SEGMENTATION AND RECOGNITION OF MALAYSIAN CAR PLATES USING FREEMAN CHAIN CODES (FCC)

NOR AMIZAM BINTI JUSOH

Thesis submitted in fulfillment of the requirements for the award of the degree of

Master of Science in Computer

Faculty of Computer Systems & Software Engineering UNIVERSITI MALAYSIA PAHANG

FEBRUARY 2011

v

ABSTRACT

Research on automatic car plate recognition has been widely and intensively conducted all over the world with the application of various types of segmentation and recognition techniques by researchers. Most of the recognition techniques applied are not focusing on shape-based recognition although through normal human vision, each character in the car plate has a unique and different shape with each other. The chosen techniques have contributed into many proposed suitable methodologies for car plate recognition research but with the same objectives; to gain high or increase the segmentation and recognition accuracy rate with less processing time. This research is conducted with the aim of identifying the suitable or appropriate segmentation technique which can be used to segment either the standard or non-standard specification car plates. Besides that, the objective is also to study whether the shape-based recognition technique is efficient and accurate enough to recognize Malaysian car plates which are varied in terms of font types. Techniques that have been chosen for segmentation process are pixel count, connected component labeling (CCL) and a proposed technique; the connected component labeling with minimum object removal. As for recognition purpose, the techniques that have been experimented are the Freeman chain codes (FCC), template matching and a proposed technique; the Freeman chain codes with characters’ features (FCCwF). The results from the experiment shows that the proposed segmentation technique; the connected component labeling with minimum object removal able to increase the segmentation success rate by more than 96% and the proposed recognition technique; the Freeman chain codes with characters’ features is able to reach the accuracy rate of 95% compared to other tested techniques. Based on the experiments and results, Freeman chain codes are efficient and accurate enough to recognize various font types of Malaysian car plates for most of the characters with less processing time of 0.1s but a higher recognition accuracy rate can be gained by combining FCC with characters’ features.

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ABSTRAK

Kajian terhadap segmentasi dan pengecaman nombor plet kereta secara automatik telah lama dijalankan diseluruh dunia dengan penggunaan pelbagai teknik dan kaedah oleh penyelidik-penyelidik. Namun begitu, kebanyakan pilihan teknik pengecaman tidak tertumpu kepada pengecaman berasaskan bentuk sedangkan secara amnya, setiap huruf yang terdapat di dalam plet kereta itu mempunyai bentuk yang unik dan berbeza antara satu sama lain. Pilihan teknik pengecaman ini menghasilkan variasi metodologi tetapi dengan maksud dan tujuan yang sama iaitu mencapai kadar ketepatan pengecaman yang tinggi dengan masa memproses yang kurang. Oleh itu, kajian ini telah dijalankan bagi mengenalpasti teknik segmentasi yang sesuai yang dapat mengasingkan huruf-huruf di dalam plet kereta yang dibina sama ada mengikut spesifikasi piawai ataupun tidak. Selain itu, kajian juga bertujuan untuk mengkaji sama ada teknik pengecaman berasaskan bentuk adalah sangat berkesan dan tepat dalam mengecam plet kereta Malaysia yang dihasilkan menggunakan pelbagai jenis bentuk tulisan. Teknik yang telah dipilih untuk proses segmentasi adalah teknik kiraan piksel (pixel count), pelabelan komponen berhubung (connected component labeling) dan teknik cadangan iaitu pelabelan komponen berhubung dengan penyingkiran objek bernilai minima. Manakala, teknik yang dipilih untuk eksperimen pengecaman adalah teknik kod rantaian Freeman (Freeman chain codes) yang ringkas yang menghasilkan kod rantaian berdasarkan piksel sempadan bagi imej huruf, padanan templat (template matching) dan teknik cadangan iaitu teknik kod rantaian Freeman dengan ciri-ciri huruf. Hasil yang diperolehi dari kajian menunjukkan bahawa teknik segmentasi cadangan iaitu pelabelan komponen berhubung dengan penyingkiran objek bernilai minima mampu meningkatkan tahap kejayaan segmentasi melebihi 96% dan teknik pengecaman cadangan iaitu teknik rantaian Freeman dengan ciri-ciri huruf dapat meningkatkan kadar ketepatan pengecaman sehingga 95% berbanding teknik-teknik ujian lain. Berdasarkan hasil tersebut maka, teknik kod rantaian Freeman boleh menjadi satu alternatif untuk mengecam plet kereta Malaysia dengan pelbagai jenis bentuk tulisan dengan kemampuan teknik ini untuk memberikan tempoh masa memproses yang kurang iaitu 0.1 saat dan kadar ketepatan pengecaman yang tinggi dapat dicapai melalui gabungan bersama ciri-ciri huruf.

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TABLE OF CONTENTS

Page

SUPERVISOR’S DECLARATION ii

STUDENT’S DECLARATION iii

ACKNOWLEDGEMENTS iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF ABBREVIATIONS xiv

CHAPTER 1 INTRODUCTION

1.0 Introduction 1

1.1 Research Background 3

1.2 Problem Statement 5

1.3 Objective 6

1.4 The Importance Of The Research 6

1.5 Scope 7

1.6 Contributions 7

1.7 Thesis Organization 7

1.8 Summary 8

CHAPTER 2 LITERATURE REVIEW

2.0 Introduction 9

2.1 Malaysia’s Car Plate 10

2.2 The Evolution Of Research In Car Plate Recognition 13

2.3 Theory Of Shape Representation 16

2.3.1 Theory Of Freeman Chain Codes (FCC) 19 2.3.2 Theory Of Template Matching 22

viii

2.4 Previous Works On Car Plate Recognition 25

2.5 General Methodology Of Car Plate Recognition 29

2.5.1 Image Acquisition 30 2.5.2 Image Pre-Processing 31 2.5.3 Image Segmentation 34 2.5.4 Image Recognition

36

2.6 Summary 38

CHAPTER 3 IMAGE ACQUISITION, DATA

DEFINITION, PRE- PROCESSING AND

SEGMENTATION

3.0 Introduction 39

3.1 Proposed Methodology 39

3.2 Image Acquisition 40

3.3 Data Definition 41

3.4 Image Pre-Processing 42

3.4.1 Image Cropping 43 3.4.2 Grayscale Conversion 44 3.4.3 Filtering 45 3.4.4 Thresholding

47

3.5 Image Segmentation 49

3.5.1 Boundary Extraction 49 3.5.2 Segmentation

55

3.6 Summary 65

CHAPTER 4 CHAIN CODE DERIVATION,

CHARACTERS’ FEATURES

EXTRACTION AND IMAGE

RECOGNITION

4.0 Introduction 66

4.1 Chain Code Derivation 66

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4.2 Characters’ Features Extraction 71

4.3 Shape Recognition 74

4.3.1 Freeman Chain Code (FCC) 75 4.3.2 Template Matching 75 4.3.3 Freeman Chain Code with Features

77

4.4 Summary 79

CHAPTER 5 RESULTS AND DISCUSSION

5.0 Introduction 80

5.1 Experiment Design 80

5.1.1 Criteria Of Success 80 5.1.2 Experiment Data 81 5.1.3 Experiment Description

81

5.2 Segmentation Results 81

5.3 Recognition Results 83

5.3.1 Recognition Time 83 5.3.2 Recognition Accuracy Rate

83

5.4 Conclusion 85

CHAPTER 6 CONCLUSION

6.0 Conclusion 87

6.1 Future Works 88

REFERENCES 90

APPENDICES

A Malaysia’s Customized Car Plates Specifications 97

B Samples of Car Plates’ Images 100

C Result of Connected Component Labeling 103

D Details of Features Extracted For Each Character 104

E Contribution of Research Papers 105

x

LIST OF TABLES

Table No. Title Page

2.1 Specific plate measurement and criteria of car plate 11

2.2 Summary of researches on car plate recognition 13

2.3 Shape equivalence and similarity 17

4.1 Direction table 67

4.2 Features extracted for some characters 73

4.3 Characters representing states or provinces in Malaysia 74

4.4 Accumulated total of 8-directional codes for character’A’ 75

5.1 Results of segmentation phase 82

5.2 Result of processing time 83

5.3 The percentage of recognition rate 84

5.4 Percentage of success recognition by character using Freeman

chain codes

84

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LIST OF FIGURES

Figure No. Title Page

2.1 Different types of Malaysian car plates 10

2.2 Special event or customized Malaysian car plates 11

2.3 Different types and sizes of fonts used for normal Malaysian car

plates

11

2.4 Malaysia’s car plate specifications endorsed by The Road and

Transport Department of Malaysia

12

2.5 The equivalence of shape character ‘B’ for (a) and (b) while (c)

shows the similarity between shapes

17

2.6 Taxonomy of shape representation techniques 18

2.7 Direction numbers for (a) 4-directional chain codes, (b) 8-

directional chain codes

19

2.8(a) A 4-connected object 21

2.8(b) A 4-connected object’ s boundary 21

2.8(c) Obtaining the chain code from the object in (a & b) with 4-

connected

21

2.8(d) Obtaining the chain code from the object in (a & b) with 8-

connected

21

2.9 Illustration of template matching 23

2.10 Methodology adopted by Paras Ram (2005) 26

2.11 Layout of methodology applied by Wu et al (2005) 27

2.12 Methodology applied by Yang et al (2007) 27

2.13 Methodology applied by Quan et al (2009) 28

2.14 The general methodology of car plate recognition 29

2.15 Basic image capturing model 30

3.1 The proposed methodology 40

3.2 Original image of back of a vehicle with car plate 41

3.3 Samples of characters from car plates observed in data

collection

42

3.4 Processes in the image pre-processing phase 42

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3.5(a) Image of Figure 3.2 which has been reduced to 30% from

original size

44

3.5(b) Manually cropped image of car plate 44

3.6 Grayscale image of a car plate 44

3.7 Filtered image 45

3.8 Flowchart for median filtering 46

3.9 Illustration example of the median filter with 3x3 neighborhood 47

3.10 Samples of threshold image 48

3.11 Binary image of character ‘C’ after the process of thresholding 48

3.12 Processes in the image segmentation phase 49

3.13 Binary image of character ‘C’ after skeletonization 50

3.14 Image of car plate after the process of skeletonization

performed

50

3.15(a) The boundary image of car plate after the process of boundary

extraction

51

3.15(b) The binary image of character ‘C’ of the plate in Fig. 3.11(a)

after the process of boundary extraction

52

3.16(a) Another boundary image of plate with different font type 52

3.16(b) A binary image of different pattern of character ‘C’ after the

process of boundary extraction

53

3.17 Flowchart of extracting external boundary 54

3.18 Part of binary image which shows pixels with value ‘1’ 56

3.19 Total of pixels ‘1’ which denotes characters and border between

characters.

56

3.20 Flowchart for vertical projection with pixel count technique 58

3.21 Result of failure segmentation using vertical projection with

pixel count technique

59

3.22 Part of object labeling using connected component labeling

technique

60

3.23 Success segmentation using connected component labeling

technique

60

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3.24(a)& (b) Segmented images with noises (in circle) 62

3.24(c) Segmented image after the cleaning process 62

3.25 Binary image for Figure 3.24(a) 62

3.26 Binary image for Figure 3.21(b) 63

3.27 Flowchart for connected component labeling with minimum

object removal technique

64

4.1(a) The initial location, P0 68

4.1(b) The next boundary pixels and directions to derive chain codes 68

4.1(c) The complete process of chain codes derivation 69

4.2(a) List of chain codes for character ‘C’ 69

4.2(b) List of chain codes for different pattern of character ‘C’ 70

4.3 Two samples of boundary images of character ‘C’ with

incorrect positions of pixel ‘1’

70

4.4 The location of pixels ‘1’ of a boundary image in binary format

which influence the chain codes

71

4.5(a) Part of constant matrix 72

4.5(b) Part of segmented image 73

4.5(c) Part of new segmented image (matrix product) 73

4.6(a) Original image of character ‘B’ with size 26 x 60 76

4.6(b) Resized image of character ‘B’ with size 12 x 56 76

4.7 Flowchart of Freeman chain codes with characters’ features

algorithm

78

5.1 Car plates with serious segmentation failure 82

5.2 Bar graph of percentage of correct recognition by characters 85

5.3(a) Car plate with clear gap between each character 86

5.3(b) Car plate with small gap between character ‘7’ and ‘4’ 86

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LIST OF ABBREVIATIONS

CCL Connected component labeling

CPR Car plate recognition

FCC Freeman chain code

FCCwF Freeman chain code with features

N/A Not available

RBF Radial basis function

TM

Template matching

CHAPTER 1

INTRODUCTION

1.0 INTRODUCTION

Digital image processing refers to processing digital images by using digital

computer in order to change its nature such as enhancing the edges of an image to make

it appear sharper, to remove noise from image, to remove detail from image and etc.

Digital image processing needs a very appropriate and fast technique especially when

dealing with images which involved in detection and recognition tasks. One of the

major advantages in digital images is the ability to perform a variety of processing

procedures with a computer. These procedures can be selected and adjusted to change

the characteristics of the images, usually for the purpose of improving quality or

optimizing characteristics for maximum visibility. Images can be stored in the computer

memory and easily retrieved on the same computer screen and can be saved indefinitely

or be printed on paper or film if necessary. All digital imaging systems can be

networked into practice management software programs facilitating integration of data.

Digital imaging is also environmentally friendly since it does not require chemical

processing. It is well known that used film processing chemicals contaminate the water

supply system with harmful metals such as the silver found in used fixer solution.

Apart from all of the above advantages, the use of digital image processing also has

disadvantages. One of the advantages is the initial cost can be high depending on the

system used, the number of detectors purchased or the hardware or device used.

Competency of using the software can takes time to master depending on the level of

computer literacy of team members.

Many applications have been developed and implemented which applies image

processing and character recognition technology. One of the most popular and

important applications of digital image processing which focus on image detection and

recognition is car plate recognition (CPR) system which is used to overcome the

challenges of monitoring modern day traffic. This technology identify vehicles

automatically by reading and recognizing their car plates which may involve in traffic

violations, crime scenes, car parks area determination, statistical and etc. The plate

recognition is done based on the given conditions and instructions. This recognition

system is installed in many places such as toll gates, parking lots and also entrance of

highly secured buildings. Police are using this application because they can detect

speeding vehicles from distance away and summon tickets will be issued later to the

vehicle’s owner. These systems are beneficial because it can automate car park

management, improve the security of car park operator and the users as well, eliminate

the usage of swipe cards and parking tickets, improve traffic flow during peak hours,

detect speeding cars on highways, and detect cars which run over red traffic lights.

Furthermore, when the data gathered by a CPR system is stored and organized in a

database, more complex information-driven tasks may be achieved, such as vehicle

travel time calculations, marketing analysis, and border control.

Different standards for car plates are found all over the world, and variations

within each country are usually limited. Because of that, many commercialized systems

to identify car plates have been developed which are mainly from Japan, USA and

Europe but the different styles and standards of car plates used in these countries caused

these commercialized systems are unsuitable to be used in other countries especially in

Malaysia. Automated car plate reading is a particularly useful and practical approach

because, apart from the existing and legally required car plate, it assumes no additional

means of vehicle identity.

The research and development (R&D) for car plate recognition system has been

carried intensively by many researchers all over the world. It has started since 1989 and

continues to evolve until today by the application of various techniques ranging from

simple to complex technique. One of the simple techniques applied in CPR was

Freeman chain code (FCC) which is done by Paras Ram (2005) but has not been

2

published in any journal or conference eventhough the recognition result achieved was

quite impressive. Recent researches on CPR have been performed using neural

networks by Shan (2010) which achieve high recognition rate for normal characters but

not Chinese characters. So, this has become a motivation to continue research on CPR

since FCC is a shape-representation technique which is very good in representing

shapes or objects information with curves. The advantages of this technique also can be

considered in proposing FCC as an alternative recognition technique.

Normally, when conducting R&D in this area, a general methodology which

consists of several phases is applied together with the appropriate techniques in order to

ensure that the R&D will meet its objectives. The general phases are image acquisition,

image pre-processing, image segmentation and image recognition and each phase is

performed continuously. Image acquisition is a phase for obtaining samples of images

while image pre-processing is a phase for enhancing the quality of images obtained

from the first phase. Image segmentation is a phase to segment images whether to

isolate object from background or to separate characters on car plates. The final phase is

image recognition where in this phase, the separated image is recognize and in this case,

the characters on car plates.

1.1 RESEARCH BACKGROUND

Car plate recognition is important because of its advantages as already discussed

in previous section. The choice of techniques for each phase especially the segmentation

and recognition techniques plays an important role because it will ensure that the

information extracted is correct. The information extracted is the characters that depict

the car plate. The segmentation technique chosen is normally based on the capability to

isolate objects from its background successfully while the recognition technique is

based on the capability of the technique to give a high recognition rate, recognize

similar pattern characters and less processing time which is the time used to process and

recognize car plates. Certain recognition techniques show a high recognition rate while

others show less processing time but the best recognition technique should deliver both

advantages.

3

Image segmentation is not an easy task and it is quite difficult to perform

because of several reasons such as non-uniform illumination, no control of the

environment, inadequate model of the object of interest and noise in the image. Due to

this, to ensure the successfulness of segmentation phase, the images feed into this phase

must be in high quality. If the images are not in a good quality, it must go through

certain processes in order to remove all noises which will affect the segmentation result.

Segmentation affects greatly on the accuracy of recognition because if the contours of

the characters are inaccurate, it might lead to errors in the recognition phase or even

failed the recognition phase. One of the factors which can makes the contours of

characters inaccurate is slope (Al Faqheri and Mashohor, 2009) which sometimes

happened during image acquisition phase. This factor can be dealt by applying vertical

and horizontal scanning (Al Faqheri and Mashohor, 2009). Since Malaysian car plates

are varied in terms of its types, fonts, sizes and styles, therefore, to apply only one

segmentation technique is not a suitable solution (Wu et al, 2004). This research

investigates on the suitable technique to apply with the variation types of normal single

row Malaysian car plates.

In normal human vision, characters in car plates are differentiated by observing

its shape characteristics whether the outer or edge shape or with its region. The shape

characteristics can be classified into three classes; contours such as chain codes

technique, regions such as template matching technique and transforms such as wavelet

technique (da Fontoura Costa and Cesar, 2009). These three classes differ in terms of

how the shape analysis is done and represented. Eventhough the characters can

obviously be differentiated by their shape, however, from the researcher’s findings on

recognition techniques for car plate recognition system (CPR), various techniques have

been applied by researches besides the shape-based representation technique. The

recognition techniques are varied from simple to complex techniques and some of the

researchers are focusing on the application of template matching technique (Naito et al,

2000; Sarfraz et al, 2003; Tao et al, 2004; Wu et al, 2005; Wang and Zhang, 2008; Quan

et al, 2009) while only one research has been conducted using stroke analysis or

Freeman chain code (FCC) (Paras Ram, 2005). The evolution of researches on CPR

with its recognition techniques applied can be referred in Section 2.3.

4

FCC is a technique which uses only a boundary of an object in a clockwise or

anti-clockwise direction and assigning a direction to the segments connecting every pair

of pixels by referring to either 4 or 8-neighbor. Since this technique only reads the

boundary pixels and not the whole region of shape, so the time taken to process the

information will be lower than the time used to read and process the whole regions.

Therefore, with this benefit, FCC is assumed to be able to give high recognition rate and

at the same time able to process or recognize car plates in high speed. Due to that, this

research is conducted to study on the efficiency and accuracy of FCC as one of the

alternative recognition technique. This technique is compared with the template

matching technique in terms of recognition accuracy and processing time. If this

technique alone does not yield high recognition rate, it will be combined with

characters’ features to improve the performance of recognition rate and at the same time

preserve the advantage of less processing time.

1.2 PROBLEM STATEMENT

One of the major concerns for car plate recognition research is to apply a

segmentation technique which is able to segment characters in car plates successfully

and a recognition technique which is capable in giving high recognition accuracy and

less processing time. For the recognition phase, both capabilities are assumed as able to

be achieved by using the Freeman chain code technique; one of the shape-based

representation technique as already discussed in general in previous section. According

to Cha et al (1999), chain codes is one of the techniques that are able to recognize

characters and digits successfully and any shape feature can be computed from the chain

codes (Jahne, 2005) while Seul et al (1999) claims that chain codes provide a lossless

compressing and preserving all topological and morphological information which bring

out another benefit in terms of speed and effectiveness for the analysis of line patterns.

From the findings, chain codes technique have been studied widely in other areas

besides car plate recognition system such as handwritten word recognition (Madhvanath

et al, 1999; Chan and Yeung, 1999; Britto et al, 2000), line drawing interpretation

(Haron et al, 2004), object or image recognition (Salem et al, 2005), corner detection

(Subri et al, 2006), length estimators (Dianat and Haron, 2008), recognizing handwritten

5

characters (Hoque et al, 2003; Shilbayeh et al, 2009) and eyes contour detection (Jing-

Dong, 2010).

Thus, this research aim is to study on the efficiency and accuracy of Freeman

chain code as an alternative recognition technique in car plate recognition. Specifically,

this research is intended to answer the following questions:

a. What are the existing methodologies that have been used by other researchers in

car plate recognition?

b. Are template matching and FCC techniques efficient or accurate enough for car

plate recognition?

c. If not accurate, is there a way to make FCC more accurate?

1.3 OBJECTIVE

The objectives for this research are:

a. To experiment on the accuracy and effectiveness of FCC and compares it with

template matching.

b. To develop a new algorithm to increase the recognition rate.

1.4 THE IMPORTANCE OF THE RESEARCH

This research is to study, analyze and test the FCC technique, algorithm and

methodology needed for automatic recognition of car plates. It is hope that this research

can contribute in the numbers of recognition techniques in image processing area. The

experience, knowledge and advantage obtained from this research can help in image

processing area especially in enhancing and improving the application of car plate

recognition.

6

1.5 SCOPE

This research only focuses on:

a. Single row car plates with black background and white characters.

b. Normal type of Peninsular Malaysia’s car plate with standard specification.

c. Images of car plates in JPEG format taken by using digital camera.

d. The image of car plate for research purpose.

e. Car plates with three alphabets and four numbers.

f. Characters to be recognized are ranged from A to Z with the exception of I,O

and Z while the numbers will range from 0-9.

1.6 CONTRIBUTIONS

The academic contributions from this research are:

a. A methodology for car plate recognition which uses FCC technique and

characters’ features.

b. A segmentation technique and algorithm which combines connected component

labeling (CCL) and minimum object removal.

c. A recognition technique and algorithm which combines FCC technique with

characters’ features.

1.7 THESIS ORGANIZATION

The organization of this thesis is consisting of six important chapters from

Chapter 1 until Chapter 6. Chapter 1 presents the introduction of the research including

the general review of previous approaches in the car plate recognition done by other

researches, research background, issues related with the needs to conduct this research

which is included in the problem statement section followed by the objectives, the

importance of the research, scopes and the contributions of the research. Chapter 2 will

explore in details about all related theoretical backgrounds, the evolution of research in

CPR systems, previous works done by existing researches on CPR using FCC and

7

template matching. Chapter 3 will elaborate in detail how the research was conducted

for the first module while the explanation on the experiment for module 2 is discussed

in Chapter 4. The results and discussion is discussed in Chapter 5 and the conclusion

and suggestions for the whole research as well as the future works that could be done to

improve the system are discussed in Chapter 6.

1.8 SUMMARY

This chapter discusses on the emerging area of image processing and the R&D

carried out under this area. One of the researches is car plate recognition system which

has been intensively done all over the world. This application has many advantages

such as helps the enforcer to enforce the law, helps in solving crimes cases and improve

the security. In order to ensure the successfulness of the application, many techniques

have been applied to increase the recognition rate, minimize the error rate and lower the

processing time. Since characters are differentiated by its shape and not many

researchers tend to use the shape-based representation technique for recognition,

therefore the FCC technique has been chosen to be experimented to study on its

efficiency and accuracy. The suitable segmentation techniques for Malaysian car plates

also become the focus for this research because segmentation affects greatly the

recognition rate.

8

CHAPTER 2

LITERATURE REVIEW

2.0 INTRODUCTION

Nowadays, recognition system plays an important role in many fields such as

medical imaging, manufactured goods inspection, industrial inspection and law

enforcement by applying image processing applications. One of the image processing

applications is car plate recognition (CPR) system which developed using the image

processing technique. This application has been widely used all over the world in

assisting the law enforcement to recognize cars which involved in certain offences or

crimes due to some benefits offered by this system (Casaletto, 2010). Normally this

application is connected with cameras installed in patrol cars to track down auto-related

crimes. These cameras can take pictures of car plates from distance and the pictures

captured is processed using the image processing techniques and then run against a

database of stolen vehicles to find possible matches. Besides that, CPR system also is

widely used as a way to detect speeding vehicles on highways. Vehicles with speeds

more than allowed speed can cause harm to the drivers himself or other people involved

in accidents. In order to avoid such things happened, the CPR can be used to detect the

speeding vehicles by capturing the images of vehicles’ plates and processed it. The

result from the processing can be used to print summon tickets to the speeding drivers.

Research on car plate recognition has been conducted intensively with the

application of various techniques ranging from simple and single technique to a

complex or combination of several techniques. The application of various techniques

was due to several reasons or factors that support the emergence of the research in this

area. The factors are such as to increase the recognition rate, minimize the error rate and

processing time. These three factors are very important to ensure the success of a CPR

system to be considered as a reliable system. In this thesis, previous researches have

been studied in order to gain some knowledge on the important aspects of car plate

recognition research such as the methodology of the recognition system, the techniques

and the algorithms applied. All theoretical parts of all elements needed for this research

are discussed later in this chapter.

2.1 MALAYSIA’S CAR PLATE

Car plates in Malaysia are varied and different in colors and formats. The

formats are single and double rows car plates and the color or background of car plates

for personal and commercial vehicles are black with white characters on it. There are

also car plates with red background and white or yellow characters which are provided

for diplomats. Figure 2.1 shows the different types of Malaysia’s car plates. Another

type of car plates is the special or customized plates which have words such as

‘BAM bee’, ‘SATRIA’, ‘PROTON’, ‘TIARA’, ‘PUTRAJAYA’, ‘SUKOM’ and others

as shown in Figure 2.2. For this research, only single row car plates for personal use as

shown in Figure 2.3 are used as testing images. The Road and Transport Department of

Malaysia has endorsed a standard for car plates that includes the font and size of

characters that must be followed by car owners (Alias, 1999). Figure 2.4 and Table 2.1

show the standard specification for normal car plates while Appendix A shows standard

specification for customized car plates.

Figure 2.1: Different types of Malaysian car plates

10

Figure 2.2: Special event or customized Malaysian car plates

Figure 2.3: Different types and sizes of fonts used for normal Malaysian car plates.

Table 2.1: Specific plate measurement and criteria of car plate

No. Specification Measurement (mm)

1. Character height 70 2. Character width 40 3. Distance between characters or numbers 10 4. Distance between character and number in single row

plate 30

5. Distance between character row and number row in double row plate

10

6. Minimum clearance between character and top-edge and bottom-edge of plate

10

7. Minimum clearance between character and left-edge and right-edge of plate

10

Source: Alias (1999)

11

Peninsular of Malaysia

Sabah

Sarawak

Figure 2.4: Malaysia’s car plate specifications endorsed by The Road and Transport Department of Malaysia

Source: The Road and Transport Department Official Portal

12

2.2 THE EVOLUTION OF RESEARCH IN CAR PLATE RECOGNITION

This section discusses on the evolution of research on car plate recognition

systems. The researcher believes that the evolution of the research has started earlier

than 1989; however, the discussion was based on the findings from available resources.

Table 2.2 shows the summary of some of the findings.

Table 2.2: Summary of researches on car plate recognition

Year Researcher Segmentation Technique

Recognition Technique Recognition Rate

1989 Williams et al N/A Syntax forcer 93%

1991 Miyamoto et al N/A Mahalanobis distance

More than 99%.

Tindall Segmentation algorithm Neural networks Over 90%. 1995

Nijhuis et al Fuzzy c-means

clustering algorithm

Fuzzy logics and neural networks

98.51%.

1997 Dragichi Differential gradient edge detection approach

Neural network 98%

Parisi et al

Finding white areas between columns with higher density of black pixels

Feed forward neural network trained with Block Recursive LS algorithm

90%. 1998

Lim et al N/A Simple character recognition algorithm

More than 90%

Sirithinaphong and Chamnongthai

Horizontal and vertical projection

Four layers BP neural network with supervised learning

92%.

1999

Yap et al Blob analysis Fuzzy ARTMAP neural network

N/A

2000 Naito et al

Geometrical Property (the range of the size of each character)

Template matching

More than 97%.

2001

Emiris and Koulouriotis

N/A Full Text Recognition option

More than 80%.

2002 Vázquez et al Labelling process Multilayer neural network 91.2%.

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