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TRIBHUVAN UNIVERSITY INSTITUTE OF ENGINEERING PULCHOWK CAMPUS DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING CERTIFICATE OF APPROVAL The undersigned certify that they have read, and recommended to the Institute of Engineering for acceptance, a project report entitled "VLPD-R" submitted by Love Shankar Shrestha, Promisha Mishra, Ravi Bhagat and Tanka Bahadur Pun in partial fulfillment of the requirements for the Bachelor’s degree in Electronics & Computer Engineering. _________________________________________________ Supervisor, Dr. Sanjeeb Prasad Panday Lecturer, Department of Electronic and Computer Engineering _________________________________________________ Co-Supervisor, Mr. Anil Verma Lecturer, Department of Electronic and Computer Engineering __________________________________________________ Internal Examiner, Coordinator, Dr. AmanShakya Deputy Head, Department of Electronic and Computer Engineering I

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Page 1: COPYRIGHTflipkarma.com/media_dir/main_documents/Major_Project.docx · Web viewCo-Supervisor, Mr. Anil Verma Lecturer, Department of Electronic and Computer Engineering _____ Internal

TRIBHUVAN UNIVERSITY

INSTITUTE OF ENGINEERING

PULCHOWK CAMPUS

DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING

CERTIFICATE OF APPROVAL

The undersigned certify that they have read, and recommended to the Institute of

Engineering for acceptance, a project report entitled "VLPD-R" submitted by Love

Shankar Shrestha, Promisha Mishra, Ravi Bhagat and Tanka Bahadur Pun in partial

fulfillment of the requirements for the Bachelor’s degree in Electronics & Computer

Engineering.

_________________________________________________

Supervisor, Dr. Sanjeeb Prasad PandayLecturer,Department of Electronic and Computer Engineering

_________________________________________________

Co-Supervisor, Mr. Anil VermaLecturer,Department of Electronic and Computer Engineering

__________________________________________________

Internal Examiner, Coordinator, Dr. AmanShakya

Deputy Head,

Department of Electronic and Computer Engineering

------------------------------------------------------------------------------

External Examiner, Mr. Subhash Dhakal

Ministry of Science and Technology,

Nepal

DATE OF APPROVAL: 26.08.2013

I

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COPYRIGHT

The author has agreed that the Library, Department of Electronics and Computer

Engineering, Pulchowk Campus, Institute of Engineering may make this report freely

available for inspection. Moreover, the author has agreed that permission for extensive

copying of this project report for scholarly purpose may be granted by the supervisors

who supervised the project work recorded herein or, in their absence, by the Head of the

Department wherein the project report was done. It is understood that the recognition

will be given to the author of this report and to the Department of Electronics and

Computer Engineering, Pulchowk Campus, Institute of Engineering in any use of the

material of this project report. Copying or publication or the other use of this report for

financial gain without approval of to the Department of Electronics and Computer

Engineering, Pulchowk Campus, Institute of Engineering and author’s written

permission is prohibited.

Request for permission to copy or to make any other use of the material in this report in

whole or in part should be addressed to:

ArunTimalsina

Head

Department of Electronics and Computer Engineering

Pulchowk Campus, Institute of Engineering

Lalitpur, Kathmandu

Nepal

II

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ACKNOWLEDGEMENT

It is an immense pleasure for us to acknowledge the guidance, encouragement and

assistance received from several individuals during the project period. Our heart-felt

gratitude goes to our project supervisor, Dr. Sanjeeb Prasad Panday and our co-

supervisor Mr. Anil Verma who have inspired, encouraged and provided invaluable

advice to accomplish this project. We also would like to thank him for showing us some

example that related to the topic of our project.  We are equally indebted to Prof. Dr.

Arun Timalsina, Head of Department of Electronics and Computer Engineering for

providing us an opportunity and environment for the project.

Our words of appreciations are short of praising the guidance of Assistant Dean Dr.

Subarna Shakya, Professor of Department of Electronics and Computer Engineering. We

also wish our thankfulness to Dr. Aman Shakya, Deputy Head of Department of

Electronics and Computer Engineering and our B.E project coordinator.

We would like to convey our acknowledgement to Mr. Ashok Kumar Pant for guiding us

during the project session and giving us his invaluable time.

Finally, we would also like to offer our gratitude to all our teachers whose ideas were the

basis for our project research and finally we would like to thank all our friends who gave

us their suggestions, ideas and support for this project.

Love Shankar Shrestha (16219)

Promisha Mishra (16223)

Ravi Bhagat (16224)

Tanka Bahadur Pun (16243)

III

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ABSTRACT

This project deals with the development of an application which recognizes the Vehicle

License Plate (VLP) that can be used for traffic system, parking area and border

crossings in Nepal. The current project work uses Artificial Intelligence (AI), Machine

Vision, and Neural Network (NN) along with image processing to construct the Vehicle

License Plate Recognition (VLPR) system for Nepal.

Specifically the system first takes images of vehicle from camera and then localizes VLP

from the image. Once the VLP is detected, it is segmented into individual characters and

the characters are recognized. The focus is on the design of algorithms used for

extracting the license plate from the image, segmenting the characters of the plate and

identifying the individual characters.

Keywords:

Artificial Intelligence, Machine Vision, Neural Network, Image-processing, Optical Character Recognition

IV

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TABLE OF CONTENTS

LETTER OF APPROVAL …………………………………………………………….... I

COPYRIGHT.....................................................................................................................II

ACKNOWLEDGEMENT................................................................................................III

ABSTRACT.....................................................................................................................IV

TABLE OF CONTENTS...................................................................................................V

LIST OF FIGURES.......................................................................................................VIII

LIST OF TABLES............................................................................................................IX

LIST OF ABBREVIATIONS AND SYMBOLS..............................................................X

1. INTRODUCTION..........................................................................................................1

1.1. Background.........................................................................................................2

1.2. Problem Statement..............................................................................................2

1.3. Objectives...........................................................................................................3

1.3.1. General Objective....................................................................................3

1.3.2. Specific Objective....................................................................................3

1.4. Scope of Work....................................................................................................4

1.5. Organization of Report.......................................................................................5

2. LITERATURE REVIEW...............................................................................................6

2.1. Related Work......................................................................................................7

2.2. Feature of Nepali Vehicle License Plate............................................................8

2.3. Image Processing..............................................................................................12

2.3.1. Image Acquisition and Preprocessing....................................................12

2.3.2. Plate Localization...................................................................................12

2.3.3. Segmentation..........................................................................................14

2.4. Feature Extraction............................................................................................14

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2.5. Neural Network................................................................................................15

3. METHODOLOGY......................................................................................................17

3.1. Generic Description..........................................................................................18

3.2. System Design..................................................................................................19

3.3. Technical Description.......................................................................................24

3.3.1. Pre-processing and VLP Localization...................................................25

3.3.2. Thinning.................................................................................................30

3.3.3. Feature Extraction..................................................................................32

3.3.3.1. Fast Fourier Transform (FFT)................................................33

3.3.3.2. Density over Different Zones.................................................33

3.3.3.3. Area of Image.........................................................................34

3.3.3.4. Moment Invariants.................................................................34

3.4. Artificial Neural Network (ANN)....................................................................36

3.4.1. Multilayer Perceptron............................................................................37

3.4.2. Feedforward Back Propagation Neural Network...................................37

3.4.3. Backpropagation....................................................................................38

3.4.4. Training FFNet.......................................................................................39

3.5. Training Neural Network.................................................................................39

3.5.1. Supervised Learning..............................................................................40

3.5.2. Error Correction Learning......................................................................40

3.6. Validating Neural Networks.............................................................................40

4. IMPLEMENTATION..................................................................................................42

4.1. Global Thresholding.........................................................................................43

4.2. Region Based Segmentation (Horizontal and Vertical)...................................43

4.3. Back Propagation..............................................................................................44

5. RESULT AND DISCUSSION.....................................................................................46

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5.1 Result................................................................................................................47

5.2. Observation and Discussions............................................................................47

5.3. Output...............................................................................................................49

5. CONCLUSION AND FUTURE ENHANCEMENT...................................................51

6.1. Conclusion........................................................................................................52

6.2. Future Enhancement........................................................................................52

6. EPILOGUE...................................................................................................................53

7.1. References........................................................................................................54

7.2. Glossary............................................................................................................56

LIST OF FIGURES

Figure 2-1: Vehicle Identifier...........................................................................................10

Figure 2-2: VLP in 4:3 ratio..............................................................................................11

Figure 2-3: VLP in 4:1 ratio..............................................................................................11

Figure 3-1: Use Case Diagram for VLP Segmentation....................................................18

Figure 3-2: Use Case Diagram of Optical Character Recognition....................................19

Figure 3-3: Use Case Diagram for Main Program............................................................19

Figure 3-4: Level 0 DFD of the System............................................................................20

Figure 3-5: Level 1 DFD for Process "Preprocessing".....................................................21

Figure 3-6: Level 1 DFD of process "Training"...............................................................22

Figure 3-7: Level 1 DFD for Process "Recognition"........................................................23

Figure 3-8: Technical Description of the System.............................................................24

Figure 3-9: Horizontal Projection of VLPD.....................................................................29

Figure 3-10: Vertical Projection of the Image..................................................................30

Figure 3-11: First Sub-iteration........................................................................................31

Figure 3-12: Second Sub-iteration....................................................................................32

Figure 3-13: Original pattern and Skeleton as a result of Zhang-Suen thinning

algorithm...........................................................................................................................32VII

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Figure 3-14: Feed Forward Multilayer Perceptron...........................................................37

Figure 3-15: Operation on Layer's Node..........................................................................37

Figure 5-1: Accuracy Rate of different stages of the system............................................48

Figure 5-2: Recognition accuracy of individual character................................................49

Figure 5-3: Input Image for VLPR System.......................................................................49

Figure 5-4: VLP Localization...........................................................................................49

Figure 5-5: VLP Vertical Segmentation...........................................................................50

Figure 5-6: VLP Horizontal Segmentation.......................................................................50

VIII

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LIST OF TABLES

Table 2-1: Major Categories of Vehicle.............................................................................9

Table 5-1: Accuracy rate corresponding to different stages.............................................47

Table 5-2: Recognition Result of individual character.....................................................48

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LIST OF ABBREVIATIONS AND SYMBOLS

Φ Activation Function

δ Delta as error

AI Artificial Intelligence

ANN Artificial Neural Network

ART Adaptive Resonance Theory

DOCR Devanagari Optical Character Recognition

DFD Data Flow Diagram

FFT Fast Fourier Transform

FFNet Feedforward Neural Network

INGO International Non-governmental Organization

ITS Intelligent Transport System

LL Letter Letter

MLP Multilayer Perceptron

NGO Non-governmental Organization

NN Neural Network

NN Number Number

NNNN Number NumberNumberNumber

OCR Optical Character Recognition

VLP Vehicle License Plate

VLPD Vehicle License Plate Detection

VLPR Vehicle License Plate Recognition

VLPD-R Vehicle License Plate Detection & Recognition

VMTR Vehicle and Transport Management Rule

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1. INTRODUCTION

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1.1. Background

VLPR has been intensively studied in many countries [1]. Due to variation in license

plates currently in practice, the requirement of an automatic license plate recognition

system is different for each country. This project is laser focused for the development of

license plate localization and recognition system for vehicles in Nepal. This system is

developed based on digital images manipulation and can be easily applied to commercial

purpose like car park systems for the use of documenting access of parking services,

secure usage of parking houses and also to prevent car theft issues and many more.

In the current era of information technology, the use of automatics and intelligent

systems is becoming more and more widespread. The Intelligent Transport System (ITS)

technology has gotten so much attention that many systems are being developed and

applied all over the world. VLP recognition has turned out to be an important research

issue. VLP recognition has significant role in traffic monitoring system including

controlling the traffic volume, ticketing vehicle without human control, vehicle tracking,

and so on. In some countries, VLPR systems installed on country borders automatically

detect and monitor border crossings. Each vehicle can be registered in a central database

and compared to a black list of stolen vehicles.

1.2. Problem Statement

In most of the countries, the attributes of the vehicle license plates are strictly

maintained. For example, the size of the plate, color of the plate, font face/ size/ color of

each character, spacing between subsequent characters, the number of lines in VLP,

script etc. are maintained very specifically. However, in Nepal, the VLP are not yet

standardized especially in size of plate and font of the characters, which makes the

system less accurate. Only the numeric letter “5 “ is written in more than three styles,

making localization and subsequent recognition of vehicle number plates extremely

difficult in this condition.

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The problem of vehicle number plate recognition is interestingly difficult one. These

tasks become more sophisticated when dealing with plate images inclined in various

angles and with noise. Because this problem is usually used in real-time systems, it

requires not only accuracy but also fast processing. The most vital and most difficult part

of any vehicle number plate recognition system is the detection and extraction of the

vehicle number plate, which directly affects the systems overall accuracy. The presence

of noise, blurring in the image, uneven illumination, dim light and foggy condition make

the task even more difficult. Localization of the VLP is also a problem due to distance

between the camera and the vehicle .Sometimes it also becomes difficult due to angular

image. This problem leads to inaccuracy in further steps [2].

Next problem in VLPR system is recognition of the characters. In Devanagari script "5"

is written in more than 5 styles. Similar is the case with "8"and "9". Lack of

standardization in Devanagari script is the cause of this problem.

1.3. Objectives

The core objective of this project is to make the vehicle number plate recognition system

automatic so that it will help the traffic and other aspect of the national security system.

There are mainly two types of objective of the this project namely,

1.3.1. General Objective

The general objective of this project is to recognize the number of the different types of

vehicle of Nepal such as government owned, Non-governmental Organization (NGO),

International Non-governmental Organization (INGO), public and private. As we know

the color of number plate of different sector are different and this will be the core thing

that help us to recognize the number plate easily and efficiently.

1.3.2. Specific Objective

The specific objectives of the project are as follows: Page | 3

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I) Detection of the number plate.

II) Recognition of the number of vehicle using the Devanagari Optical Character

Recognition.

1.4. Scope of Work

The scope of this project is to build an automatic system that can recognize the vehicle

by taking the image of the vehicle with VLP as input and obtaining the vehicle

registration.

The main feature of the system is, it can separate the VLP from the image and obtain the

character in the Devanagari. Recognition of Devanagari is always a difficult task and the

worst scenario is performing such task in the error prone surrounding.

VLPR system plays a major role in monitoring traffic rules and maintaining law

enforcement on public roads. This area is challenging because it requires an integration

of many areas in computer science, which include Object detection (plate localization)

and Character recognition. There are many scope of such recognition systems, some of

the examples where system fits are discussed below.

Traffic Systems: VLPR systems can be used for traffic systems to recognize

the number plate of the vehicle and store it in database. From which the wanted

or stolen vehicles can be searched easily as well as the density of running vehicle

in an area can be easily taken.

Parking: VLPR system can be used for parking places to keep the record of the

vehicles. The VLPR system can be used to automatically enter pre-paid members

and calculate parking fee for non-members (by comparing the exit and entry

times) by using some more technology along with VLPR.

Border Crossings: VLPR system can be used to monitor the border crossings

for keeping the track of vehicles which exits of the country. Each vehicle

information can be registered into a central database and can be linked to

additional information.

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1.5. Organization of Report

Chapter 1: It deals with the general introduction of the project, system, problems, scope

and organization of the report.

Chapter 2: It occupies the literature review related to the project system. It includes the

features of Nepali VLP, Devanagari script, image processing, neural network

and optical character recognition.

Chapter 3: It describes the process and methodology applied on the raw image, overall

system view and technical description of the system. It presents the system

diagram, data flow diagram and data model on which the system is built. It

also explains the interaction among the different component of the system.

Chapter 4: It focuses on the implementation model and application overview of the

system describing the algorithm followed during the system design.

Chapter 5: It explains the results and discussions of the project. It also shows the error

calculation and accuracy rates of recognizing the VLP and registration

number in it.

Chapter 6: It explains the conclusion and future enhancement of the system.

Chapter 7: It deals with the epilogue part of the report. It contains the glossary,

references and the appendix part of the report.

2.

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LITERATURE REVIEW

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2.1. Related Work

In this section focus is on the related work that has been done previously by several

researchers. In literature we can find many methods for license plate detection and

recognition system. The major drawback is that how long it will take to compute and

recognize the license plates. This is critical and most needed when it is applied to real

time applications. However, there is always a trade-off between computational time and

performance rate. In order to achieve an accurate result and increase the performance of

the system more computational time is required.

The problem of automatic VLP recognition has been studied since 1990s. The first

approach was based on characteristics of boundary lines [1, 3]. The input image was first

processed to enrich boundary lines’ information by some algorithms such as the gradient

filter, and resulted in an edging image. This image was binarized and then processed by

certain algorithms, such as Hough transform, to detect lines. Eventually, couples of 2-

parallel lines were considered as a plate-candidate.

Another approach was morphology-based [4, 5, 6]. This approach focuses on some

properties of plate images such as their brightness, symmetry, angles, etc. Due to these

properties, this method can detect the similar properties in a certain image and locate the

position of license plate regions. The third approach was texture-based. In this approach,

a VLP was considered as an object with different textures and frames [1, 7]. The texture

window frames of different sizes were used to detect plate-candidates. Each candidate

was passed to a classifier to confirm whether it is a plate or not. This approach was

commonly used in finding text in images tasks. In addition, there have been a number of

other methods relating to this problem focusing on detecting VLP in video data (objects

appear in a chain of sequent images).

The fourth approach was based on statistical properties of text [7]. In this approach, text

regions were discovered using statistical properties of text like the variance of gray level,

number of edges, edge densities in the region, etc. This approach was commonly used in

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finding text in images, and could well be used for discovering and designating candidate

number plate areas as they include alphabets and numerals [8].

In addition, there have been a number of other methods relating to this problem focusing

on detecting VLP using AI and genetic algorithms [1, 9]. These systems used edge

detection and edge statistics and then artificial intelligence techniques to detect the

location of the number plate-designate area. All of the systems discussed above have

some kind of limitations for example they are plate size dependent, color dependent,

work only in certain conditions or environment like indoor images etc.

2.2. Feature of Nepali Vehicle License Plate

License plate is the unique identification number provide to each vehicle. Its registration

number is binded with the chasis number of the vehicle. The VNP's number is issued by

the zonal-level Transport Management Office, a government agency under the

Department of Transport Management [10]. The vehicle number plates are placed in the

front as well as back of the vehicle. The plates are required to be either in Devanagari or

Latin script. In practice, the registration plates of Nepal are bilingual. As per the latest

guidelines issued by the Traffic Police Division, the plate must not be reflective and

digitally printed [10].

The vehicle are provided with 6 major categories and 4 identifier and 2 physical form.

For the purpose of vehicle registration Vehicle & Transport Management Act, 2049

(1992) and Vehicle & Transport Management Rule, 2054 (1997) of Nepal, classifies

vehicles into the following 5 main categories on the basis of size and capacity:

Heavy and medium-sized vehicle: This includes bus, truck, dozer, dumper,

loader, crane, Fire engine, tanker, roller, pick-up, van, mini bus, mini truck,

minivan etc. having the capacity to carry more than 14 people (for passenger

vehicle) or more than 4 tons (for cargo vehicle).

Light vehicle: This includes car, jeep, van, pick-up, micro bus, etc. having the

capacity to carry less than 24 people or less than 4 tons.

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Two-wheeler: This includes vehicle having two wheels like motor cycle, scooter

etc.

Tractor and power-trailer:

Three-wheeler: This includes vehicle having three wheels like tempo, auto-

rickshaw etc.

The above mentioned each categories are further divided into 5 sub categories on the

basis of ownership and service-type which are as follows:

Table 2-1: Major Categories of Vehicle

Type of vehicle Heavy size Middle size Motorcycle, scooter

Government ग झ बPrivate क च प, तLocal ख ज थTourist य यGovernment Organization/

Institutionघ ञ

Diplomatic सि� डी सि� डी सि� डीConstitutional झ

Private vehicle: The vehicles which are for entirely personal purpose and uses a

red license plate with the letters written in white.

Public vehicle

Government vehicle: The vehicles owned by the government agencies and

constitutional bodies such as ministries, departments, directorates, along with

police, military etc. falls under this category which uses white plate with the

letters written in red.

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1 2 3

4

National Corporation vehicle: The vehicles which are registered under the name

of public corporations fully or partially owned by the government falls under this

category. These vehicles uses yellow plate with the letters written in blue.

Tourist vehicle

The vehicles are provide with the letter as shown in the Table 2-1: Major categories of

the vehicle so that the visual distinction can be made. Every development region in

Nepal follows the same trend for vehicle classification except the region has own

identifier described below.

The 4 identifiers are:

The license plate of Nepal is more detailed in comparison with other countries. The

current license plate format for vehicles in Nepal consists of 4 parts composed of letters

and digits in the LL NN LL NNNN format:

Figure 2-1: Vehicle Identifier

The identifier of the vehicle shown in Figure 2-1 is described as:

The first part indicate the zone code, signifying the zone in which the vehicle is

registered.

The second part is the set number which is prefixed when the four digit number runs

out from the last part.

The third part indicate the type of vehicle like private, public, governmental, national

corporation, tourist etc. as well as the class of vehicle like two-wheeler, light vehicle,

heavy and medium-sized vehicle etc.

The last part signifies four digits running in sequence.

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The color of the background and foreground represent the owned type of vehicle like

public, private, government, diplomatic vehicle, etc.

All 14 zones of Nepal have their own abbreviated code for reference purpose. These

codes are normally single letter in Nepali and two letters (sometimes three letters also,

but the third letter 'a' can be omitted) in English which are shown in the Figure 2-1 [4].

The two physical form of VLP are:

The physical standard of the VLP is determined by Ministry of Infrastructure and

Transport. As per the Vehicle and Transport Management Rule (VTMR), the two

physical form are shown in the Figure 2-2 & Figure 2-3.

I. The VLP present in the 4:3 ratio.

Figure 2-2: VLP in 4:3 ratio

II. VLP present in 4:1 ratio

Figure 2-3: VLP in 4:1 ratio

According the VMTR, the character and number are written inside the number plate by

leaving ½ inch of space around the border for heavy and middle size four wheeler as for

two wheeler bikes, scooter the space are made at ¼ inch. The distance between the

number and the character must be ¼ inch and the distance between upper line and lower

line of character must be ½ inch [10].Page | 11

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2.3. Image Processing

2.3.1. Image Acquisition and Preprocessing

We have image file as problem domain. Image Acquisition is the first step in a VLPR

system and there are a number of ways to acquire images, the current literature discusses

different image acquisition methods used by various authors. Yan et. al. used an image

acquisition card that converts video signals to digital images based on some hardware-based

image preprocessing [6]. Naito et. al. developed a sensing system, which uses two CCDs

(Charge Coupled Devices) and a prism to split an incident ray into two lights with different

intensities [7, 8 9]. To do so requires the imaging sensor and capability to digitalize the

signal produced by the sensor. After digital image has been obtained, next step is to deal

with pre-processing of the image. The main purpose of the pre-processing is to increase

the efficiency of other processes. Pre-processing deals with the processes like image

enhancement, noise reduction, histogram equalization, edge detection, binarization of the

image.

2.3.2. Plate Localization

The next step is characterized as vehicle license plate (VLP) localization. VLP

localization is concerned with the finding the position of license plate in the captured

image. It is also known as license plate detection. The main idea of VLP localization is

to extract the license plate from whole image which is used in next step.

It is the most important phase in a VLPR system. This section discusses some of the

previous work done during the extraction phase. Hontani et. al. proposed a method for

extracting characters without prior knowledge of their position and size in the image [11].

The technique is based on scale shape analysis, which in turn is based on the assumption

that, characters have line-type shapes locally and blob-type shapes globally. In the scale

shape analysis, Gaussian filters at various scales blur the given image and larger size

shapes appear at larger scales. To detect these scales the idea of principal curvature plane Page | 12

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is introduced. By means of normalized principal curvatures, characteristic points are

extracted from the scale space x-y-t. The position (x, y) indicates the position of the

figure and the scale t indicates the inherent characteristic size of corresponding figures.

All these characteristic points enable the extraction of the figure from the given image

that has line-type shapes locally and blob-type shapes globally. Kim et. al. [12] used two

Neural Network-based filters and a post processor to combine two filtered images in

order to locate the license plates.

The two Neural Networks used are vertical and horizontal filters, which examine small

windows of vertical and horizontal cross sections of an image and decide whether each

window contains a license plate. Cross-sections have sufficient information for

distinguishing a plate from the background. Lee et. al. [13] and Park et. al. [14] devised a

method to extract Korean license plate depending on the color of the plate. A Korean

license plate is composed of two different colors, one for characters and other for

background and depending on this they are divided into three categories. In this method a

neural network is used for extracting color of a pixel by HLS (Hue, Lightness and

Saturation) values of eight neighboring pixels and a node of maximum value is chosen as

a representative color. After every pixel of input image is converted into one of the four

groups, horizontal and vertical histogram of white, red and green (i.e. Korean plates

contains white, red and green colors) are calculated to extract a plate region. To select a

probable plate region horizontal to vertical ratio of plate is used. Dong et. al [15] presented

histogram based approach for the extraction phase. Kim G. M [16] used Hough transform

for the extraction of the license plate. The algorithm behind the method consists of five

steps. The first step is to threshold the 14 gray scale source image, which leads to a

binary image. Then in the second stage the resulting image is passed through two parallel

sequences, in order to extract horizontal and vertical line segments respectively. The

result is an image with edges highlighted. In the third step the resultant image is then

used as input to the Hough transform, this produces a list of lines in the form of

accumulator cells. In fourth step, the above cells are then analyzed and line segments are

computed. Finally the list of horizontal and vertical line segments is combined and any

rectangular regions matching the dimensions of a license plate are kept as candidate

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regions. The disadvantage is that, this method requires huge memory and is

computationally expensive.

2.3.3. Segmentation

The next step is characterized as segmentation of license plate. Segmentation carried out

in two steps. The first one deals with separation of license plate in two consecutive rows

of two-row license plate such that each row consists of series of Devanagari numbers

with the application of horizontal projection. The second step is separation of each

character of the license plate. After that another process comes into account that is

enhancement of segmentation. The segmentation of plate contains beside the characters

also undesirable such as dots and stretches as well as redundant spaces on the sides of

characters. There is need to deals with these problems in segmentation.

Many different approaches have been proposed in the literature and some of them are as

follows, Nieuwoudt et. al. [17] used region growing for segmentation of characters. The basic

idea behind region growing is to identify one or more criteria that are characteristic for the

desired region. After establishing the criteria, the image is searched for any pixels that fulfill

the requirements. Whenever such a pixel is encountered, its neighbors are checked, and if

any of the neighbors also match the criteria, both the pixels are considered as belonging to

the same region. Morel et. al. [18] used partial differential equations (PDE) based technique;

Neural network and fuzzy logic were adopted in for segmentation into individual characters.

2.4. Feature Extraction

The purpose of feature extraction is the measurement of those attributes of patterns that

are most pertinent to a given classification task. The task of the human expert is to select

or invent features that allow effective and efficient recognition of patterns. All images

are down sampled before being used. This prevents the neural network from being

confused by size and position. By down sampling the image down to a consistent size, it

will not matter how large the letter, as the down sampled image will always remain a

consistent size. Down-sampling involves taking the image from a larger resolution to a

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24*24resolution. To see how to reduce an image to 24*24, think of an imaginary grid

being drawn over top of the high resolution image. This divides the image into

quadrants, 24 across and 24 down. If any pixel on a region is filled, then the

corresponding pixel in the 24*24 down sampled image is also filled it. The skeleton of

the image may contain several numbers of pixels. So the image is down sampled to 576

pixels of 24*24 grids. These 576 pixels values represent the pattern of the image.

In the literature of this section, a lot of work has been done for feature extraction of the

segmented image of the number plate. We can have many different features that can be

extracted from segmented character images [19, 20, 21, 22].

There are three major categories of feature extraction techniques:

o Geometrical and Topological Features: Extracting and Counting Topological

Structures, Geometrical Properties, Coding, Graphs, Trees, Strokes, Chain Codes etc.

o Statistical Features: Zoning, Crossing and Distances, Projections, Distribution

measures, etc.

o Global Transformation and Series Expansion Features: Fourier Transform, Cosine

o Transform, wavelets, Moments, Karhuen-Loeve Expansion, etc.

2.5. Neural Network

Computers can perform many operations considerably faster than a human being. Yet

there are many tasks where the computer falls considerably short of its human

counterpart. There are numerous of this. Given two pictures, a preschool child could

easily tell the difference between a cat and dog. Yet this same simple task would

confound today's computers.

Artificial intelligence (AI) is the field of computer science that attempts to give computer

human like abilities. One of the primary means by which computers are endowed with

humanlike abilities is through the use of a neural network. The human brain is the

ultimate example of a neural network. The human brain consists of a network of over a

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hundred billion interconnected neurons. Neurons are individual cells that can process

small amount of information and then activates other neurons to continue the process.

A work is done on Devanagari optical character recognition by Anil et. al. [23] shows the

use of feed-forward for training the data and back-propagation for recognition of

characters. The paper by Dong Xiao Ni [24] uses the basic biological neuron and the

artificial computation model; outlines network architectures and learning processes and

multilayer feed-forward network is used for optical character recognition. It uses neural

network as a powerful data modeling tool that is able to capture and represent complex

input/output relationships. The paper by Anne et. al. [25] uses MLF neural networks ,

trained with a back-propagation learning algorithm, the most popular neuralnetworks

which is applied to a wide variety of problems. Bishnu Chaulagain et. al . uses Hidden

Markov Model (HMM) for neural network [26, 27] . The paper also shows the use of

Tesseract engine for character recognition in Devanagari script.

It is not possible to find weights which enable Single Layer Perceptron to deal with non-

linearly separable problems like XOR. However, Multi-Layer perceptions (MLPs) are

able to cope with non-linearly separable problems. Minsky & Papert (1969) offered

solution to XOR problem by combining perceptron unit responses using a second layer

of units [28].

The most common neural network model is known as a supervised network because it

requires a desired output in order to learn [24]. The binary data is then fed into a neural

network that has been trained to make the association between the character image data

and a numeric value that corresponds to the character. The output from the neural

network is then translated into ASCII text and saved as a file [24].

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3. METHODOLOGY

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3.1. Generic Description

The general overview of the VLPD-R system is shown in the Figure 3-1 and Figure 3-2.

The system includes the two cases:

i) Localization of license plate

ii) Recognition of character from the plate

And these are the main goal of the system. In the first case, the user provides the images

and the system perform processing with the output of the segmented number plate from

the provided image. The second case is about the neural network learning and training.

Teach pattern is another sub use case. This is included dependency of the use case train

neural network. This means teaching a neural network must include some kind of pattern

teaching. Then character recognition use case comes. This goal is accomplished by a sub

goal which includes some kind of pattern recalling. Thus character recognition system

works. Neural network system is secondary actor whose main purpose is to interact with

overall system.

The main program is represented by the Figure 3-3. Our main goal is to obtain the

character from the segmented part of the image. It has the three main sub cases namely

image processing, localization and optical character recognition. The final output of the

system is user readable in the text format.

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Figure 3-4: Use Case Diagram for VLP Segmentation.

Figure 3-5: Use Case Diagram of Optical Character Recognition

Figure 3-6: Use Case Diagram for Main Program

3.2. System Design

The data flow diagram (DFD) – level 0 of the system is shown in the Figure 3-4. Further

the DFD is expanded to level 1 for different process: preprocessing, training and

recognition as shown in Figure 3-5, Figure 3-6 and Figure 3-7 respectively.

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Scanner1. Pre-processing of Image

2. VLP Localization4. Train 3. Character Extraction5. Trained

data6.

Recognize7. OutputUSERUSEREnhancement on ImageReadable TextDigital ImageWeightsNormalized Image dataExtraction of VLPNormalized Image dataWeightsResult

Figure 3-7: Level 0 DFD of the System

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1.1 Grey Scaling

1.5 Horizontal and vertical

Projection

1.3 Sobel Filter

(Horizontal and Vertical)

1.6 Segmentation

1.2 Histogram Equalization

1.4Binarization

Digital ImageGrey Scaled ImageUniform Pixel Density Distribution in imageObtain Horizontal and Vertical edges in imageObtain the Image in 0’s and 1’sObtain Square Shaped ImagesObtain VLP from the Image

Figure 3-8: Level 1 DFD for Process "Preprocessing"

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Normalized Image

4.1Get Normalized

Image

4.2 Initialize weight for

first time

4.3 Calculate output by backward

propagation

4.4 Compare teacher output to

calculated output

USER

4.5 Back propagate errorTrained Data

(weights)

Error

Trained data (weight)

Image in matrix form

Change in weight

Weight matrix

Calculated output

Teacher output

Figure 3-9: Level 1 DFD of process "Training"

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Normalized Image

6.1 Get Normalized Image

6.2 Calculate Output by back

propagation

6.3 Make Decision

Trained Data(weights)

Image in matrix form

Weight matrix form

Calculated output

ResultFigure 3-10: Level 1 DFD for Process "Recognition"

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3.3. Technical Description

The technical description of the system is shown in the Figure 3.8.

Scanned ImagePre-processingSegmentationFeature ExtractionNeural NetworkTrainingTestingOutputRecognized Text

Figure 3-11: Technical Description of the System

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3.3.1. Pre-processing and VLP Localization

Image taken from camera is processed by the preprocessing module. The purpose of this

module is to enrich the edge features. Because our detection method is based on the

boundary features, it improves the successful rate of the VLP Localization module. The

algorithms which are used in this module can be sequentially stated as graying,

normalization and histogram equalization. After having a gray scaled image, we have

used Sobel filters to extract the edging image, and threshold the image to the binary one.

We have used the local adaptive threshold algorithm for binarization of image. The

resulted image is used as input for the VLP localization module [21, 29].

In VLP Localization step, detection of a number plate area is carried out. This

problematic includes algorithms that are able to detect a rectangular area of the number

plate in an original image. Humans define a number plate in a natural language as a

“small plastic or metal plate attached to a vehicle for official identification purposes”,

but machines do not understand this definition as well as they do not understand what

“vehicle”, “road”, or whatever else is. Because of this, there is a need to find an

alternative definition of a number plate based on descriptors that will be comprehensible

for machines.

Let us define the number plate as a “rectangular area with increased occurrence of

horizontal and vertical edges”. The high density of horizontal and vertical edges on a

small area is in many cases caused by contrast characters of a number plate, but not in

every case. This process can sometimes detect a wrong area that does not correspond to a

number plate. Because of this, we often detect several candidates for the plate by this

algorithm, and then we choose the best one by a further heuristic analysis [2, 21, 29,].

3.3.1.1. Edge Detection

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We can use a periodical convolution of the function f(x,y), where x and y are spatial

coordinates and f(x,y) is intensity of light at that point of the image, with specific types

of matrices m to detect various types of edges in an image

………………….. (3.1)

Where w and h are dimension of image represented by the function f and m[x,y]

represent the element in xth rows and jth column of the matrix m.

Horizontal and vertical edge detection

To detect horizontal and vertical edges, we convolve source image with matrices mhe and

mve. The convolution matrices are usually much smaller than the actual image. Also, we

can use bigger matrices to detect rougher edges.

mhe=−1 −1 −10 0 01 1 1

mve= −1 0 1−1 0 1−1 0 1

Sobel edge detector

The Sobel edge detector uses a pair of 3x3 convolution matrices. The first is dedicated

for evaluation of vertical edges, and the second for evaluation of horizontal edges.

Gx = −1 −2 −10 0 01 2 1

Gy = −1 0 1−2 0 2−1 0 1

The magnitude of the affected pixel is then calculated using the formula, G = √Gx2+G y

2

In praxis, it is faster to calculate only an approximate magnitude as, G =| G x∨¿ + |G y|.

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f (x , y )=f (x , y )∗m [ x , y ]=∑i=0

w−1

∑j=0

h−1

f (x , y ) . m [modw ( x−i ) ,mod h( y− j)]

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Horizontal and Vertical projection

The vertical projection of the image is a graph, which represents an overall magnitude of

the image according to the axis y. The vertical projection of so transformed image can be

used for a vertical localization of the number plate. The horizontal projection represents

an overall magnitude of the image mapped to the axis x.

We can mathematically define the horizontal and vertical projection as:

px ( x )=∑j=0

h−1

f (x , j) ……………………………………………………………… (3.2)

py ( y )=∑i=0

w−1

f (i , y ) ……………………………………………………………... (3.3)

3.3.1.2. Double-phase statistical image analysis

The statistical image analysis consists of two phases. The first phase covers the detection

of a wider area of the number plate. The output of double-phase analysis is an exact area

of the number plate.

The detection of the number plate area consists of a “band clipping” and a “plate

clipping”. The band clipping is an operation, which is used to detect and clip the vertical

area of the number plate (so-called band) by analysis of the vertical projection of the

snapshot. The plate clipping is a consequent operation, which is used to detect and clip

the plate from the band (not from the whole snapshot) by a horizontal analysis of such

band.

Snapshot

Assume the snapshot is represented by a function f(x,y), where x0<x<x1 and y0<y<y1

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The [x0,y0] represents the upper left corner of the snapshot, and [x1,y1] represents the

bottom right corner. If w and h are dimensions of the snapshot, then

x0=0 , y0=0 , x1=w−1andy1=h−1.

Band

The band b in the snapshot f is an arbitrary rectangleb=(xb 0 , yb 0 , xb 1 , y b1), such as:

(xb 0=xmin) and (xb 1=xmax) and (ymin ≤ yb 0 ≤ yb1 ≤ ymax)

Plate

Similarly, the plate p in the band b is an arbitrary rectangle p=(x p 0 , y p 0 , xp 1 , y p 1), such

as:

¿¿) and (y p 1= yb1) and (xb 0≤ x p 0≤ x p 1≤ xb 1)

Band clipping

The band clipping is a vertical selection of the snapshot according to the analysis of a

graph of vertical projection. If h is the height of analyzed image, the corresponding

vertical projection pyr ( y ) contains h values, such as y € < 0; h-1 >.

The fundamental problem of analysis is to compute peaks in the graph of vertical

projection. The peaks correspond to the bands with possible candidates for number

plates. The maximum value of py ( y ) corresponding to the axle of band can be computed

as:

ybm=argmax y0≤ y≤ y1 { p y( y)} ………………………………... (3.4)

The yb 0 and yb 1 are coordinates of band, which can be detected as:

yb 0=max y0 ≤ y ≤ ybm { y∨p y ( y )≤ c y . py ( ybm)}………….. ….. (3.5) yb 1=max ybm≤ y≤ y1 { y∨p y ( y )≤ c y . py ( ybm)}……………..... (3.6)

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c y is a constant, which is used to determine the foot of peak ybm. In praxis, the constant

is calibrated to c1=¿ 0.55 for the first phase of detection, and c2=¿ 0.42 for second

phase.

3.3.1.3. Segmentation

Segmentation is a classifier which helps to separate each character and line from the

given image with the proper definition of boundaries. In VLPD, segmentation is one of

the important step for automatic number plate recognition, because the further processing

of image depends in it. In segmentation, boundaries are defined and each lines and

character is separated so that character-wise manipulation on the license plate can be

done.

Horizontal Segmentation:

It is the method for horizontal separation of the image for the separation of lines in the

license plate. Normally, VLP in the back side are provided with 2 rows, upper row

containing the development region, available numbers of vehicle and its type and lower

row containing the numeric value. The image document is horizontally projected so that

the band of minima can be obtained describing the region for splitting.

Figure 3-12: Horizontal Projection of VLPD

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The minima obtained after projection is the point required for the intersection of the

word. The horizontal segmentation separate the upper row and lower row as shown in the

Figure 3-9.

Vertical segmentation:

It is the method for determining the character of the image from the line of the words.

The vertical projection of the image as shown in the Figure …… will provide the lowest

point for determining the separation of word. Since the density value for the pitch black

pixel is 0 and for white is 255. The vertical projection will calculate the column wise

pixel density and multiple minimum points for segmentation is obtained. The region

between the two minimum points is the required region for segmentation of the image.

The process is same as the horizontal segmentation but the difference is here character is

segmented and ready for the input in the neural network.

Figure 3-13: Vertical Projection of the Image

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3.3.2. Thinning

Thinning is the process of peeling off a pattern as many pixels as possible

without affecting the general shape of the pattern. In other words, after pixels have been

peeled off, the pattern can still be recognized.

Zhang-Suen Thinning Algorithm

This algorithm has two steps, which will be successively applied to the image [30]. In each

step contour points of the region that can be deleted are identified. Contour points

are defined as points that have value “1” and they have at least one 8-neighbor pixel

value equal to “0.”

Step 1: Pixel I(i,j) is marked for deletion if ALL of the following 4 conditions are true

1. Its connectivity = 1

2. Has at least 2 object neighbors and not more than 6

3. At least one of P2, P4, P6are background

4. At least one of P4, P6, P8are background

5. Delete marked

Step 2: Same as first except rules 3 and 4 are changed

3. At least one of P2, P4, P8are background

4. At least one of P2, P6, P8are background

5. Delete Marked

If at the end of any sub-iteration, there are no pixels to be deleted, the skeleton is

complete. First sub-iteration, as shown in Figure3-11, removes south or east boundary

pixels or north-west corner pixels.

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Figure 3-14: First Sub-iteration

Second sub-iteration, as shown in Figure 3-12, removes north or west boundary pixels or

south-east corner pixels

Figure 3-15: Second Sub-iteration

Each of the above steps reduces the amount of the information to be processed by a

feature extraction. Figure 3-13 shows the result of Zhang-Suen thinning algorithm.

Figure 3-16: Original pattern and Skeleton as a result of Zhang-Suen thinning algorithm

3.3.3. Feature Extraction

Information contained in a bitmap representation of an image is not suitable for

processing by computers. Because of this, there is need to describe a character in another

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way. The description of the character should be invariant towards the used font type, or

deformations caused by a skew. In addition, all instances of the same character should

have a similar description. A description of the character is a vector of numeral values,

so-called “descriptors”, or “patterns”:

Generally, the description of an image region is based on its internal and external

representation. The internal representation of an image is based on its regional

properties, such as color or texture. The external representation is chosen when the

primary focus is on shape characteristics. The description of normalized characters is

based on its external characteristics because we deal only with properties such as

character shape. Then, the vector of descriptors includes characteristics such as number

of lines, bays, lakes, the amount of horizontal, vertical and diagonal or diagonal edges,

and etc. The feature extraction is a process of transformation of data from a bitmap

representation into a form of descriptors, which are more suitable for computers.

3.3.3.1. Fast Fourier Transform (FFT)

FFT is one of the efficient method for extracting the signature of an image. It

decomposes an image into its real and imaginary components which is a representation

of the image in the frequency domain. If the input signal is an image then the number of

frequencies in the frequency domain is equal to the number of pixels in the image or

spatial domain. The inverse transform re-transforms the frequencies to the image in the

spatial domain. The FFT of a 2D image are given by the following equations:

F ( x )=∑n=0

N−1

f (n)e−i2 π (x n

N )……………………………………………… (3.6)

The Fourier transform of an image is a simple extension of the 1-D Fourier transform

into two dimensions, and is achieved by simply applying the 1-D transform to each row

of an image, and then transforming each column of the resulting image. It produces

essentially the same thing. A picture of smooth water waves travelling in a diagonal

direction will transform to a series of spikes along that same diagonal.

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The Fourier transform is defined over continuous functions. The FFT is a technique for

efficiently evaluating the Fourier transform over discrete sets of data.

3.3.3.2. Density over Different Zones

The density of each pixel over various region defines the structure of the image. The

image is splitted into different zones as per the need for the accuracy. The system over

here splits the image into 8 different region i.e. 24*24 image into 3*3 image and the

density of each region is calculated. It will provide the data of 8 different region which is

assumed to be nearer or equivalent for the target value.

3.3.3.3. Area of Image

The area of the image is the count of the number of pixel for which f ( x , y )=1. The area

of the image for the constant size image is almost invariable due to which the properties

is one of the reason for defining the image properties [8].

3.3.3.4. Moment Invariants

Moment invariants are important tools in object recognition problem. These techniques

grab the property of image intensity function. Moment invariants were first introduced to

the pattern recognition community in 1962 by Hu [29], who employed the results of the

theory of algebraic invariants and derived his seven famous invariants to rotation of 2-D

objects. Moment invariants used in this research for extracting statistical patterns of

character images are given in [10]. Moment invariants are pure statistical measures of the

pixel distribution around the center of gravity of the character and allow capturing the

global character shape information.

The standard moments mpq of order (p+q) of an image intensity function f(x;y) is given

by,

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…………………... (3.7)

A uniqueness theorem states that if f(x;y) is piecewise continues and has non-zero values

only in a finite part of the xvis ; yvis plane, moments of all order exist and the moment

sequence (mpq) is uniquely determined by f(x;y). Conversely, (mpq) is uniquely

determines f(x;y).

For discrete domain, the 2-D moment of order (p+q) for a digital image f(x;y) of size

M x N is given by,

……………….. (3.8)

The corresponding central moment of order (p+q) is defined as,

………... (3.9)

Where,

………………………………….. (3.10)

The normalized central moments, denoted by ɳpq , are defined as,

………………………………………………… (3.11)

Where,

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……………………………. (3.12)

A set of seven invariant moments can be derived from the second and third moments [23]

which are invariant to translation, scale change, mirroring, and rotation, are given as

follows.

…………………………………………………….. (3.13a)

………………………………………… (3.13b)

………………………………... (3.13c)

……………………………… (3.13d)

…… (3.13e)

……………………. (3.13f)

………. (3.13g)

3.4. Artificial Neural Network (ANN)

ANN is a non-linear, parallel, distributed, highly connected network having capability of

adaptivity, self-organization, fault tolerance and evidential response, which closely

resembles with physical nervous system. Physical nervous system is highly parallel,

distributed information processing system having high degree of connectivity with

capability of self-learning.

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f1(e)f2(e)f3(e)f4(e)f5(e)f6(e)X1X2

Figure 3-17: Feed Forward Multilayer Perceptron

3.4.1. Multilayer Perceptron

It consists of multiple layers of computational units, usually interconnected in a feed-

forward way. Each neuron in one layer has directed connections to the neurons of the

subsequent layer. It consists of a layer of input units, one or more layers of hidden units,

and one layer of output units shown in Figure 3-14 and 3-15. The output from each layer

is the weighted linear summation of all input vectors along with the bias term, passed

through some activation function. The network weight adjustment is done by back-

propagating the error of the network.

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W1 W3 W2

Activation

Function

Weight Adder

Output

U1

U2

U3

ФY

3.4.2. Feedforward Back Propagation Neural Network

A Feedforward Neural Network (FFNet) is a biologically inspired classification

algorithm. It consist of a (possibly large) number of simple neuron-like processing units,

organized in layers. Every unit in a layer is connected with all the units in the previous

layer. These connections are not all equal, each connection may have a different strength

or weight. The weights on these connections encode the knowledge of a network. Often

the units in a neural network are also called nodes.

Data enters at the inputs and passes through the network, layer by layer, until it arrives at

the outputs. During normal operation, that is when it acts as a classifier, there is no

feedback between layers. This is why they are called FFNet.

3.4.3. Backpropagation

Backpropagation, an abbreviation for "backward propagation of errors", is a common

method of training artificial neural networks. From a desired output, the network learns

from many inputs, similar to the way a child learns to identify a dog from examples of

dogs.

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Figure 3-18: Operation on Layer's Node

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It is a supervised learning method, and is a generalization of the delta rule. It requires a

dataset of the desired output for many inputs, making up the training set. It is most useful

for feed-forward networks (networks that have no feedback, or simply, that have no

connections that loop). Backpropagation requires that the activation function used by

the artificial neurons (or "nodes") be differentiable.

For better understanding, the Backpropagation learning algorithm can be divided into

two phases: propagation and weight update.

Phase 1: Propagation

Each propagation involves the following steps:

1. Forward propagation of a training pattern's input through the neural network in order

to generate the propagation's output activations.

2. Backward propagation of the propagation's output activations through the neural

network using the training pattern target in order to generate the deltas of all output

and hidden neurons.

Phase 2: Weight update

For each weight-synapse follow the following steps:

1. Multiply its output delta and input activation to get the gradient of the weight.

2. Bring the weight in the opposite direction of the gradient by subtracting a ratio of it

from the weight.

This ratio influences the speed and quality of learning; it is called the learning rate. The

sign of the gradient of a weight indicates where the error is increasing, this is why the

weight must be updated in the opposite direction.

3.4.4. Training FFNet

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The FFNet uses a supervised learning algorithm: besides the input pattern, the neural net

also needs to know to what category the pattern belongs. Learning proceeds as follows: a

pattern is presented at the inputs. The pattern will be transformed in its passage through

the layers of the network until it reaches the output layer. The units in the output layer all

belong to a different category. The outputs of the network as they are now are compared

with the outputs as they ideally would have been if this pattern were correctly classified:

in the latter case the unit with the correct category would have had the largest output

value and the output values of the other output units would have been very small. On the

basis of this comparison all the connection weights are modified a little bit to guarantee

that, the next time this same pattern is presented at the inputs, the value of the output unit

that corresponds with the correct category is a little bit higher than it is now and that, at

the same time, the output values of all the other incorrect outputs are a little bit lower

than they are now. (The differences between the actual outputs and the idealized outputs

are propagated back from the top layer to lower layers to be used at these layers to

modify connection weights. This is why the term backpropagation network is also often

used to describe this type of neural network.

3.5. Training Neural Network

The individual neurons that make up a neural network are interconnected through the

synapses. These connections allow the neurons to signal each other as information is

processed. Not all connections are equal. Each connection is assigned a connection

weight. If there is no connection between two neurons, then their connection weight is

zero. These weights are what determine the output of the neural network. Therefore, it

can be said that the connection weights form the memory of the neural network.

Training is the process by which these connections weights are assigned. Most training

algorithm begins by assigning random numbers to the weights matrix. Then the validity

of the neural network is examined. Next the weights are adjusted based on how valid the

neural network performed. This process is repeated until the validation error is within an

acceptable limit. The type of learning in neural networks is determined by the manner in

which the parameter changes. There are many ways to train neural networks.

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3.5.1. Supervised Learning

In a supervised learning process, the adjustment of weights is done under the supervision

of a teacher or ideal output, that is, precise information about the desired or correct

network output is available from a teacher when given a specific input pattern.

3.5.2. Error Correction Learning

The goal is to minimize the cost to correct the errors. This leads to the well-known delta

rule (or Widrow-Hoff rule), which is stated as the adjustment made to a synaptic weight

of a neuron is proportional to the product of the error signal and the input signal of the

synapse in question

3.6. Validating Neural Networks

Once a neural network has been trained, it must be evaluated to see if it is ready for

actual use. This is important so that it can be determined if additional training is required.

To correctly validate a neural network, validation data must be aside that is completely

separate from the training data.

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4.

IMPLEMENTATION

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4.1. Global Thresholding

The basic global threshold, T is calculated as follows:

Select an initial estimate for T (typically the average grey level in the image)

Segment the image using T to produce two groups of pixels, G1 consisting of

pixels with grey levels > T and G2 consisting pixels with grey levels < T

Compute the average grey levels of pixels in G1 to give µ1 and G2 to give µ2.

Compute a new threshold value

T=µ1+µ22 …………………………………………………………………

(4.1)

Repeat steps 2 to 4 until the difference in T in successive iterations is less than a

predefined limit T.

4.2. Region Based Segmentation (Horizontal and Vertical)

The goal of the segmentation algorithm is to find peaks, which correspond to the spaces

between characters. At first, there is a need to define several important values in a graph

of the horizontal projection px(X):

Vm – The maximum value contained in the horizontal/ vertical projection px(x).

Va – The average value of the horizontal projection px(x).

The algorithm of segmentation iteratively finds the maximum peak in the graph of

vertical/ horizontal projection. The peak is treated as a space between characters, if it

meets some additional conditions, such as height of peak. The algorithm then zeroizes

the peak and iteratively repeats this process until no further space is found. This principle

can be illustrated by the following steps:

1. Determine the index of the maximum value of horizontal projection.

Xm = arg {max (px)} 0≤x≤w

2. Detect the left foot and the right foot of the peak as:

Xl = max {x|px(x)≤ Cx . px(xm)}

Xr = min {x|px(x)≤ Cx . px(xm)}

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3. Zeroize the horizontal projection px(xm) on inverval (xl,xr)

4. If px(xm)< Cw. Vm, go to step 7.

5. Divide the plate horizontally in the point xm.

6. Go to step 1.

7. End.

Two different constants have been used in the algorithm above. The constant x c is used

to determine foots of peak xm . The optimal value of cx is0.7. The constant cw determines

the minimum height of the peak related to the maximum value of the projection ( m v ). If

the height of the peak is below this minimum, the peak will not be considered as a space

between characters. It is important to choose a value of constant w c carefully. An

inadequate small value causes that too many peaks will be treated as spaces, and

characters will be improperly divided. A big value of w c causes that not all regular peaks

will be treated as spaces, and characters will be improperly merged together. The optimal

value of w c is 0.86. To ensure a proper behavior of the algorithm, constants x c and w c

should meet the following condition:

Where P is a set of all detected peaks m x with corresponding foots xl and xr.

4.3. Back Propagation

It is a supervised learning method, and is a generalization of the delta rule. It requires a

dataset of the desired output for many inputs, making up the training set. It is most useful

for feed-forward networks (networks that have no feedback, or simply, that have no

connections that loop). Backpropagation requires that the activation function used by

the artificial neurons (or "nodes") be differentiable.

1. Initialize the weights to small random values.

2. Feed the training sample through the network and determine the final output.

3. Compute the error for each output unit, for unit k it is: δk=(tk-yk)f'(y_ink) where

tk=Required Output yk=Actual Output f'(y_ink)=Derivative of f

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4. Calculate the weight correction term for each output unit, for unit k it is:

Δwjk=αδkzj Where, α is a small constant zj is hidden layer signal.

5. Propagate the delta terms (errors) back through the weights of the hidden units

where the delta input for the jth hidden unit is : Δ_inj=Σk=1m δkwjk The delta term

for the jth hidden unit is: δj=δ_injf'(z_inj)

6. Calculate the weight correction term for the hidden units Δwij=α δjxi

7. Update the weights. wjk(new)=wjk(old)+ Δwjk

8. Test for stopping (maximum cycles, small changes etc.)

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5. RESULT AND DISCUSSION

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5.1 Result

The experiments conducted were targeted for the validation of the model for deployment.

The result was analyzed in various stages of the system. First stage of experiment was

carried out for license plate localization following with horizontal segmentation and

vertical segmentation. After that experiment for character recognition was carried out.

The experiments for the recognition of individual character of the license plate were

carried out in two different modes.

Training the System: In this mode, the errors in each iteration were analyzed. The main

issue of this experiment was to test whether the system is reaching to the stable state or

not.

Recognize the Characters: This mode of experiment was conducted to test whether the

model trained is capable of predicting the correct values or not.

5.2. Observation and Discussions

Table 5-1 shows accuracy of the system at different stages. The plate localization,

horizontal segmentation of localized license plate, vertical segmentation and finally

character recognition are considered as different stages of the system. Figure 5-1 shows

the accuracy of the system at different stages.

Table 5-2: Accuracy rate corresponding to different stages

Stage/Part Accuracy

Plate Localization 67%

Horizontal Segmentation 98%

Vertical Segmentation 90%

Character Recognition 92%

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Plate Localization Horizontal Segmentation Vertical Segmentation Character Recognition0%

10%20%30%40%50%60%70%80%90%

100% Chart Title

Stages of the system

Acc

urac

y R

ate

Figure 5-19: Accuracy Rate of different stages of the system

Table 5-2 shows the recognition rate of each character of the vehicle license plate presented to the system. Figure 5-2 shows the graph of recognition accuracy rate of the system corresponding to individual character.

Table 5-3: Recognition Result of individual character

Class Accuracy Rate Class Accuracy Rate

0 97% 8 88%

1 85% 9 82%

2 82% k 87.5%

3 97% r 86.6%

4 94% af 90.7%

5 84% h 92.3%

6 86% u 85.4%

7 85% s 94.1%

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0 1 2 3 4 5 6 7 8 9 k r af h u s70%75%80%85%90%95%

100%

Chart Title

Character

Rec

ogni

tion

Acc

urac

y

Figure 5-20: Recognition accuracy of individual character

5.3. Output

Figure 5-21: Input Image for VLPR System

Figure 5-22: VLP Localization

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Above Figure 5-3 indicate the image taken from camera for the VLPR system. The result

that shown in Figure 5-4 indicate the VLP localization. The area surrounded by blue

rectangle indicates the position of the VLP in the image.

Segmentation

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Figure 5-23: VLP Horizontal

Segmentation

Figure 5-24: VLP Vertical Segmentation

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5. CONCLUSION AND FUTURE ENHANCEMENT

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6.1. Conclusion

The project is an attempt to emphasize on the recognition of Nepali Vehicle License

Plate Detection and Recognition in the simplest possible manner with the use of image

processing techniques and neural network enable us to determine the best possible

output.

The system developed has capability to locate the license plate from the supplied image

and determine the character present in it. The various image processing techniques like

filtering, thinning, binarization, cropping, etc. help in determining the plate and

individual character’s image and its features are used as input to the neural network

which recognizes the character.

6.2. Future Enhancement

VLPD-R is a vast area of research. VLPD-R like project need deep research and

knowledge to address different problem associated with it in different conditions. The

system is optimized to work with straight image and in proper lighting condition of the

license plate image, skewness and low contrast in the image reduces accuracy rate of

plate localization. Moreover the similar color of vehicle and license plate creates

problem in detection of license plate. The present system requires series of manual input

which is not acceptable in real life implementation.

Hence, further work to improve the present system is needed. The system should be

made to work with any type of image in any lighting condition. Moreover system has to

build fully automatic so that it can easily deploy in real life for various purpose. The

system is working with only image; it will make to operate video too.

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6. EPILOGUE

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7.1. References

[1] MukeshKumar,”A Real-Time Vehicle License Plate Recognition (LPR)

System”, A thesis report submitted for the completion of Master degree in

Electronics Instrumentation and control engineering, July 2009.

[2] OndrejMartinsky, “Algorithmic and Mathematical Principles of Automatic

Number Plate Recognition System”, A thesis report submitted for the completion

of BSC to Faculty of Information Technology, BRNO University Of Technology,

August 2007.

[3] Vehicle and Transport Management Act of Nepal, 2003.

[4] An article Published on Himalayan times

[5] Nepal Traffic Police, License Plate Information cited from

http://traffic.nepalpolice.gov.np/other-notices/number-plate1.html

[6] Tran DucDuan, Tran Le Hong Du, Tran VinhPhuoc, Nguyen Viet Hoang,

“Building an Automatic Vehicle License-Plate Recognition System”, Intl. Conf.

in Computer Science – RIVF’05, Feb 21-24, 2005, Can Tho, Vietnam.

[7] An article on Tesseract based Nepali OCR - Resarch Report published on

http://nepalinux.org/index.php?

option=com_content&task=view&id=46&Itemid=53

[8] Ashok Kumar Pant, Sanjeeb Prasad Panday and Prof. Dr. Shashidhar Ram Joshi,

"Off-line Nepali Handwritten Character RecognitionUsing Multilayer Perceptron

and Radial Basis Function Neural Networks".

[9] Dr. Richard Spillman, “Artificial Intelligence”, PLU, Fall 2003

[10] Gonzalez Woods and Eddins, “Digital Image Processing”, vol-3.

[11] Oivind Due Trier, Anil K. Jain, and TorfinnTaxt, “Feature Extraction Methods

for Character Recognition – A Survey”, Pattern Recognition, Vol. 29.No. 4, pp.

641-662, 1996.

[12] Huang L., Wan G., Liu C., “An Improved Parallel Thinning Algorithm”, 2003.

[13] Martin F. Møller, "A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning", University of Aarhus, Denmark, 1990.

[14] KiriWagstaff, "ANN Backpropagation: Weight updates for hidden nodes", 2008

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[15] Bal´azsEnyedi, LajosKonyha and K´alm´anFazekas, "Real Time Number Plate

Localization Algorithms",Journal of ELECTRICAL ENGINEERING, VOL. 57,

NO. 2, 2006, 69–77.

[16] Hamid Mahini, ShohrehKasaei, FaezehDorri and FatemehDorri, "An Efficient

Features–Based License Plate Localization Method", IEEE, 2006.

[17] Dong Xiao Ni, "Application of Neural Networks to Character

Recognition",Proceedings of Students/Faculty Research Day, CSIS, Pace

University, May 4th, 2007.

[18] Augusto Celentanoand Vincenzo Di Lecce, "A FFT based technique for image

signature generation".

[19] Vehicle and Transportation Management act",2054B.S..[20] KuruGollu, B. Sankur and A.E. Harmanci, "Color Image Segmentation Using

Histogram Multithresholding And Fusion",2001.

[21] BishnuChaulagain, BrizikaBantawaRai and Sharad Kumar Raya, "Final

Report on Nepali Optical Character Recognition ",2009.

[22] Anne Magaly De Paula Canuto,"Combining Neural Networks And Fuzzy Logic

For Applications In Character Recognition", University of Kent at Canterbury,

2001.

[23] M.-K. Hu, “Visual pattern recognition by moment invariants,”IRE Transactions

on Information Theory, vol. IT-8, pp. 179–187, 1962.

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7.2. Glossary

Activation function: A mathematical function used in neural network to map input

values to closed range of values between -1 to 1.

Back Propagation training: A sound and systematic means of training a multilayer

network.

Digital image processing: Manipulation, improvement analysis of pictorial information

of image that is digitally represented.

Epoch: Number of iteration taken for one cycle.

Feature extraction: The process of extracting essential characteristics of an input.

Momentum: A method that is used to accelerate the training process of back

propagation neural network.

Neurons: Interconnected nerve cells that make up most of the brain tissue in a living

organism.

Neural network: A computer model that simulates the working of biological neuron.

Output vector: A vector that hold the output values generated by a trained network from

an input vector during the process of knowledge retrieval.

Pixel: The smallest unit of an image. All images are composed of 2-d array of pixels.

Segmentation: Differentiation of object of interest from the background.

Thinning: The process of extracting shape/skeleton of image.

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