Intelligent automatic car park system.
LICENSE PLATE RECOG t ITIO
SYSTEM USING VlDEO AND 1MAGE
MAZREE BfN IBRA HIM
MAASPALIZA BfNTI AZRI
AZR ITA BfNTI ALIAS
SALEHA 81 Tl MO HAMAD SALLE H
RESEARC H VOTE:
PJP/2006/FKE( 12)S21 0
FAKULT I KEJURUTERAAN ELEKTRIK
UNIVERSITI TEKN IKAL MA LAYS IA MELAKA
2009
II
ACKNOWLEGDMENT
First of all. I would like to thanks to our s ponsor UTeM. second ly to our dean.
Engr. Profesor Dr. Marizan bin Sulaiman and deputy dean (research) Dr. Hamzah bin
Sakidin, and my head of department, Mr Hyre il Anuar bin Kasdirin, my co-researchers.
take thi s opportunity to extend my deepest gratitude to a ll those who have assisted me in
making this project and report successfull y.
My sincere thanks a lso to all those who have assisted me in making this project
and report successfull y.
Say ing thank you is surely inadequate. yet most appropriate at this juncture. To
the above menti oned. thi s report seals the author' s gratitude to you a ll.
Ill
ABSTRACT
The purpose of this project was to build a rea l time application which recognizes
license plates from cars at gate, for example at entrance of a parking area. The
S)
ste m.
based on regular PC vvith video camera. catches video frames whic h include a vi sible car
license plate a nd processes them . Once a license pl ate is detected. its digi ts a re
recognized. displayed on the User Interface or checked aga inst database. The focus is on
the design of a lgorithms used for extracting the license plate from a s in gle image.
isolating the characters of the plate a nd identifying the indiv idua l character. Image
sample of vehicle' s license plate is acquisited by video camera. then. it is converted into
Black and Whi te (BW). Artific ia l
eura l Netwo rk (ANN) is applied to recogni ze the
c haracter' s plate. Learning method of the ANN is back propagation and a ll process is
handled by Matlab version R2006a 7. I.
I\
ABSTRAK
Projek ini dijalankan adalah bertujuan untuk mengaplikasikan kepada suasana
ataupun kepada keadaan yang sebenar iaitu mengenai pengenalpastian nombor
pendaftaran kenderaan, contohnya seperti pada pintu masuk di kawasan tempat letak
kereta . Sistem ini menggunakan aplikasi komputer dan juga kamera video yang mana
akan menangkap gambar, iaitu gambar nombor pendaftaran kenderaan dan akan
memproseskannya. Apabila nombor pendaftaran kenderaan telah dikesan. digit-digit
yang ada pada nombor pendaftaran kenderaan tersebut akan dikenalpasti. dan
dipaparkan pada User Interface ataupun membuat pemeriksaan pada database. Focus
projek ini adalah untuk mereka algoritme untuk ··extracting.. nombor pendaftaran
kenderaan kepada imej yang lebih mudah untuk dikenalpasti , memisahkan semua ciriciri pada nombor pendaftaran dan mengenalpasti setiap abjad dan nom bor.
ombor
pendaftaran kenderaan ini akan ditangkap dengan kamera video. dan selepas itu. akan
ditukarkan kepada wama hitam dan putih. Jaringan Neural digunakan untuk
mengenalpasti semua cirri-ciri yang ada pada nombor pendaftaran kenderaan. Kaedah
pembelajaran di dalam Jaringan Neural adalah .. back propagation .. dan semua proses
akan menggunakan Matlab versi R2006a 7.1.
CONTENTS
CHAPTER
CONTENTS
PAGE
PROJECT TITLE
I
II
ACKNOWLEDGEMENT
II
ABSTRACT
I II-IV
CONTENTS
V- V III
LIST OF APPENDIX
VIII
LIST OFTABLE
IX
LIST OF FIGURE
X
LIST OF ABBREVIATION
XI
INTRODUCTION
1.0
Introduction
1.1
Objective
2
1.2
Problem Statement
2
1.3
Scope Project
3
LITERATURE REVIEW
2. 1
Purpose statement for Neu ra l Network approach
4
2.2
Artificia l Neural Netvvork
4-5
2.3
Recognition
5-6
2.4
Preprocessing/Image Extraction
6-7
III
IV
THEORY
3. 1
Chapter Overvie\\
8
3.2
Malaysian Vehicle Licen se Plate
8
.) .)
.., ."''
Basic design of license plate
9
3.4
Software R2006a
9-1 0
3.5
De finition of Image Processing
10
3.6
What is Neural
11-1 2
etwork
3.6. 1
Multilayer Perceptron (M LP)
12-1 3
3.6.2
Neura l
13
3.6.3
Neural Netvvork Architecture
et\ ork e lement
14
METHODOLOGY
4. 1
Chapter Overview
15
4.2
Development of program codes
15
4.3
Capturing image
16
4.3. 1
GU I (Graph ical User Inte rface)
17- 18
for capturing image
4.3.2
Video Setup
18- 19
4.3.3
Previewing video
20
4.3.4
Capturing images
20
4.4
Recognizing character on the car pl ate
20
4.5
Simulink
21
4 .5. 1
Part I: Image acqui sition and object
21 -24
Recogniti on
4.5.2
Part 2 : Design the progression of the system 24-25
4.5.3
Part 3: Description ofthe implementation
26
4.5.3. 1 License plate extracti on
26
4.5.3.2 Black region extraction
26
V II
4.5 .3.3 Dil ating black regions
27
4.5.3.4 Determining the ang le of the plate
27
4.5.3.5 Im proved license pl ate region
28
4.5.3.6 License Pl ate Crop
28
4.5.3.7 License Pl ate Quantization and
29
Equalization/ converts RGB image to B W
4.5.4
4.6
Part 4: Developed Simulink .model
29-30
Neural Network
31
4.6. 1
31
Part I : Design character recogn ition of
alphabet and number
4.6.2
4 .6.3
Part 2: Train ing and recognize the cha racter 31 -33
4.6.2. 1 Initialization
33
4.6.2.2 Training
33
4.6.2.3 Training w itho ut noise
34
4.6.2.4 Training w ith noise
34
Part 3: Training aNN for classification
35
4.6.3. 1 Read the image
35
4.6.3.2 Image Preprocessi ng
35
4.6.3.3 Creating Vectors data for the
35
Neural Network (objects)
4.6.3.4 Creating and tra inin g of the
36
Neural Netv ork
4.6.3.5 Testing the Neura l Network
36
4 .7 Using GU I (G raphical User Interface)
37-42
4.8 Assemble
42
4.9 Gantt C hart
43
Ill
V
RESULT
5.1
Capturing image using \\eb camera
44-45
5.2
Result from RGB image
45-46
5.3
Neural
5.4
Noisy letter A to Z and the network
etwork
46
47-54
recognize the character
5.5
5.6
Error for network to recognize
oisy letter I to 9 and the net\ ork
55
56-58
recog nized the charac ter
5.7
VI
VII
GU l
59-63
DISCUSSION AND SUGGESTION
6.1
Discussion and Conclusion
64
6.2
Suggestion
65
CONCLUSION
7.1
Conclusion
REFERENCES
66
67-68
APPENDIX A
CAPTURING IMAGE
69
APPENDIX B
NEURAL NETWORK
73
APPENDIX C
GUT
84
IX
LIST OFT ABLE
NO.
TITLE
PAGE
4. 1
Progress of the Proj ect
43
LIST OF FIG URE
NO.
TITLE
PAGE
2. 1
Some graphics of transfer function
5
2.2
Extraction of a character in a matrix
7
2.3
Identification of background and character.
7
3. 1
A graph ical representation ofan MLP
12
3.2
Training process
13
3.3
Neu ral Network arch itecture
14
4. 1
Block diagram show 2 methods in thi s project
15
4.2
Block diagram shO\ sequence method for capturing image. 16
4.3
Command wi ndow
17
4.4
Guide Qu ick Start
17
4.5
Create fi gure using button
18
4.6
Create fi gure using 3 pushbut1on and I axes
18
4.7
Command window for adaptor
19
4.8
Block diagram show the sequence method of recognizing
20
4.9
Block diagram show the sequence method ofSimu link
21
4.10
Image Acquisition block-mask parameters
22
4.1 1
Co lor Space Conversion block-mask parameter
23
4.12
Autothreshold block parameter
24
4. 13
Block diagram of the system
24
4.14
Block diagram of license plate extraction and character
Segmentation
25
4.15
Example of captured frame
26
4.16
Captured frame with black region dil ated
27
4. 17
Determin ing the angle of the plate
27
XI
4.18
Improved License Plate region
28
4. 19
Li cense Plate Crop
28
4.20
Gray scale input license plate
29
4.2 1
Overall Simu link Program
30
4.22
Overall Program for Neural etwork
31
4.23
A lphabet A w ithou t noise
32
4.24
A lphabet A w ith noise
32
4.25
Block diagram of the sequence method using GUI
37
4.26
Des ign the dra\ ing using axes. panel button and
pushbutton
38
4.27
Save and Run the M- file
39
4.28
Block diagram of the sequence method using
pushbutton load
4.29
Block d iagram of the sequence method using
pushbutton select.
4.30
41
Block diagram of the sequence method using
pushbutton extract
4.32
40
Block diagram of the sequence method using
pushbutton pre-process
4.3 1
39
41
Block diagram of the sequence method using
pushbutton recognize
42
5.1
Setup the adapter using pushbutton video setup
44
5.2
C hoose the best pixel
44
5.3
Capture image usi ng pushbutton snap
45
5.4
Video Viewer
45
5.5
Video Viewer I
45
5.6
Video Viewer 2
46
5.7
Video Viewer 3
46
5.8
Training without noi se
46
XII
5.9
Trai ning " ith noise
46
5. 10
Alphabet A
47
5. I I
Alphabet B
47
5. 12
Alphabet C
47
5. 13
Alphabet D
48
5. 14
Alphabet E
48
5. 15
Alp habet F
48
5.1 6
Alphabet G
49
5. I 7
Alphabet H
49
5. 18
Alphabet I
49
5. 19
Alphabet J
50
5.20
Alphabet K
50
5.2 1
Alphabet L
50
5.22
Alphabet M
51
5.23
Alphabet N
51
5.24
Alphabet 0
51
5.25
Alphabet P
52
5.26
Alphabet Q
52
5.27
Alphabet R
52
5.28
Alphabet S
53
5.29
Alphabet V
53
5.30
Alphabet W
53
5.3 1
Alphabet X
54
5.32
Alphabet Y
54
5.33
Alphabet T
55
5.34
Alphabet U
55
5.35
Alphabet Z
55
5.36
Alphabet I
56
5.37
Alphabet 2
56
XI II
5.38
Alphabet 3
56
5.39
Alphabet 4
57
5.40
A lphabet 5
57
5.41
A lphabet 6
57
5.42
A lphabet 7
58
5.43
A lphabet 8
58
5.44
Alphabet 9
58
5.45
Chose image
59
5.46
Load image
59
5.47
Select c haracter
59
5.48
Pre-Process the image
60
5.49
Ex tract the image us ing Neural Network
60
5.50
Recognize number 0
60
5.5 1
Recognize number I
61
5.52
Recognize number 2
61
5.53
Recognize number 3
61
5.54
Recognize number 4
62
5.55
Recognize number 5
62
5.56
Recognize number 6
62
5.57
Recognize number 7
63
5.58
Recognize number 8
63
5.59
Recognize number 9
63
XI
LIST OF ABBREVIATION
1. NN
= Neural Network
2.ANN
=Artificial Neural Network
3. GUI
= Graphical User Interface
4. LPR
=License Plate Recognition System
5.MLP
= Multilayer Perceptron
CHAPTER I
INTRODUCTION
1.0
Introduction
The number of vehicle in metropolitan cities is always growth. The growth
has a positive implication to provide a professional and sufficient parking system.
This fact requires a plate number recognition system to support a parking
arrangement.
The aim of this final project is to develop Simulink Model of License Plate
Recognition system using Sirnulink, Video and Image Processing Block set R2006a
(software).
License Pate Recognition system (LPR) is an image-processing technology
used to identify vehicles by their license plates number. LPR project is to present a
real time car park contro l system. A video-based car park model will be built by
using Simulink of Video and Image Processing Block set to capture the license plate
number.
The proposed of this License Plate Recognition system (LPR) works,
whenever the vehicle approached the secured area (Entrance). The sensor senses the
car and its presence signaled to the LPR unit. After that, the LPR unit activates the
illumination (lnfrared) and takes pictures of the front plates from the LPR camera.
The images of the vehicle include the plate and the pixel information is read by the
LPR unit's image processing hardware. Then, the LPR unit checks if the vehicle
appears on a predefined list of authorized cars compare with the database, and if
found, it gives signals to open the gate. Next, the authorized vehicle enters into the
secured area. After passing the gate, its detector closes the gate. Now the system
waits for the next vehicle to approach the secured area. The same process will be
repeated when the car goes out of the parking lot.
2
1.1
Project Objectives
The objective of the project proposal:
I.
To develop Simulink Model of License Plate Recognition
System using Video and Image Processing Block set
(software).
1.2
2.
Capturing images (license plate number)
3.
Training character of the license plate number
4.
Recognized the character of the license plate number.
Problem Statements
Nowadays parking lots are becoming organic part of our everyday li fe. We
use the entrance, day by day when going to work, shop, a hotel, a restaurant, an
airport, a hospital etc. But, for people who are living in private area like in
condominium, they have to wait for guard to open the gate before cars can enter the
entrance. Parking our car, leaving it in safe, finding it there again. fast, comfortably
and easily, that's the goal. To reduce time. increase efficiency and security, that's an
important issue. License plate recognition system using video and images offers
flexibl e, low maintenance, yet effective and intelligent technology for car park
operation: ticketing, security, location services, etc.
With integration of license plate recognition system using video and images
into parking management systems in private area, it becomes possible to automate
vehicle entry to and exit from a car park or a secure zone and to use the recognized
registration number for inventory management. The usage of license plate
recognition with specially designed camera set, equipped and synchronized with a
Infra Red flash - provides quick and accurate identification of vehicles 24 hours a
day, 7 days a week, without human assistance or any driver co-operation. It is a
significant added value for comprehensive parking management systems.
3
1.3
Scope Project
The scope of this project using Simulink, Video and Image Processing Block
set R2006a (software) are:
I.
Used the system for private area o nly such as in condominium
2.
Capture the license plate recognition.
3.
Takes picture of the front plate
4.
Train the alphabet (A to Z) and number (0 to 9) using neural
network.
5.
Recognized the alphabet (A to Z) and number ( 0 to 9)
4
CHAPTER II
LITERATURE REVIEW
This chapter reviews existing project created to get an idea about the project
design, conception, specification and any information that related to improve the
project. In this chapter, some review about an Artificial Neural Network system that
proposed to fulfill this project will also be reported.
2.1
Purpose statement for Neural Network approach
Tim Klassen [ I] as mentioned in the introduction, this research will show online average Arabic character recognition rates above 80% and training recognition
rates above 90% using Neural Networks for feature extraction and classification w ith
multiple unconstrained 19 writers. Linear networks will be emphasized, where this
represents the lowest computational overhead during both training and recall , hence
suitable for the target application device.
2.2
Artificial Neural N etwork
Haris Al-Qodri Maarif, and Sar Sardy [2] mentioned that Artificial Neural
Network (ANN) is a network which imitate to human neural network with some
simple processors (units). Each unit has some local memory and connected
unidirectional to flow the numerical data. Those units are operated only at local data
and input. ANN has some components [3]:
5
I.
Neuron is a unit consists of input, weight, bias. adder and transfer
function.
2.
Weight defines the position of hyper plane in input space.
3.
Bias is used to change the position of hyper plane in order not to
across the zero hyperspace.
4.
Transfer functi on gives a threshold value to activate neuron. Some of
transfer functions are illustrated on Fig.2.1.
. ......
I
I
I
I
- - 1 - - - 1- - -1- - -
"
1
- - t-I
"
"
- -r -
I
Ｍ
SYSTEM USING VlDEO AND 1MAGE
MAZREE BfN IBRA HIM
MAASPALIZA BfNTI AZRI
AZR ITA BfNTI ALIAS
SALEHA 81 Tl MO HAMAD SALLE H
RESEARC H VOTE:
PJP/2006/FKE( 12)S21 0
FAKULT I KEJURUTERAAN ELEKTRIK
UNIVERSITI TEKN IKAL MA LAYS IA MELAKA
2009
II
ACKNOWLEGDMENT
First of all. I would like to thanks to our s ponsor UTeM. second ly to our dean.
Engr. Profesor Dr. Marizan bin Sulaiman and deputy dean (research) Dr. Hamzah bin
Sakidin, and my head of department, Mr Hyre il Anuar bin Kasdirin, my co-researchers.
take thi s opportunity to extend my deepest gratitude to a ll those who have assisted me in
making this project and report successfull y.
My sincere thanks a lso to all those who have assisted me in making this project
and report successfull y.
Say ing thank you is surely inadequate. yet most appropriate at this juncture. To
the above menti oned. thi s report seals the author' s gratitude to you a ll.
Ill
ABSTRACT
The purpose of this project was to build a rea l time application which recognizes
license plates from cars at gate, for example at entrance of a parking area. The
S)
ste m.
based on regular PC vvith video camera. catches video frames whic h include a vi sible car
license plate a nd processes them . Once a license pl ate is detected. its digi ts a re
recognized. displayed on the User Interface or checked aga inst database. The focus is on
the design of a lgorithms used for extracting the license plate from a s in gle image.
isolating the characters of the plate a nd identifying the indiv idua l character. Image
sample of vehicle' s license plate is acquisited by video camera. then. it is converted into
Black and Whi te (BW). Artific ia l
eura l Netwo rk (ANN) is applied to recogni ze the
c haracter' s plate. Learning method of the ANN is back propagation and a ll process is
handled by Matlab version R2006a 7. I.
I\
ABSTRAK
Projek ini dijalankan adalah bertujuan untuk mengaplikasikan kepada suasana
ataupun kepada keadaan yang sebenar iaitu mengenai pengenalpastian nombor
pendaftaran kenderaan, contohnya seperti pada pintu masuk di kawasan tempat letak
kereta . Sistem ini menggunakan aplikasi komputer dan juga kamera video yang mana
akan menangkap gambar, iaitu gambar nombor pendaftaran kenderaan dan akan
memproseskannya. Apabila nombor pendaftaran kenderaan telah dikesan. digit-digit
yang ada pada nombor pendaftaran kenderaan tersebut akan dikenalpasti. dan
dipaparkan pada User Interface ataupun membuat pemeriksaan pada database. Focus
projek ini adalah untuk mereka algoritme untuk ··extracting.. nombor pendaftaran
kenderaan kepada imej yang lebih mudah untuk dikenalpasti , memisahkan semua ciriciri pada nombor pendaftaran dan mengenalpasti setiap abjad dan nom bor.
ombor
pendaftaran kenderaan ini akan ditangkap dengan kamera video. dan selepas itu. akan
ditukarkan kepada wama hitam dan putih. Jaringan Neural digunakan untuk
mengenalpasti semua cirri-ciri yang ada pada nombor pendaftaran kenderaan. Kaedah
pembelajaran di dalam Jaringan Neural adalah .. back propagation .. dan semua proses
akan menggunakan Matlab versi R2006a 7.1.
CONTENTS
CHAPTER
CONTENTS
PAGE
PROJECT TITLE
I
II
ACKNOWLEDGEMENT
II
ABSTRACT
I II-IV
CONTENTS
V- V III
LIST OF APPENDIX
VIII
LIST OFTABLE
IX
LIST OF FIGURE
X
LIST OF ABBREVIATION
XI
INTRODUCTION
1.0
Introduction
1.1
Objective
2
1.2
Problem Statement
2
1.3
Scope Project
3
LITERATURE REVIEW
2. 1
Purpose statement for Neu ra l Network approach
4
2.2
Artificia l Neural Netvvork
4-5
2.3
Recognition
5-6
2.4
Preprocessing/Image Extraction
6-7
III
IV
THEORY
3. 1
Chapter Overvie\\
8
3.2
Malaysian Vehicle Licen se Plate
8
.) .)
.., ."''
Basic design of license plate
9
3.4
Software R2006a
9-1 0
3.5
De finition of Image Processing
10
3.6
What is Neural
11-1 2
etwork
3.6. 1
Multilayer Perceptron (M LP)
12-1 3
3.6.2
Neura l
13
3.6.3
Neural Netvvork Architecture
et\ ork e lement
14
METHODOLOGY
4. 1
Chapter Overview
15
4.2
Development of program codes
15
4.3
Capturing image
16
4.3. 1
GU I (Graph ical User Inte rface)
17- 18
for capturing image
4.3.2
Video Setup
18- 19
4.3.3
Previewing video
20
4.3.4
Capturing images
20
4.4
Recognizing character on the car pl ate
20
4.5
Simulink
21
4 .5. 1
Part I: Image acqui sition and object
21 -24
Recogniti on
4.5.2
Part 2 : Design the progression of the system 24-25
4.5.3
Part 3: Description ofthe implementation
26
4.5.3. 1 License plate extracti on
26
4.5.3.2 Black region extraction
26
V II
4.5 .3.3 Dil ating black regions
27
4.5.3.4 Determining the ang le of the plate
27
4.5.3.5 Im proved license pl ate region
28
4.5.3.6 License Pl ate Crop
28
4.5.3.7 License Pl ate Quantization and
29
Equalization/ converts RGB image to B W
4.5.4
4.6
Part 4: Developed Simulink .model
29-30
Neural Network
31
4.6. 1
31
Part I : Design character recogn ition of
alphabet and number
4.6.2
4 .6.3
Part 2: Train ing and recognize the cha racter 31 -33
4.6.2. 1 Initialization
33
4.6.2.2 Training
33
4.6.2.3 Training w itho ut noise
34
4.6.2.4 Training w ith noise
34
Part 3: Training aNN for classification
35
4.6.3. 1 Read the image
35
4.6.3.2 Image Preprocessi ng
35
4.6.3.3 Creating Vectors data for the
35
Neural Network (objects)
4.6.3.4 Creating and tra inin g of the
36
Neural Netv ork
4.6.3.5 Testing the Neura l Network
36
4 .7 Using GU I (G raphical User Interface)
37-42
4.8 Assemble
42
4.9 Gantt C hart
43
Ill
V
RESULT
5.1
Capturing image using \\eb camera
44-45
5.2
Result from RGB image
45-46
5.3
Neural
5.4
Noisy letter A to Z and the network
etwork
46
47-54
recognize the character
5.5
5.6
Error for network to recognize
oisy letter I to 9 and the net\ ork
55
56-58
recog nized the charac ter
5.7
VI
VII
GU l
59-63
DISCUSSION AND SUGGESTION
6.1
Discussion and Conclusion
64
6.2
Suggestion
65
CONCLUSION
7.1
Conclusion
REFERENCES
66
67-68
APPENDIX A
CAPTURING IMAGE
69
APPENDIX B
NEURAL NETWORK
73
APPENDIX C
GUT
84
IX
LIST OFT ABLE
NO.
TITLE
PAGE
4. 1
Progress of the Proj ect
43
LIST OF FIG URE
NO.
TITLE
PAGE
2. 1
Some graphics of transfer function
5
2.2
Extraction of a character in a matrix
7
2.3
Identification of background and character.
7
3. 1
A graph ical representation ofan MLP
12
3.2
Training process
13
3.3
Neu ral Network arch itecture
14
4. 1
Block diagram show 2 methods in thi s project
15
4.2
Block diagram shO\ sequence method for capturing image. 16
4.3
Command wi ndow
17
4.4
Guide Qu ick Start
17
4.5
Create fi gure using button
18
4.6
Create fi gure using 3 pushbut1on and I axes
18
4.7
Command window for adaptor
19
4.8
Block diagram show the sequence method of recognizing
20
4.9
Block diagram show the sequence method ofSimu link
21
4.10
Image Acquisition block-mask parameters
22
4.1 1
Co lor Space Conversion block-mask parameter
23
4.12
Autothreshold block parameter
24
4. 13
Block diagram of the system
24
4.14
Block diagram of license plate extraction and character
Segmentation
25
4.15
Example of captured frame
26
4.16
Captured frame with black region dil ated
27
4. 17
Determin ing the angle of the plate
27
XI
4.18
Improved License Plate region
28
4. 19
Li cense Plate Crop
28
4.20
Gray scale input license plate
29
4.2 1
Overall Simu link Program
30
4.22
Overall Program for Neural etwork
31
4.23
A lphabet A w ithou t noise
32
4.24
A lphabet A w ith noise
32
4.25
Block diagram of the sequence method using GUI
37
4.26
Des ign the dra\ ing using axes. panel button and
pushbutton
38
4.27
Save and Run the M- file
39
4.28
Block diagram of the sequence method using
pushbutton load
4.29
Block d iagram of the sequence method using
pushbutton select.
4.30
41
Block diagram of the sequence method using
pushbutton extract
4.32
40
Block diagram of the sequence method using
pushbutton pre-process
4.3 1
39
41
Block diagram of the sequence method using
pushbutton recognize
42
5.1
Setup the adapter using pushbutton video setup
44
5.2
C hoose the best pixel
44
5.3
Capture image usi ng pushbutton snap
45
5.4
Video Viewer
45
5.5
Video Viewer I
45
5.6
Video Viewer 2
46
5.7
Video Viewer 3
46
5.8
Training without noi se
46
XII
5.9
Trai ning " ith noise
46
5. 10
Alphabet A
47
5. I I
Alphabet B
47
5. 12
Alphabet C
47
5. 13
Alphabet D
48
5. 14
Alphabet E
48
5. 15
Alp habet F
48
5.1 6
Alphabet G
49
5. I 7
Alphabet H
49
5. 18
Alphabet I
49
5. 19
Alphabet J
50
5.20
Alphabet K
50
5.2 1
Alphabet L
50
5.22
Alphabet M
51
5.23
Alphabet N
51
5.24
Alphabet 0
51
5.25
Alphabet P
52
5.26
Alphabet Q
52
5.27
Alphabet R
52
5.28
Alphabet S
53
5.29
Alphabet V
53
5.30
Alphabet W
53
5.3 1
Alphabet X
54
5.32
Alphabet Y
54
5.33
Alphabet T
55
5.34
Alphabet U
55
5.35
Alphabet Z
55
5.36
Alphabet I
56
5.37
Alphabet 2
56
XI II
5.38
Alphabet 3
56
5.39
Alphabet 4
57
5.40
A lphabet 5
57
5.41
A lphabet 6
57
5.42
A lphabet 7
58
5.43
A lphabet 8
58
5.44
Alphabet 9
58
5.45
Chose image
59
5.46
Load image
59
5.47
Select c haracter
59
5.48
Pre-Process the image
60
5.49
Ex tract the image us ing Neural Network
60
5.50
Recognize number 0
60
5.5 1
Recognize number I
61
5.52
Recognize number 2
61
5.53
Recognize number 3
61
5.54
Recognize number 4
62
5.55
Recognize number 5
62
5.56
Recognize number 6
62
5.57
Recognize number 7
63
5.58
Recognize number 8
63
5.59
Recognize number 9
63
XI
LIST OF ABBREVIATION
1. NN
= Neural Network
2.ANN
=Artificial Neural Network
3. GUI
= Graphical User Interface
4. LPR
=License Plate Recognition System
5.MLP
= Multilayer Perceptron
CHAPTER I
INTRODUCTION
1.0
Introduction
The number of vehicle in metropolitan cities is always growth. The growth
has a positive implication to provide a professional and sufficient parking system.
This fact requires a plate number recognition system to support a parking
arrangement.
The aim of this final project is to develop Simulink Model of License Plate
Recognition system using Sirnulink, Video and Image Processing Block set R2006a
(software).
License Pate Recognition system (LPR) is an image-processing technology
used to identify vehicles by their license plates number. LPR project is to present a
real time car park contro l system. A video-based car park model will be built by
using Simulink of Video and Image Processing Block set to capture the license plate
number.
The proposed of this License Plate Recognition system (LPR) works,
whenever the vehicle approached the secured area (Entrance). The sensor senses the
car and its presence signaled to the LPR unit. After that, the LPR unit activates the
illumination (lnfrared) and takes pictures of the front plates from the LPR camera.
The images of the vehicle include the plate and the pixel information is read by the
LPR unit's image processing hardware. Then, the LPR unit checks if the vehicle
appears on a predefined list of authorized cars compare with the database, and if
found, it gives signals to open the gate. Next, the authorized vehicle enters into the
secured area. After passing the gate, its detector closes the gate. Now the system
waits for the next vehicle to approach the secured area. The same process will be
repeated when the car goes out of the parking lot.
2
1.1
Project Objectives
The objective of the project proposal:
I.
To develop Simulink Model of License Plate Recognition
System using Video and Image Processing Block set
(software).
1.2
2.
Capturing images (license plate number)
3.
Training character of the license plate number
4.
Recognized the character of the license plate number.
Problem Statements
Nowadays parking lots are becoming organic part of our everyday li fe. We
use the entrance, day by day when going to work, shop, a hotel, a restaurant, an
airport, a hospital etc. But, for people who are living in private area like in
condominium, they have to wait for guard to open the gate before cars can enter the
entrance. Parking our car, leaving it in safe, finding it there again. fast, comfortably
and easily, that's the goal. To reduce time. increase efficiency and security, that's an
important issue. License plate recognition system using video and images offers
flexibl e, low maintenance, yet effective and intelligent technology for car park
operation: ticketing, security, location services, etc.
With integration of license plate recognition system using video and images
into parking management systems in private area, it becomes possible to automate
vehicle entry to and exit from a car park or a secure zone and to use the recognized
registration number for inventory management. The usage of license plate
recognition with specially designed camera set, equipped and synchronized with a
Infra Red flash - provides quick and accurate identification of vehicles 24 hours a
day, 7 days a week, without human assistance or any driver co-operation. It is a
significant added value for comprehensive parking management systems.
3
1.3
Scope Project
The scope of this project using Simulink, Video and Image Processing Block
set R2006a (software) are:
I.
Used the system for private area o nly such as in condominium
2.
Capture the license plate recognition.
3.
Takes picture of the front plate
4.
Train the alphabet (A to Z) and number (0 to 9) using neural
network.
5.
Recognized the alphabet (A to Z) and number ( 0 to 9)
4
CHAPTER II
LITERATURE REVIEW
This chapter reviews existing project created to get an idea about the project
design, conception, specification and any information that related to improve the
project. In this chapter, some review about an Artificial Neural Network system that
proposed to fulfill this project will also be reported.
2.1
Purpose statement for Neural Network approach
Tim Klassen [ I] as mentioned in the introduction, this research will show online average Arabic character recognition rates above 80% and training recognition
rates above 90% using Neural Networks for feature extraction and classification w ith
multiple unconstrained 19 writers. Linear networks will be emphasized, where this
represents the lowest computational overhead during both training and recall , hence
suitable for the target application device.
2.2
Artificial Neural N etwork
Haris Al-Qodri Maarif, and Sar Sardy [2] mentioned that Artificial Neural
Network (ANN) is a network which imitate to human neural network with some
simple processors (units). Each unit has some local memory and connected
unidirectional to flow the numerical data. Those units are operated only at local data
and input. ANN has some components [3]:
5
I.
Neuron is a unit consists of input, weight, bias. adder and transfer
function.
2.
Weight defines the position of hyper plane in input space.
3.
Bias is used to change the position of hyper plane in order not to
across the zero hyperspace.
4.
Transfer functi on gives a threshold value to activate neuron. Some of
transfer functions are illustrated on Fig.2.1.
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