DEVELOPMENT OF AUTOMATIC NUMBER PLATE RECOGNITION ON ANDROID MOBILE PHONE

PLATFORMS

BY

ABDUL MUTHOLIB

A thesis submitted in fulfilment of the requirement for the degree of Master of Science

(Computer and Information Engineering)

Kulliyyah of Engineering International Islamic University Malaysia

MAY 2014

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ABSTRACT

Automatic Number Plate Recognition (ANPR) is an intelligent system which has the capability to recognize the character on vehicle number plate. As the combination of hardware and software, ANPR is designed to offer the optimum reliability. Over the last decade, the researchers have proposed their methods to recognize the vehicle number plate and implemented it in various access control, law enforcement and security, parking management system, toll gate or border access, tracking of stolen vehicles and traffic violations (speed trap). Most of the researchers implement the ANPR system on personal computer (PC) using high resolution camera and high computational capability. Only some of them have been designed and implemented the ANPR on mobile phone, which has limitation in camera resolution and processing speed. Prior to that, the main challenge of implementing ANPR algorithm on mobile phone is how to produce a higher coding efficiency with lower computational complexity and higher scalability. Hence, the objective of this research is to propose suitable and optimize algorithm for the development of ANPR system on Android mobile phone. In this thesis, various steps to optimize ANPR are described, such as image pre-processing, segmentation, and optical character recognition (OCR) using template matching. The proposed ANPR algorithm is based on an open source image processing library called Leptonica and OCR library called Tesseract. For comparison purposes, the template matching based OCR is compared to Artificial Neural Network (ANN) based OCR. Furthermore, the optimization on ANPR is performed on the image pre-processing step using our own Java code as currently there is no image processing library available on the standard Android mobile phone. Performance of the proposed algorithm is evaluated by the developed number plates’ image database captured by mobile phone’s camera, i.e. 30 images. Results showed that the accuracy and processing time of the proposed algorithm using template matching are 97.46% and 1.13 seconds, respectively. On the other hand, the traditional algorithm used template matching only obtained 83.65% accuracy with 0.97 second processing time. The result shows that our proposed algorithm has improved the accuracy of the ANPR with negligible additional processing time.

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البحث ملّخص

طبيعة على التعرف على قدرة ذو ذكي نظام) ANPR( التلقائي رقم لوحة على التعرف نظام إنالنظام هو جمموعة من خردوات و برجميات و اهلدف من املواصالت. آلالت األرقام لوحة على مركبة

منذ العقود األخرية، اقرتح الباحثون طريقتهم لتعريف لوحة .تصميم هذا النظام لعرض االعتماد األمثل مواقف إدارة أرقام السيارة/ الدراجة و تطبق لغرض التحكم على الشيء، إنقاذ القانون و األمن، نظام

يق السريع، التحكم على وصول احلدود، التتبع على سرقة السيارات، االنتهاك على السيارات، بوابة الطر الكمبيوتر على أجهزة (ANPR) يطبق بعض الباحثني على هذا النظام إشارة املرور و هكذا.

و منهم ابتكروا الطريقة .استخدمت الكامريا عالية الدقة و قدرة احلساب العايل(PC) الشخصية و مع هذا، على اهلاتف احملمول (اجلوال) حمدودية الدقة يف الكامريا و يف سرعة احلساب.لتطبيق النظام

التحديات األوىل لتطبيق اخلوارزمية يف اهلاتف احملمول (اجلوال) هي كيفية حتصيل الرموزالفعالة العالية مع اح أمثل اخلوارزمية و لذلك، اهلدف من هذا البحث هو اقرت تعقيد احلساب األدىن بالتدرجية العالية.

يف هذه .يف اهلاتف احملمول (اجلوال) األندرويد (الربوت القائم) (ANPR)لتطوير هذا النظااملناسبة الرسالة ماجسرت، تبني عن أنواع اخلطوات لتمثيل و تعظيم النظام، منها ما قبل معاجلة الصورة، التقسيم،

باستخدام القالب املطابقة. و اقرتح خوارزمية النظام تستند (OCR)التعرف الضوئي على احلروفو مكتبة التعرف (Leptoninca) من مكتبة معاجلة الصورة من املصدر املفتوح و مها لبيتونيكا

و ألهداف املقارنة، تقارن بني التعرف . (Tesseract)الضوئي على احلروف املسمى تسسراكتلب املطابقة و التعرف الضوئي على احلروف باستخدام شبكة الضوئي على احلروف باستخدام القا

ذلك، يطبق النظام األمثل فيما قبل معاجلة الصورة باستخدام كود على عالوة .العصبية االصطناعية وقد من عندنا، حيث مل توجد مكتبة معاجلة الصورة املتاحة يف اهلاتف احملمول أندرويد. (Java)جافا

آلة عليها اليت استولت املتقدمة أرقام لوحات صورة بيانات قاعدة على املقرتحة اخلوارزمية أداء تقييم تمي لتجهيز والوقت معدل االعرتاف أن بنتائج الباحث وخلص .صورة ٣٠ أي ، احملمول للهاتف التصوير

.التوايل على ثانية، 1.13و ٪ 97.46 كان قالب مطابقة باستخدام املقرتحة اخلوارزمية هذه 83.65 نسبة بنتائج إال تأيت ال مطابق قالب باستخدام التقليدية اخلوارزمية فإن ذلك، إىل وباإلضافة

الباحث اقرتاحها املقرتحة اليت اخلوارزمية أن تبني هنا ومن .الثانية املعاجلة وقت 0.97االعرتاف مع ٪ .اإلضافية املعاجلة وقت تذكر مع االعرتاف معدل سيحسن

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APPROVAL PAGE I certify that I have supervised and read this study and that in my opinion; it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Master of Science in Computer and Information Engineering.

….………………..…………… Teddy Surya Gunawan Supervisor ….………………..…………… Jalel Chebil Co-Supervisor

I certify that I have read this study and that in my opinion; it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Master of Science in Computer and Information Engineering.

.……………..………………… Suhaimi Abd Latif Examiner (Internal) ………………..………………. M. Iqbal Saripan Examiner (External)

This dissertation was submitted to the Department of Electrical and Computer Engineering and is accepted as a fulfilment of the requirement for the degree of Master of Science in Computer and Information Engineering.

……………..…………………. Othman O. Khalifa Head, Department of Electrical and Computer Engineering

This dissertation was submitted to the Kuliyyah of Engineering and is accepted as a fulfilment of the requirement for the degree of Master of Science in Computer and Information Engineering.

..………………………………. Mohd. Noor bin Salleh Dean, Kulliyah of Engineering

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DECLARATION I hereby declare that this dissertation is the result of my own investigations, except

where otherwise stated. I also declare that it has not been submitted as a whole for any

other degrees at IIUM or other institutions previously or concurrently.

Abdul Mutholib

Signature …………………...……………… Date ……………..……

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INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION OF FAIR USE OF UNPUBLISHED RESEARCH

Copyright © 2014 by International Islamic University Malaysia. All rights reserved.

DEVELOPMENT OF AUTOMATIC NUMBER PLATE RECOGNITION ON ANDROID MOBILE PHONE PLATFORMS

No part of this unpublished research may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the permission of the copyright holder except as provided below. 1. Any material contained in or derived from this unpublished research may

only be used by others in their writing with due acknowledgement. 2. IIUM or its library will have the right to make and transmit copies (print

or electronic) for institutional and academic purpose. 3. The IIUM library will have the right to make, store in a retrieval system

and supply copies of this unpublished research if requested by other universities and research libraries.

Affirmed by Abdul Mutholib

………………….. …………………. Signature Date

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To my parents, my brothers, my sisters, my wife and my son.

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ACKNOWLEGEMENTS First of all I would like to say that I am so grateful and thankful to Allah SWT who has allowed me to complete all step in my thesis project. Without His mercy and blessing, it would have been impossible for me to work hard and reach all of the objectives of this project.

I gratefully acknowledge my supervisors, Associate Professor Dr. Teddy Surya Gunawan and Associate Professor Dr. Jalel Chebil for their full support, assistantship, supervision and giving me spirit in doing this project, so that I can complete my work.

I would like to express my acknowledgement for Prof. Dr. A.K.M Nurul Amin for giving me chance to become Engineering Journal assistant, Dr. Khaizuran for giving me chance to become his tutor. I would like to express my sincere thanks to Professor Dr. Othman O. Khalifa, Professor Dr. Farhat Anwar, Associate Professor Dr. Wadji Al Khateb, Professor Dr. Aisha Hasan, Associate Professor Dr. Shihab Ahmed, and Associate Prof. Dr. Abdi Omar Shuriye, for their knowledge and lecture given to me during my master program.

I also would like to express my gratitude for Br. Nashrul Hakim, Br. Abdul Rahmat Abdul Latief, and all my friends for their help and support during my study.

Last but not least, special thanks are specially given to my parents, my brothers, my sisters, and my wife, for their endless support, encouragement, and love.

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TABLE OF CONTENTS Abstract ............................................................................................................... ii Abstract in Arabic ............................................................................................... iii Approval Page .................................................................................................... iv Declaration Page .................................................................................................. v Dedication Page .................................................................................................. vi Acknowlegements ............................................................................................ viii List of Tables ...................................................................................................... xi List of Figures .................................................................................................. xiii List of Abbreviations ........................................................................................ xvi List of Symbols .............................................................................................. xviii CHAPTER ONE: INTRODUCTION ................................................................ 1

1.1 Background ....................................................................................... 1 1.2 Problem Statement and Its Significance ............................................. 2 1.3 Research Objectives .......................................................................... 3 1.4 Research Methodology ...................................................................... 4 1.5 Research Scope ................................................................................. 4 1.6 Thesis Outlines .................................................................................. 5

CHAPTER TWO: LITERATURE REVIEW .................................................. 7

2.1 Introduction ....................................................................................... 7 2.2 Malaysian’s Vehicle Number Plates .................................................. 7 2.3 Automatic Number Plate Recognition ................................................ 9

2.3.1 Image Acquisition ................................................................ 11 2.3.2 Image Pre-Processing ........................................................... 12 2.3.3 Localization of the License Plate .......................................... 14 2.3.4 Character Segmentation ....................................................... 16 2.3.5 Optical Character Recognition ............................................. 19

2.4 Android System ............................................................................... 27 2.4.1 Android Platform ................................................................. 29 2.4.2 Android Architecture ........................................................... 30 2.4.3 Dalvik Virtual Machine ........................................................ 32 2.4.4 Android Version................................................................... 33 2.4.5 The Life Cycle of an Application ......................................... 33 2.4.6 ANPR and Android .............................................................. 34

2.5 Application Library Overview ......................................................... 35 2.5.1 Leptonica ............................................................................. 36 2.5.2 Tesseract .............................................................................. 36

2.6 Performance Evaluation Metric ....................................................... 44 2.7 Summary ......................................................................................... 44

CHAPTER THREE: PROPOSED SYSTEM DEVELOPMENT,

ALGORITHM AND ANDROID IMPLEMENTATION .................... 45 3.1 Introduction ..................................................................................... 45 3.2 System Development Environment .................................................. 45

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3.2.1 Hardware Environment Development ................................... 46 3.2.2 Software Environment Development .................................... 48

3.3 System Design ................................................................................. 57 3.3.1 Graphical User Interface Design ........................................... 57 3.3.2 Process Design ..................................................................... 58 3.3.3 Database Design................................................................... 59

3.4 Proposed Algorithm......................................................................... 61 3.4.1 Acquisition of Number Plate Image...................................... 62 3.4.2 Image Pre-Processing ........................................................... 63 3.4.3 Character Segmentation of the Number Plate Image ............. 70 3.4.4 Character Recognition of Numbers Plate Image ................... 72

3.5 Implementation ............................................................................... 78 3.5.1 Graphical User Interface Design ........................................... 78 3.5.2 Code Constructing................................................................ 80 3.5.3 Compiling and Deploying .................................................... 85

3.6 Summary ......................................................................................... 87 CHAPTER FOUR: RESULTS AND DISCUSSION ...................................... 88

4.1 Introduction ..................................................................................... 88 4.2 Actual Grapichal User Interfaces ..................................................... 88 4.3 Image Database ............................................................................... 90 4.4 Experimental Scenarios ................................................................... 92 4.5 Development of The Proposed Algorithm ........................................ 94

4.5.1 The Effect of Unsharp Masking............................................ 94 4.5.2 The Effect of Color Inversion ............................................... 96 4.5.3 The Effect of Unsharp Masking and Color Inversion ............ 97 4.5.4 Summary of Improvement on the Proposed Algorithm ......... 98

4.6 Results of Original ANPR Algorithm .............................................100 4.6.1 Original Algorithm using ANN based OCR ........................100 4.6.2 Original Algorithm using Template Matching based OCR ...102

4.7 Results of Proposed ANPR Algorithm ............................................104 4.7.1 Proposed Algorithm using ANN based OCR .......................104 4.7.2 Proposed Algorithm using Template Matching based OCR .106 4.7.3 Comparison of Proposed Algorithms using ANN and

Template Matching Based OCR ..........................................108 4.8 Complexity Measurement ...............................................................108

4.8.1 Cyclometic Complexity .......................................................109 4.8.2 Halstead Complexity ...........................................................111

4.9 Discussion ......................................................................................113 4.10 Summary ........................................................................................117

CHAPTER FIVE: CONCLUSIONS AND FUTURE WORK ......................118

5.1 Conclusions ....................................................................................118 5.2 Contributions ..................................................................................119 5.3 Future Works ..................................................................................120

REFERENCES ...............................................................................................121 LIST OF PUBLICATIONS ............................................................................125 APPENDIX A .................................................................................................126

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LIST OF TABLES Table No. Page No.

2.1 Various Algorithms for Image Pre-Processing 14

2.2 Various Algorithms for Localization the License Plate 16

2.3 Various Algorithms for Character Segmentation 18

2.4 Various Algorithms for Optical Character Recognition 26

2.5 Summary of Android Versions 33

3.1 Minimum Hardware Requirement 46

3.2 PC Details Specification 47

3.3 Android Mobile Phone Details Specification 47

3.4 JDK 7 Minimum Requirement 49

3.5 Eclipse IDE Minimum Requirement 52

3.6 Android Emulator Controls 56

3.7 Detail of Table renp_master 60

3.8 Details of Table renp_collection 61

4.1 One Row Malaysian Number Plates 90

4.2 Two Rows Malaysian Number Plates 91

4.3 Accuracy of the Proposed Algorithm without Unsharp Masking 95

4.4 Accuracy of the Proposed Algorithm without Color Inversion 96

4.5 Accuracy of the Proposed Algorithm without Unsharp Masking and Color Inversion 98

4.6 Accuracy and Processing Time of Original ANPR Algorithm using ANN for One Row Number Plates 101

4.7 Accuracy and Processing Time of Original ANPR Algorithm using ANN for Two Rows Number Plates 101

4.8 Accuracy and Processing Time Averages for Original ANPR Algorithm using ANN for One Row and Two Rows Number Plates 102

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4.9 Accuracy and Processing Time for Original ANPR Algorithm Using Template Matching for One Row Number plates 102

4.10 Accuracy and Processing Time for Original ANPR Algorithm Using Template Matching for Two Row Number Plates 103

4.11 Accuracy and Processing Time Averages for Original ANPR Algorithm Using Template Matching for One Row and Two Rows Number Plates 104

4.12 Accuracy and Processing Time for Proposed ANPR Algorithm using ANN for One Row Number Plates 105

4.13 Accuracy and Processing Time for Proposed ANPR Algorithm using ANN for Two Rows Number Plates 105

4.14 Accuracy and Processing Time Averages for Proposed ANPR Algorithm Using ANN for One Row and Two Rows Number Plates 106

4.15 Accuracy and Processing Time for Proposed ANPR Algorithm Using Template Matching for One Row Number Plates 107

4.16 Accuracy and Processing Time for Proposed ANPR Algorithm Using Template Matching for Two Rows Number Plates 107

4.17 Accuracy and Processing Time Averages for Proposed ANPR Algorithm Using Template Matching for One Row and Two Rows Number Plates 108

4.18 Accuracy and Processing Time Comparison of Proposed Algorithm using ANN and Template Matching based OCR for One Row and Two Rows Number Plate 108

4.19 McCabe’s Cyclomatic Complexity of original and proposed ANPR 111

4.20 Halstead’s complexity of original and proposed ANPR 112

4.21 Error Analysis for Various Number Plates using Original Algorithm 114

4.22 Improvement of Recognition Errors using Proposed Algorithm 115

A.1 Peninsula Malaysia Registration Plates 126

A.2 Sarawak Registration Plates 127

A.3 Sabah Registration Plates 128

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LIST OF FIGURES Figure No. Page No.

2.1: Example of Putrajaya Car Number Plate Guidelines 8

2.2: Example of Jaguh Motorcycle Number Plate Guideline 9

2.3: General Flow Chart of ANPR System 10

2.4: Khalifa et al. Image Pre-processing Method 12

2.5: Duan et al. Image Pre-processing Method 13

2.6: Yo-Ping et al. Image Pre-processing Method 14

2.7: Kim et al. Localization of License Plate Method 15

2.8: Duan et al. Localization of License Plate Method 15

2.9: Badr et al. Localization of License Plate Method 16

2.10: Khalifa et al. Character Segmentation Method 17

2.11: Duan et al. Character Segmentation Method 17

2.12: Maarif and Sardi Character Segmentation Method 18

2.13: Hegt et al. OCR Method 20

2.14: Duan et al. OCR Method 21

2.15: Mathematical Non-linear Model of a Neuron 22

2.16: Artificial Neural Network used as OCR Method 24

2.17: SVM used as OCR method 25

2.18: Serkan Ozbay, and Ergun Ercelebi; and Kasaei et al. OCR Method 25

2.19: Top 8 Mobile Operating Systems in Asia (4/12 – 4/13) 28

2.20: Top 8 Mobile Operating Systems in worldwide (4/12 – 4/13) 29

2.21: Android Architecture 30

2.22: Dex File Anatomy 32

2.23: Tesseract OCR Engine Flowchart 38

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2.24: Initial Training Image 40

2.25: Code Generating Box File 41

2.26: Box File Generated from my.renp.exp0.tif 41

2.27: Box File Correction 42

2.28: Training Mode Code Generating 42

2.29: Generating Character Shape Code 43

3.1: JDK Version 49

3.2: Eclipse IDE 50

3.3: Android SDK Manager 53

3.4: Android Virtual Device Manager (AVD) 55

3.5: Android Emulator 56

3.6: Graphical User Interfaces 57

3.7: Flow Chart of ANPR in Android Mobile Phone and Database Server 58

3.8: Scenario of Design Process on Android Mobile Phone and Database Server 58

3.9: Database Design and Its Table Relation 60

3.10: Image Acquired by Android Mobile Phone’s Camera 62

3.11: Edge Enhancement Result 64

3.12: Color Inversion Pseudo Code 65

3.13: Color Inversion Result 65

3.14: 8-by-8 Image Divided into 3-by-3 Blocks 66

3.15: Otsu Thresholding Pseudo Code 70

3.16: Otsu Thresholding Result 70

3.17: A Fixed Pitch Chopped Method 71

3.18: Fixed Pitch pseudo code 71

3.19: Structure of the CNN 73

3.20: Illustration of the convolution and subsampling process inside a CNN 74

3.21: The subsampling layer of a CNN 75

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3.22: Outline Approximation 76

3.23: Feature and Matching 76

3.24: Main Layout of ANPR 79

3.25: Example of XML file contains UI elements 80

3.26: The onCreate method of the ANPR system 81

3.27: The callCamera method of the ANPR system 83

3.28: The onPictureTaken method of the ANPR system 84

3.29: The sendDataToServer method of the ANPR system 85

3.30: Dialog Android chooser 86

3.31: Compiling and deploying process 86

3.32: Information log of the running ANPR system 87

4.1: Actual Graphical User Interfaces (GUI) 89

4.2: Experimental Scenario of Original Algorithm (Tesseract Default) 93

4.3 Experimental Scenario of Proposed Algorithm 93

4.4 Comparison Effects of Additional Processes in Proposed Algorithm Using Template Matching for One Row Number Plates 99

4.5 Comparison Effects of Additional Processes in Proposed Algorithm Using Template Matching for Two Rows Number Plates 99

4.6: Result Improvement of Proposed Algorithm using Template Matching based OCR 116

4.7: Result Improvement of Proposed Algorithm using ANN based OCR 116

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LIST OF ABBREVIATIONS ADT Android Development Tool

ALPR Automatic License Plate Recognition

ANN Artificial Neural Network

ANPR Automatic Number Plate Recognition

API Application Programming Interface

AVD Android Virtual Device

AVI Automatic Vehicle Identification

BPP Bits per Pixel

BSD Berkeley Software Distribution

CJK Chinese, Japanese, and Korean

CNN Convolution Neural Network

CPR Car Plate Recognition

DDMS Dalvik Debug Monitoring Service

DPI Dot Per Inch

FFNN Feed Forward Neural Network

GNU GNU is Not Unix

GPL General Public License

GPS Global Positioning System

GUI Graphical User Interface

HMM Hidden Markov Model

HP Hewlett-Packard

HTML Hyper Text Markup Language

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IBM International Business Machinery

IDE Integrated Development Environment

Java EE Java Enterprise Edition

Java ME Java Micro Edition

Java SE Java Standard Edition

JDK Java Development Kit

JIT Just In Time

JPJ Jabatan Pengangkutan Jalan Malaysia

JVM Java Virtual Machine

LPR License Plate Recognition

MLP Multilayer Perceptron

MLP NN Multi-Layer Perceptron Neural Network

NTSC National Television System Committee

OCR Optical Character Recognition

OS Operating System

OTI Object Technology International

PC Personal Computer

RAM Random Access Memory

RGB Red Green Blue

ROI Region of Interest

SDK Software Development Kit

SMS Short Messaging Service

SVM Support Vector Machine

UI User Interface

xviii

LIST OF SYMBOLS ωk The zeroth-order cumulative moment of the kth ɵ Threshold

μk The first-order cumulative moment of the kth φ(.) Activation function

2B

The between-class variance of the thresholded image

1

CHAPTER 1

INTRODUCTION

1.1 BACKGROUND

Due to the increasing number of vehicle nowadays, the modern city needs to establish

the effective and efficient automatic traffic system for the management of the traffic

law enforcement. Automatic number plate recognition (ANPR) leads the significant

role in this condition. The ANPR is an image processing technique to extract the

image of license plate on vehicle taken by digital camera or taken by either a color or

a grayscale digital camera, as well as an infrared camera in order to identify the

vehicles using their number plate through optical character recognition (OCR) (Badr

et al., 2011).

The ANPR system recognizes characters on the license plate through the

combination of various techniques and algorithms, namely image pre-processing,

object detection, and character recognition. The ANPR system consists of a camera to

detect the number plate object and processing unit to process and extract the

characters and interpret the pixels into numerically readable characters (Lakshmi et

al., 2011). Furthermore, it became much exciting in the last decade along with the

improvement of digital camera technology and the computational processing (Qadri &

Asif, 2009).

Nowadays, the ANPR system has been used in traffic law enforcement,

including speed prosecution, stolen car detection and border monitoring. It can be

applied also for building management, such as parking lots and gate control (Hamey

& Priest, 2005). Many researchers have proposed and published various methods and

CHAPTER ONE

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algorithms on ANPR. ANPR system commonly deployed one of two basic methods:

one accomplished the entire process real-time at the track location and the other

transmits all the images from many paths to a remote computer location then performs

the OCR process at some later point in time. For the process at the track location, it

requires high-resolution camera to capture the number plate image. In the transmitting

and processing at server, it needs a reliable network and a large number of PCs to

handle high workload, high bandwidth connection and processing the number plate

image.

Motivated by the requirement of high-resolution camera, reliable network and

costly PCs, the author proposes enhancing algorithm for Automatic Number Plate

Recognition (ANPR) to be implemented in the low-resolution camera available and

low processing power on mobile phone. The enhancing algorithm consists of

improvement on image pre-processing, inclusion of GPS tagging and intelligent

character recognition based on template matching and artificial neural network (ANN)

to improve the accuracy. In this research, as few constraints which is possible on the

working environment are considered as highlighted in the next section.

1.2 PROBLEM STATEMENT AND ITS SIGNIFICANCE

Currently, the model, design and analysis of ANPR on mobile devices such as Android

devices are a poorly studied area compared to ANPR implementation on a PC which

has been studied for many years. Moreover, there were not many researches have

focused on the limited processing power device, i.e. mobile phone. The ANPR is also

implemented in various applications, such as tracking of stolen cars, traffic violations

(speed trap) and parking management, then again still with some limitations.

3

Due to its portability and practically, the demand on ANPR implementation on

Android will grow tremendously. The challenges in design and implementation of

ANPR in Android mobile phone include lower image resolution, less memory, and

less processing power compare to a PC.

Various difficulties possibly occur in ANPR because the variety format of

number plate in Malaysia, which has one row and two rows format. Another difficulty

of ANPR is the image quality that produced by a low-resolution camera with the

addition of the weather conditions, lighting condition which may cause poor contrast,

shadows and reflection. The last difficulty is the implementation of ANPR on Android

mobile phone with a low-resolution camera, limited memory and limited processing

power.

In order to overcome those difficulties, it is necessary to enhance the algorithm

of ANPR, which must has the capability to improve the image quality, reduce the

computational cost and increase the number plate recognition.

1.3 RESEARCH OBJECTIVES

The principal objective of this research is to design, develop and implement an

effective ANPR algorithm on Android mobile phone devices with high accuracy and

low computational complexity. This broad objective expressed in a number of aims:

1. To design the ANPR application that suitable for the Android mobile

phone with limited camera resolution, in terms of dpi, and limited

processing power.

2. To develop and implement an efficient and capable ANPR algorithm for

Android mobile.

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3. To evaluate the performance of proposed algorithm and compare to

existing ANPR algorithms in terms of accuracy and processing time.

1.4 RESEARCH METHODOLOGY

In order to accomplish the objectives of the research work, the following tasks are

conducted.

1. This is research started by doing some literature study of various ANPR

algorithms. The study covers the basic theory of number plate recognition,

Android platforms, and the existing algorithms, which has already

implemented, including merit and demerit of the existing implemented

system.

2. Define the limitations, state some parameters and variables of the research.

3. Design and develop ANPR by analysing and comparing the capability of

existing systems and propose an algorithm that can be implemented on

Android mobile phone platforms. The analysing and comparing step are

done after deploying the ANPR system into Android mobile phone to find

out the best option and achieve a suitable and capable algorithm.

4. Implement the proposed algorithm and existing algorithms on actual

Android mobile phones.

5. Evaluate the performance and compare the results of the proposed

algorithm to the common existing algorithms for ANPR.

1.5 RESEARCH SCOPE

The scope of this research covers the analysis of ANPR algorithms. The

implementation of propose ANPR algorithm on certain Android mobile phone has

5

been performed. Comparison and evaluation for common and propose ANPR

algorithms using template matching based OCR and ANN based OCR are conducted.

Focus of comparison and evaluation based on accuracy and processing time for

Malaysia vehicle number plate within short capture distance to the vehicle number

plate.

1.6 THESIS OUTLINES

This thesis is organized in five chapters. Chapter 1 discuss and explains background of

the research, problem statement and research significance, objectives, methodology

and research scope.

Chapter 2 presents the literature review of Malaysia vehicle number plate,

ANPR algorithms, Android mobile operating system with its capabilities, application

library that support the ANPR implementation on Android mobile phone and

performance evaluation metric which is applied in this research.

Chapter 3 describes ANPR system development including hardware and

software environment developments; system design including graphical user interface

(GUI) design, process design and database design; proposed algorithm including

image acquisition, image pre-processing, character segmentation and OCR; and

finally the implementation of GUI, code construction and compiling and deploying the

proposed ANPR algorithm.

Chapter 4 evaluates the results of proposed ANPR algorithm including

experimental data, development of proposed ANPR algorithm, experimental scenario,

original and proposed ANPR algorithm results in terms of accuracy and processing

time, and error analysis.

6

Chapter 5 concludes the thesis by revising the lessons learned for this study

and some recommendations are elaborated for the future research of the ANPR on

Android mobile device.