existing singleton*. Starting from the left, property value pairs are

% applied to the GUI before Project_gui_OpeningFunction gets called. An

% unrecognized property name or invalid value makes property application

% stop. All inputs are passed to Project_gui_OpeningFcn via varargin.

% GUI Options on GUIDE's Tools menu. Choose "GUI allows only one

% instance to run (singleton)".

% Edit the above text to modify the response to help Project_gui

% Last Modified by GUIDE v2.5 07-Apr-2009 19:59:37

% Begin initialization code - DO NOT EDIT

gui_Singleton = 1;

gui_State = struct('gui_Name', mfilename, ...

'gui_Singleton', gui_Singleton, ...

'gui_OpeningFcn', @Project_gui_OpeningFcn, ...

47

'gui_OutputFcn', @Project_gui_OutputFcn, ...

'gui_LayoutFcn', [] , ...

'gui_Callback', []);

if nargin && ischar(varargin{1})

gui_State.gui_Callback = str2func(varargin{1});

end

if nargout

[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});

else

gui_mainfcn(gui_State, varargin{:});

end

% End initialization code - DO NOT EDIT

% --- Executes just before Project_gui is made visible.

function Project_gui_OpeningFcn(hObject, eventdata, handles, varargin)

% This function has no output args, see OutputFcn.

% hObject handle to figure

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% varargin command line arguments to Project_gui (see VARARGIN)

% Choose default command line output for Project_gui

handles.output = hObject;

% Update handles structure

guidata(hObject, handles);

% UIWAIT makes Project_gui wait for user response (see UIRESUME)

% uiwait(handles.figure1);

48

% --- Outputs from this function are returned to the command line.

function varargout = Project_gui_OutputFcn(hObject, eventdata, handles)

% varargout cell array for returning output args (see VARARGOUT);

% hObject handle to figure



% Get default command line output from handles structure

varargout{1} = handles.output;

% --- Executes on selection change in popupmenu1.

function popupmenu1_Callback(hObject, eventdata, handles)

global file;

contents = get(hObject,'String');

a=contents{get(hObject,'Value')} ;

if(a=='Image1')

file=('Image1.bmp');

end

if(a=='Image2')

file=('Image2.bmp');

end

% hObject handle to popupmenu1 (see GCBO)



% Hints: contents = get(hObject,'String') returns popupmenu1 contents as cell array

% contents{get(hObject,'Value')} returns selected item from popupmenu1

% --- Executes during object creation, after setting all properties.

49

function popupmenu1_CreateFcn(hObject, eventdata, handles)

% hObject handle to popupmenu1 (see GCBO)


% handles empty - handles not created until after all CreateFcns called

% Hint: popupmenu controls usually have a white background on Windows.

% See ISPC and COMPUTER.

if ispc

set(hObject,'BackgroundColor','white');

else

set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));

end

% --- Executes on button press in pushbutton1.

function pushbutton1_Callback(hObject, eventdata, handles)

global file;

Tmain(file,1);

% hObject handle to pushbutton1 (see GCBO)





global file;

Tmain(file,2);




50



global file;

Tmain(file,3);






global file;

Tmain(file,4);






global file;

Tmain(file,5);






global file;

Tmain(file,6);

51






global file;

Tmain(file,7);



% handles structure with handles and user data

4.7 CONCLUSION

This chapter discussed flow of function calls in the MATLAB code. MATLAB

Code and GUI code with comments is also given. Tmain is the main function. ImBin

converts the image to binary. NoBlack gives the coordinates of the number plate in the

image. Plate gives the final output of characters of the number plate in ASCII format.

52

CHAPTER V

ADVANTAGES, DISADVANTAGES & APPLICATIONS

5.1 ADVANTAGES

Simple and easy to implement

Good performance

Translation-invariant

The CNPR system significant advantage is that the system can keep an image

record of the vehicle which is useful in order to fight crime and fraud ("an image is worth

a thousand words"). An additional camera can focus on the driver face and save the

image for security reasons. Additionally, this technology does not need any installation

per car (such as in all the other technologies that require a transmitter added on each car

or carried by the driver).

5.2 DISADVANTAGES

Requires considerable tweaking to find the right templates

Not scale or rotation invariant.

Performance deteriorates rapidly for incomplete or noisy data

53

5.3 APPLICATIONS

Traffic Congestion Management (differential pricing for use of roads in a

precinct)

Traffic Speed Restrictions

Access Control.

Tolling

Border Control

Offender Identification (unregistered, uninsured, stolen vehicles, fake number

plates)

Criminal Intelligence (location of terrorism suspects and 'persons of interest to the

police')

Generation of blacklists of vehicles seeking fuel or other products from petrol

stations

5.3.1 Traffic Congestion

Traffic congestion schemes, of increasing significance in urban centres where

there are concerns about pollution or the capacity of streets to accommodate a large

number of vehicles during peak hours, seek to exclude classes of motor vehicles (for

example cars owned by non-residents) or to allow entry of vehicles on a selective basis

(e.g. at a particular time or subject to a financial penalty). In essence most schemes

involve charging for use of public streets and have thus proved to be controversial,

although sometimes supported by residents of the particular precinct.

This system has been used in such schemes to identify what vehicles are

traversing a location, on the basis that the technology is less "invasive" than electronic

tagging systems that require users to gain a RFID tag for the particular driver or vehicle.

The registration database is typically linked to a billing system, with this system being

used to identify 'authorized' vehicles (which have usually paid for the privilege of using

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the local road network) and non-authorized vehicles, which are automatically issued with

a fine.

Given the speed of processing and sufficient cameras it is possible to develop highly

granular, time-specific and dynamic charging, e.g. different charges at various times of

the day, higher charges when pollution reaches particular levels.

5.3.2 Speed Restrictions

This system can also be used for automated enforcement of speed restrictions and

associated traffic codes, such as prohibitions on particular classes of vehicles (such as

those carrying explosives or chemical) using specific routes.

Speed restriction is mechanistic, with the this system 'logging' the vehicle at two

or more locations and determining whether transit between those points and times

breached speed limits. A billing database can then automatically issue a fine and/or alert

traffic police that a breach is underway.

5.3.3 Access Control

A gate automatically opens for authorized members in a secured area, thus

replacing or assisting the security guard. The events are logged on a database and could

be used to search the history of events.

5.3.4 Tolling

The car number is used to calculate the travel fee in a toll-road, or used to double-

check the ticket. In this installation, the plate is read when the vehicle enters the toll lane

and presents a pass card. The information of the vehicle is retrieved from the database

and compared against the pass information. In case of fraud the operator is notified.

5.3.5 Network Management

This is academic and official interest, although apparently few implementations,

in use of this system to identify how road networks are being used and thereby better

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model future traffic flows or underpin intelligent transport systems (e.g. dynamic signage

and traffic signals that reflect loads on parts of the network).

5.3.6 Offender Identification

Law enforcement officials appear to have been more enthusiastic about the scope

for using this system in ‘offender identification’.

That encompasses such things as identification of

unregistered vehicles, i.e. vehicles with an expired number plate (significant

because most regimes prohibit driving of an unregistered vehicle but do not

require that 'unused' vehicles have a current plate)

uninsured vehicles (primarily in regimes where it is mandatory to insure a vehicle

through a government-controlled insurer)

stolen vehicles (i.e. where the thief takes the vehicle but does not change the

number plates)

vehicles with cloned plates (e.g. the same plates are identified on separate

vehicles in different locations at much the same time) or with bogus numbers (e.g.

there is no match with the vehicle register)

Vehicles engaged in activity such as unauthorized use of bus-only lanes.

Such identification can be tied to automated issue of a fine or summons to appear

before a magistrate. It can also be used in real time to alert law enforcement personnel to

intercept the suspect vehicle, for example to find and pull over a car that has been

reported as stolen.

5.3.7 Criminal Intelligence

Police, customs and other law enforcement officials are reluctant to tightly

circumscribe use of this system. As the following pages note there appears to be

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considerable sharing of information by agencies and real time checking of plates against

multiple hotlists.

Such lists include vehicles –

Reported to have been involved in hit and run incidents, petrol thefts, drive by

shootings, burglaries and abductions

Reported as having been at the scene of a crime

Registered to suspected terrorists, family and associates

Rented using 'flagged' credit cards

Registered to suspected drug traffickers

Registered to suspected stalkers

In practice a wide range of people may be 'of interest to the police' (and their

peers); the major limit on real-time large scale ad hoc sorting based on this system is the

difficulty of linking different databases.

Blacklists and Whitelists

The preceding applications have primarily involved official ANPR (Automatic

Number Plate Recognition) systems, particularly those tied to one or more government

databases. It is important, however, to recognize that private ANPR schemes have been

established in many countries.

One Australian promoter for example has marketed a commercial ANPR network

to petrol stations, with individual stations capturing an image of the plate on each vehicle

entering those premises. The expectation is that if there is a 'drive off' (i.e. the driver

leaves the station without paying) the site manager will manually apply a 'flag' to the

corresponding license number on the station's database. If the vehicle returns to the site

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an alarm will be sounded by the system and the cashier will disable the pump. That

blacklist can be shared with other stations via periodic exchange of information over the

promoter's server, a "feature that prevents an offending vehicle stealing fuel from a

different site every time".

Other vendors are promoting use of ANPR as an access control mechanism for

entry to a building or campus, with the vehicle's number plate replacing a proximity card

or other identifier.

CHAPTER VI

RESULTS & CONCLUSIONS

This chapter deals with the results obtained at the various stages of the project.

And the final output of the project in ASCII format.

6.1 RESULTS

STEP 1

Photograph of the car which contains number plate portion taken by CCTV

camera.

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Size: 254x333

Bytes: Class: uint8 array

STEP 2

Binarised form of the above image, output of Imbin function. (If the image is binarised then most of the detail is lost from the image, leaving our area of interest more prominent.)

FIGURE 6.2 Binarised image of input image

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FIGURE 6.1 Input image to Tmain

STEP 3

High frequency areas of the image to find the probable location of the number plate (Using row wise histogram)

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FIGURE 6.3 Row wise segmentation of the Binarised Iimage

STEP 4

After column wise histogram (for the above obtained result)

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FIGURE 6.4 High frequency areas of the image (after column wise segmentation)

STEP 5

Number plate

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FIGURE 6.5 Number Plate from the Input Image ( Binarised Image)

STEP 6

Segmented characters from the above obtained number plate.

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FIGURE 6.6 Segmented characters of the number plate

OUTPUT

Output of the system in ASCII format

GHW356K

6.2 CONCLUSION

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As described in the project, this system provides characters of the car number

plate in ASCII format that can be used for various applications. This is a comprehensive

step providing traffic management, security, control and offender identification. This

technology is simple and does not need any installations on cars.

6.3 FUTURE ADVANCEMENTS

The software can be used along with camera to serve many applications and we can even make the system automatic

An additional camera can focus on the driver face and save the image for security reasons.

In order to reduce the execution time of the code this can be implemented using c/c++.

APPENDIX A

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IMAGE PROCESSING TOOLBOX

INTRODUCTION

The Image Processing Toolbox is a collection of functions that extend the

capability of the MATLAB numeric computing environment. The toolbox supports a

wide range of image processing operations, including

Spatial image transformations

Morphological operations

Neighborhood and block operations

Linear filtering and filter design

Transforms Image analysis and enhancement

Image registration

Deblurring

Region of interest operations

Many of the toolbox functions are MATLAB M-files, a series of MATLAB

statements that implement specialized image processing algorithms.

The capabilities of the Image Processing Toolbox can be extended by writing user

defined M-files, or by using the toolbox in combination with other toolboxes, such as the

Signal Processing Toolbox and the Wavelet Toolbox

IMAGES IN MATLAB

The basic data structure in MATLAB is the array, an ordered set of real or

complex elements. This object is naturally suited to the representation of images, real-

valued ordered sets of color or intensity data. MATLAB stores most images as two-

dimensional arrays (i.e., matrices), in which each element of the matrix corresponds to a

single pixel in the displayed image. (Pixel is derived from picture element and usually

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denotes a single dot on a computer display.) For example, an image composed of 200

rows and 300 columns of different colored dots would be stored in MATLAB as a 200-

by-300 matrix. Some images, such as RGB, require a three-dimensional array, where the

first plane in the third dimension represents the red pixel intensities, the second plane

represents the green pixel intensities, and the third plane represents the blue pixel

intensities. This convention makes working with images in MATLAB similar to working

with any other type of matrix data, and makes the full power of MATLAB available for

image processing applications.

For example, we can select a single pixel from an image matrix using normal

matrix subscripting. I (2, 15)

This command returns the value of the pixel at row 2, column 15 of the image I.

COORDINATE SYSTEMS

Locations in an image can be expressed in various coordinate systems, depending

on context. This section discusses the two main coordinate systems used in the Image

Processing Toolbox and the relationship between them. These two coordinate systems are

described in

Pixel Coordinates

Spatial Coordinates

Pixel Coordinates

Generally, the most convenient method for expressing locations in an image is to

use pixel coordinates. In this coordinate

system, the image is treated as a grid

of discrete elements, ordered from top

to bottom and left to right, as

illustrated by the following figure.

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FIGURE. A.1 Pixel Coordinate System

For pixel coordinates, the first component r (the row) increases downward,

while the second component c (the column) increases to the right. Pixel coordinates are

integer values and range between 1 and the length of the row or column. There is a one-

to-one correspondence between pixel coordinates and the coordinates MATLAB uses for

matrix subscripting. This correspondence makes the relationship between an image's data

matrix and the way the image is displayed easy to understand. For example, the data for

the pixel in the fifth row, second column is stored in the matrix element (5, 2).

Spatial Coordinates

In the pixel coordinate system, a pixel is treated as a discrete unit, uniquely

identified by a single coordinate pair, such as (5, 2). From this perspective, a location

such as (5.3,2.2) is not meaningful. At times, however, it is useful to think of a pixel as a

square patch. From this perspective, a location such as (5.3, 2.2) is meaningful, and is

distinct from (5, 2). In this spatial coordinate system, locations in an image are positions

on a plane, and they are described in terms of x and y (not r and c as in the pixel

coordinate system). The following figure illustrates the spatial coordinate system used for

images. Notice that y increases

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FIGURE A.2 Spatial Coordinate System

Differences between Pixels Coordinate System and Spatial Coordinate System

There are some important differences, however. In pixel coordinates, the upper

left corner of an image is (1, 1), while in spatial coordinates, this location by default is

(0.5, 0.5). This difference is due to the pixel coordinate system's being discrete, while the

spatial coordinate system is continuous. Also, the upper left corner is always (1, 1) in

pixel coordinates, but you can specify a nondefault origin for the spatial coordinate

system. See Using a Nondefault Spatial Coordinate System for more information.

STORAGE CLASSES SUPPORTED BY MATLAB AND IPT

Images are most commonly stored in MATLAB using the logical, uint8, uint16

and double data types. By default, MATLAB stores most data in arrays of class double.

The data in these arrays is stored as double-precision (64-bit) floating-point numbers. All

MATLAB functions work with these arrays.

For image processing, however, this data representation is not always ideal. The

number of pixels in an image can be very large. To reduce memory requirements,

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MATLAB supports storing image data in arrays as 8-bit or 16-bit unsigned integers, class

uint8 and uint16. These arrays require one eighth as much memory as double arrays and

can perform many standard MATLAB array manipulations.

The data classes listed in the below table are supported by most IPT functions.

Data Class Description

Double Double-precision, floating point numbers in the approximate range

-10^308 to 10 ^308 (8 bytes per element).

Unit8 Unsigned 8-bit integers in the range [0,255] (1 byte per element).

Unit16 Unsigned 16-bit integers in the range [0, 65535] (2 bytes per

element).

Unit32 Unsigned 32-bit integers in the range [0, 4294967295] (4 bytes per

element).

Int8 Signed 8-bit integers in the range [-128, 127] (1 byte per element).

Int16 Signed 16-bit integers in the range [-32768, 32767] (2 bytes per

element).

Int32 Signed 32-bit integers in the range [-2147483648, 2147483647] (4

bytes per element).

Single Single-precision, floating point numbers in the approximate range -

10^38 to 10 ^38 (4 bytes per element).

Char Characters (2 bytes per element)

Logical Values are 0 or 1 (1 byte per element)

TABLE A.1 Data Classes supported by IPT functions

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Certain MATLAB functions, including the find, all, any, conv2, convn, fft2, fftn,

and sum functions, accept uint8 or uint16 data but return data in double-precision format.

TYPES OF DIGITAL IMAGES

The Image Processing Toolbox supports four basic types of images:

Binary Images

Intensity Images

Indexed Images

RGB (True Colour) Images

Binary Images

In a binary image, each pixel assumes one of only two discrete values.

Essentially, these two values correspond to on and off. A binary image is stored as a

logical array of 0's (off pixels) and 1's (on pixels).

FIGURE A.3 Binary Image

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Intensity Images

An intensity image is a data matrix, I, whose values represent intensities within

some range. MATLAB stores an intensity image as a single matrix, with each element of

the matrix corresponding to one image pixel. The matrix can be of class double, uint8, or

uint16.While intensity images are rarely saved with a colormap, MATLAB uses a

colormap to display them. The elements in the intensity matrix represent various

intensities, or gray levels, where the intensity 0 usually represents black and the intensity

1, 255, or 65535 usually represents full intensity, or white.

FIGURE A.4 Intensity Image

Indexed Image

An indexed image consists of a data matrix, X, and a colormap matrix, map. The

data matrix can be of class uint8, uint16, or double. The colormap matrix is an m-by-3

array of class double containing floating-point values in the range [0,1]. Each row of map

specifies the red, green, and blue components of a single color. An indexed image uses

direct mapping of pixel values to colormap values. The color of each image pixel is

determined by using the corresponding value of X as an index into map. The value 1

points to the first row in map, the value 2 points to the second row, and so on.

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FIGURE A.5 Indexed Image

RGB (True Colour) images

An RGB image, sometimes referred to as a true-color image, is stored in

MATLAB as an m-by-n-by-3 data array that defines red, green, and blue color

components for each individual pixel. RGB images do not use a palette. The color of

each pixel is determined by the combination of the red, green, and blue intensities stored

in each color plane at the pixel's location. Graphics file formats store RGB images as 24-

bit images, where the red, green, and blue components are 8 bits each. This yields a

potential of 16 million colors. The precision with which a real-life image can be

replicated has led to the commonly used term true-color image. An RGB array can be of

class double, uint8, or uint16. In an RGB array of class double, each color component is a

value between 0 and 1. A pixel whose color components are (0, 0, 0) is displayed as

black, and a pixel whose color components are (1, 1, 1) is displayed as white. The three

color components for each pixel are stored along the third dimension of the data array.

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For example, the red, green, and blue color components of the pixel (10,5) are stored in

RGB(10,5,1), RGB(10,5,2), and RGB(10,5,3), respectively.

FIGURE A.6 RGB (True Colour) Image

Multiframe image arrays

For some applications, you might need to work with collections of images related

by time or view, such as magnetic resonance imaging (MRI) slices or movie frames. The

Image Processing Toolbox provides support for storing multiple images in the same

array. Each separate image is called a frame. If an array holds multiple frames, they are

concatenated along the fourth dimension. For example, an array with five 400-by-300

RGB images would be 400-by-300-by-3-by-5. A similar multiframe intensity or indexed

image would be 400-by-300-by-1-by-5. Use the cat command to store separate images in

one multiframe array. For example, if you have a group of images A1, A2, A3, A4, and

A5, you can store them in a single array using A = cat(4,A1,A2,A3,A4,A5)

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We can also extract frames from a multiframe image. For example, if you have a

multiframe image MULTI, this command extracts the third frame. FRM3 = MULTI (:,:,:,

3)

Note that, in a multiframe image array, each image must be the same size and

have the same number of planes. In a multiframe indexed image, each image must be the

same size and have the same number of planes. In a multiframe indexed image, each

image must also use the same colormap.

SUMMARY OF IMAGE TYPES AND DATA CLASSES

Image Type Data Class Interpretation

Binary logical Array of zeros (0) and ones (1)

Indexed double Array of integers in the range [1, p]. The associated colormap is a p-by-3 array of floating-point values in the range [0, 1]

uint8 or uint16 Array of integers in the range [0, p-1]. The associated colormap is a p-by-3 array of floating-point values in the range [0, 1].

Intensity double Array of floating-point values. The typical range of values is [0, 1]. The associated colormap, typically grayscale, is a p-by-3 array of floating-point values in the range [0, 1].

uint8 or uint16 Array of integers. The typical range of values is [0, 255] or [0, 65535]. The associated colormap, typically grayscale, is a p-by-3 array of floating-point values in the range [0, 1].

RGB (true-color)

double m-by-n-by-3 array of floating-point values in the range [0, 1]

uint8 or uint16 m-by-n-by-3 array of integers in the range [0, 255] or [0, 65535]75

TABLE A.2 Image Types and Numeric Classes

IMAGE TYPE CONVERSION FUNCTIONS

For certain operations, it is helpful to convert an image to a different image type

Function Description

dither Create a binary image from a grayscale intensity image by dithering; create an indexed image from an RGB image by dithering

gray2ind Create an indexed image from a grayscale intensity image

grayslice Create an indexed image from a grayscale intensity image by thresholding

im2bw Create a binary image from an intensity image, indexed image, or RGB image, based on a luminance threshold

ind2gray Create a grayscale intensity image from an indexed image

ind2rgb Create an RGB image from an indexed image

mat2gray Create a grayscale intensity image from data in a matrix, by scaling the data

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TABLE A.3 Image Type conversion Functions

IMAGE ARITHMETIC

Image arithmetic is the implementation of standard arithmetic operations, such as

addition, subtraction, multiplication, and division, on images. Image arithmetic has many

uses in image processing both as a preliminary step in more complex operations and by

itself. For example, image subtraction can be used to detect differences between two or

more images of the same scene or object. we can do image arithmetic using the

MATLAB arithmetic operators. The Image Processing Toolbox also includes a set of

functions that implement arithmetic operations for all numeric, nonsparse data types. The

toolbox arithmetic functions accept any numeric data type, including uint8, uint16, and

double, and return the result image in the same format. The functions perform the 77

operations in double precision, on an element-by-element basis, but do not convert

images to double-precision values in the MATLAB workspace. Overflow is handled

automatically. The functions truncate return values to fit the data type.

Image Arithmetic Truncation Rules

The results of integer arithmetic can easily overflow the data type allotted for

storage. For example, the maximum value you can store in uint8 data is 255. Arithmetic

operations can also result in fractional values, which cannot be represented using integer

arrays. The image arithmetic functions use these rules for integer arithmetic: Values that

exceed the range of the integer type are truncated to that range. Fractional values are

rounded. For example, if the data type is uint8, results greater than 255 (including Inf) are

set to 255.

BIBLIOGRAPHY

1. Automatic Number Plate Recognition – Universty of Trás-os-Montes e Alto Douro - Vila Real – Portugal.

2. CARINA - Software Product for Automatic Number Plate Recognition – Budapest – Hungary.

3. Real-time number plate reading, J. Bulas-Cruz , J. Barroso, A Rafaele E. L. dagless

4. http://www.mathworks.com/

5. Digital Image Processing Using MATLAB 2nd Edition By Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins

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Car Number Plate Recognition