LICENSE PLATE RECOGNITION OF MOVING VEHICLES
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LICENSE PLATE RECOGNITION OF MOVING VEHICLES
Siti Rahimah Binti Abd Rahim
Bachelor of Engineering with Honors
(Electronics & Computer Engineering)
2009/2010
UNIVERSITI MALAYSIA SARAWAK
R13a
BORANG PENGESAHAN STATUS TESIS
Judul: LICENSE PLATE RECOGNITION OF MOVING VEHICLES
SESI PENGAJIAN: 2009/2010
Saya SITI RAHIMAH BINTI ABD RAHIM
(HURUF BESAR)
mengaku membenarkan tesis * ini disimpan di Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak
dengan syarat-syarat kegunaan seperti berikut:
1. Tesis adalah hakmilik Universiti Malaysia Sarawak.
2. Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dibenarkan membuat salinan untuk
tujuan pengajian sahaja.
3. Membuat pendigitan untuk membangunkan Pangkalan Data Kandungan Tempatan.
4. Pusat Khidmat Maklumat Akademik, Universiti Malaysia Sarawak dibenarkan membuat salinan tesis ini
sebagai bahan pertukaran antara institusi pengajian tinggi.
5. ** Sila tandakan ( ) di kotak yang berkenaan
SULIT (Mengandungi maklumat yang berdarjah keselamatan atau kepentingan
Malaysia seperti yang termaktub di dalam AKTA RAHSIA RASMI 1972).
TERHAD (Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/
badan di mana penyelidikan dijalankan).
TIDAK TERHAD
Disahkan oleh
(TANDATANGAN PENULIS) (TANDATANGAN PENYELIA)
Alamat tetap: NO. 1, JALAN 1, TAMAN
BATU 30,
44300 BATANG KALI, DR. MOHD SAUFEE BIN MUHAMMAD
Nama Penyelia
SELANGOR DARUL EHSAN.
Tarikh: Tarikh:
CATATAN * Tesis dimaksudkan sebagai tesis bagi Ijazah Doktor Falsafah, Sarjana dan Sarjana Muda.
** Jika tesis ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi
berkenaan dengan menyatakan sekali sebab dan tempoh tesis ini perlu dikelaskan sebagai
SULIT dan TERHAD.
Final Year Project attached here:
Title: License Plate Recognition of Moving Vehicles
Author Name: Siti Rahimah Binti Abd Rahim
Metric Number: 17312
Is hereby read and approved by:
__________________________ _________________________
Dr. Mohd Saufee Bin Muhammad Date
Project Supervisor
LICENSE PLATE RECOGNITION OF MOVING VEHICLES
SITI RAHIMAH BINTI ABD RAHIM
Thesis is submitted to
Faculty of Engineering, University Malaysia Sarawak
in Partial Fulfillment of the Requirements
for Degree of Bachelor of Engineering with Honors
(Electronics & Computer) 2009/2010
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Dedicate this dissertation to my lovely parents, Abd Rahim Bin Mohd Ali and
SitiSakniahBintiSarman and my siblings AbdRahiman Bin Abd Rahim and
SitiRaihaniahBintiAbd Rahim for their love and being supportive family for me.
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ACKNOWLEDGEMENTS
My sincerest appreciation extended to my supervisor, Dr. Mohd Saufee bin
Muhammad for guidance, support, advices, comments and suggestions throughout
the process of completing this project.
I would like to thank the Faculty of Engineering lecturers for the guidance
given for pursuing engineering knowledge and skills during my years here. I express
my thank to the Final Year Project coordinator, Madam Ade Syahida Wani for the
information’s and guidance’s to complete the project report as the format that has
been explained during Semester 1.
A special thank goes to my family and friends for the supports, guidance’s,
comments, suggestions and encouragements to complete this project. Special thanks
go those who are that upload their project on the internet or blog because these
information’s help a lot for the completion of the project.
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ABSTRAK
Pengecaman corak telah diketahui oleh ramai penganalisis sebagai satu
aplikasi dalam rangkaian neural. Ramai penyelidik memilih tajuk ini sebagai bahan
penyelidikan samada pengecaman huruf dan lain-lain corak. Pengecaman huruf
adalah satu aplikasi yang paling terkenal dalam pengecaman corak samada huruf
tulisan tangan atau lain-lain seperti huruf Arab and China. Projek ini melibatkan
pengecaman huruf dari pendaftaran kenderaan sewaktu kenderaan sedang bergerak di
atas jalan raya. Pendaftaran kenderaan mempunyai dua jenis huruf iaitu abjad dan
nombor. Huruf dari pendaftaran kenderaan dapat dikecam dengan mengunakan
teknik pemprocessan gambar dan aplikasi rangkaian neural yang terdapat dalam
perisian MATLAB. Projek ini mempunyai dua bahagian, dimana gambar kenderaan
akan diproses mengunakan teknik pemprosesan gambar dan kemudian akan dikecam
mengunakan rangkaian neural. Pengecaman huruf dari pendaftaran kenderaan akan
dikecam mengikut sasaran yang telah ditentukan. Seterusnya, perbandingan diantara
50 dan 100 neurons lapisan tersembunyi dilaksanakan untuk mengenalpasti
pengecaman huruf yang terbaik. Pada peringkat akhir projek, pengenalpastian huruf
akan dibentang dan dibincangkan.
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ABSTRACT
Pattern recognition has been identified by researchers as one of the neural
network applications. There are many researches on this topic whether it character
recognition or other pattern. The famous application in pattern recognition is the
character recognition whether it handwritten recognition or others such as Arabic and
Chinese character. In this project, the character recognition is for moving vehicles
where character from license plate of moving vehicles will be recognized. License
plate character consists of alphabet and number. Incorporated with image processing
and neural network toolbox, this simulation will be design using the MATLAB
toolbox. This project consists of two parts where the image will be process in image
processing part while the character will be recognized using the backpropagation
neural network. The character recognition will be recognizing according to the target
output. In addition, performing recognition simulations compare between 50 and 100
neuron of hidden layer for the best character recognition. At the end of this project
the recognized character from the license plate will be presented.
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TABLE OF CONTENTS
Pages
Dedication ii
Acknowledgement iii
Abstrak iv
Abstract v
Table of Contents
vi
List of Tables x
List of Figures xi
List of Abbreviations xiii
Chapter 1
INTRODUCTION
1.1 Background
1.2 Project Objectives
1.3 Statement of Expected Problems
1.4 Proposed Solutions
1.5 Expected Outcomes
1.6 Report Outlines
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3
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4
5
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Chapter 2 LITERATURE REVIEW
2.1 License Plate Recognition System
2.2 Image Preprocessing
2.3 Image Enhancement
2.3.1 Contrast Manipulation
2.3.2 Histogram Manipulation
2.4 Image Enhancement using Filters
2.4.1 Sharpening Filter in Spatial Domain
2.4.2 Sharpening Filter in Frequency Domain
2.5 Image Segmentation
2.6 Feature Extraction
2.7 Neural Network
2.8 Artificial Neural Network
2.8.1 Feed-Forward Neural Network
2.8.2 Recurrent Neural Network
2.9 Back-propagation Neural Network
2.10 Training Algorithm
2.10.1 Supervised Training
2.10.2 Unsupervised Training
2.11 Introduction to MATLAB
2.11.1 What is MATLAB?
2.11.2 The MATLAB System
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Chapter 3 METHODOLOGY
3.1 Character Recognition Tools
3.2 Flow Chart of Character Recognition
3.3 Image Acquisition
3.4 Image Preprocessing
3.4.1 Image Cropping
3.4.2 Grayscale Conversion
3.4.3 Image Enhancement
3.5 Neural Network in Character Recognition
3.5.1 Network Creation
3.5.2 Network Initialization
3.5.3 Network Training
3.5.4 Network Simulation
3.5.5 Network Performance
3.6 Training Characters
3.7 Gradient Descent with Momentum BP (traingdm)
3.8 Training Object
3.9 Network Properties
3.10 Testing Set Images
3.11 Implementation of Graphical User Interface (GUI)
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Chapter 4 RESULTS AND DISCUSSIONS
4.1 Image Processing Process
4.1.1 Step On Image Processing
4.2 GUI Results
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4.3 Result Discussions
4.4 Number of Neuron in Hidden Layer
4.5 Number of Epochs
4.6 Learning Rate
4.7 Project Discussions
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Chapter 5
CONCLUSSIONS AND RECOMMENDATIONS
5.1 Project Achievements
5.2 Recommendation for Future Works
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REFERENCES
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APPENDIX
APPENDIX A
APPENDIX B
APPENDIX C
APPENDIX D
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LIST OF TABLES
Tables
Pages
3.1 Training Character with the Targets 45
3.2 Network Properties 47
4.1 Characters Result with Respective Compet Function
Answer
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xi
LIST OF FIGURES
Figures
Pages
1.1 Standard Configuration of Malaysia License Plates 2
2.1 Gray Level Transformation for Contrast
Enhancement
10
2.2 Gray Level Slicing Transformation 11
2.3 Result Image of using Technique in Figure 2.2 (a) 11
2.4 The Eight Bit Planes of the Eight Bit Image 12
2.5
High and Bright Intensity Image with Histogram 14
2.6 Low-contrast and High-contrast Image with
Histogram
14
2.7 Result Of Histogram Equalization And Their
Histogram
15
2.8 Result Images of Ideal Highpass Filter 19
2.9 Extracting the Character from License Plate 20
2.10 License Plate Character 21
2.11 Feature Extracted using Kirsch Edge Detection 22
2.12 Multilayer Net Architecture 23
2.13 Feed-Forward Neural Network (Single Hidden
Layer)
25
2.14 Multilayer Feed-Forward Neural Network 25
2.15 Basic Feedback Structure 26
xii
2.16 Back-propagation Net
27
3.1 Flow Chart of Character Recognition 34
3.2 Image Preprocessing Process 36
3.3 Converting RGB to Grayscale Image 37
3.4 Neural Network Design 39
3.5 Neural Network Training Windows 43
3.6 License Plate Recognition GUI 48
4.1 Image of Moving Vehicles
50
4.2 Load Image into GUI 51
4.3 License Plate Image after Extraction 52
4.4 Image of Seven License Plate Character
52
4.5 Convert into Binary Image 53
4.6 Load Image for Neural Network Test 53
4.7 Recognized Character 54
4.8 Uncompleted Recognize Characters 54
4.9 License Plate with Segmentation Problem 57
xiii
LIST OF ABBREVIATIONS
ANN Artificial Neural Network
BP Back-propagation
DSLR Digital Single-lens Reflex Camera
FYP Final Year Project
MATLAB Matrix Laboratory
NN Neural Network
RGB Red Green Blue
UNIMAS University Malaysia Sarawak
GUI Graphical User Interface
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CHAPTER 1
INTRODUCTION
1.1 Background
Vehicles license plate recognition is one of the important techniques that can
be used for identification of vehicles around the world. It useful in many applications
such as entrance admission, security, parking control, road traffic control, speed
control and so on [1]. This project entitled “License Plate Recognition of Moving
Vehicles” is a system that will be developed to recognize the license plate characters
in various speeds and conditions of moving vehicles. This project consists of
simulation program to recognize license plate characters where a captured image of
moving vehicles will be the input. The image will then be processed and analyzed
using image processing and neural network techniques. Based on network
performance error calculated from neural network output and target output will
determined whether the neural network recognize the input as the target. Figure 1.1
shows the standard layout configurations of license plates for Malaysian private
vehicles [1].
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Figure 1.1: Standard Configuration of Malaysia License Plates
1.2 Project Objectives
The project objectives of designing the software for license plate recognition
as has been discussed with the supervisor have been identified, as follows:-.
1. Develop the coding to loading image and neural network training to
recognizing the character from license plate of moving vehicles.
2. Develop the coding that can extract the character from single line
pattern with seven character of license plate in Malaysia particularly
Sarawak state.
3. Develop the coding for image processing process and neural network
training.
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1.3 Statement of Expected Problems
The main expected problem that will be encountered in this project is blurred
image that produce when capturing moving vehicles license plate image using
normal camera. This problem occurs due to slow camera speed as compared to the
moving vehicles. The blurred image capture also occurs when capturing image
during the different conditions weather such as raining, sunshine, night and cloudy.
Blurred image problem is basically causes by the hardware part of this project.
The similarity of some alphabet and number patterns are also foreseen to the
problems occur in this project. The alphabet and number that might be similar are
misinterpreted by developed software are “1” with “7”, “2” with “Z” and “8” with
“B”. This may causes error where incorrect results are displayed by the simulation
software developed
1.4 Proposed Solutions
The solution on blurred image can be solved by using the appropriate camera
that suitable for capture the moving image such as high speed camera. Although this
camera speed can capture the freeze moving image, it is still incapable of solving the
blurred image. The images will still certain same blurred edges. Therefore, the image
must undergo some image processing technique using MATLAB software to remove
the blur edges. After this process, the image obtain will be used to undergo the next
part for character recognition.
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The neural network approach will recognize each character that has been
extract from image, where it will solve the misinterpreted character problem. The
solution on recognizing the similar character will be also solved using neural
network approach where the best chosen learning pattern identified will be used for
this problem. There are a lot of neural network types can be chosen for better
accuracy on recognizing the character. Large amount of neural network training in
MATLAB software toolbox will give more understanding on this approach and
increases the ability of this project to recognize the character correctly.
1.5 Expected Outcomes
This FYP project is based on simulation program develop using MATLAB.
However, this project still employs hardware system on the image acquisition part.
This project consist the hardware used to capture the moving vehicles image and the
simulation program used to do the image preprocessing and recognize the license
plate character based on target output given in neural network training process. This
simulation program will be developed using MATLAB software. Image
preprocessing consist the process of image enhancement, filtering the noise and
extracts the character from license plate where neural network part consists of
process to recognize each license plate character based on error calculation between
network output and target output. Video camera is used to capture the moving image
and convert it into image frame.
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1.6 Report Outlines
The report outline contains the undergoing chapter of the final year project
report. Chapter 1 starts with introduction of the project, benefits and also the aims
and objective of the project. This chapter also gives explanation on the statement of
problem together with the proposed solution on each problem and project outline that
has been followed while undergoing the final year project.
Chapter 2 is the literature review where it summarizes the recent research and
scholarly sources relevant on the particular issue and theory in this project. This
chapter also summarizes the particular of theory on simulation approach that
connected with this project. In this context, the research on license plate recognition
using another approach and the explanation on simulation approach such as image
processing part will be discusses.
Chapter 3 is methodology which summarize about the method that will be
used in this project to obtain the result. In this project the method that will be used in
recognizing the license plate character is image processing approach and neural
network simulation using MATLAB.
Chapter 4 explains the result and discussion from this project. The result
represent in from of network performance graph, the network simulation result from
the network training and the discussion on recognition result.
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Chapter 5 concludes the report summary of the finding obtain though out the
whole FYP project. The conclusion on work experience and work effort done to meet
the requirement on this project development has been told here. The future work on
improve this project and recommendation on new title research that similar with the
project also been suggest here.
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CHAPTER 2
LITERATURE REVIEW
2.1 License Plate Recognition System
License Plate Recognition of Moving Vehicles is based on image processing
and neural network where image processing techniques such as edge detection,
thresholding and re-sampling has been used to locate and isolate the license plate and
the characters. The neural network was used for successful recognition the license
plate number [2]. There are many researches on this project title where they using a
different method for license plate character recognition [1, 2, 3].
Among the thesis on license plate recognition system titles Vehicles license
plate character recognition by neural network by M. Khalid et. al. [1], Smart License
Plate Recognition System based on Image Processing using neural network by
V. Koval et. al. [2] and car license plate recognition with neural network and fuzzy
logic by J.A.G Nijhuis et. al. [3]. Some of the method that they used is almost same
especially on the image preprocessing, image segmentation and also use the
grayscale image.
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2.2 Image Preprocessing
Image preprocessing is an important process which it used to manipulate the
images for character recognition operation. The image preprocessing applies some
standard image processing technique such as contrast stretching and noise filtering to
enhance the quality of the image [3]. Capturing image of moving object will produce
the blurred image, using the computer algorithm; image will be preprocessing to
improve the quality to allow the character in the images to be recognized. In image
preprocessing, color image (RGB) acquired by a digital camera is converted to gray-
scale image based on the RGB to gray-scale conversion technique. The basic idea of
this conversion is performed by eliminating the hue and saturation information while
retaining the luminance. Equation (2.1) shows an optimal method for RGB to gray-
scale conversion [4].
(2.1)
where
Lu is luminance
R refers to red components
G refers to green components
B refers to blue components
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