vii
TABLE OF CONTENTS
CHAPTER
1
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xi
LIST OF FIGURES
xii
LIST OF SYMBOLS
xv
LIST OF APPENDICES
xvii
INTRODUCTION
1
1.1
Introduction
1
1.2
Problem Background
2
1.3
Problem Statement
4
1.4
Project Objectives
5
1.5
Scope of the Project
6
1.6
Significant of the Study
6
1.7
Report Organization
7
viii
2
LITERATURE REVIEW
8
2.1
Introduction
8
2.2
Segmentation Categories
9
2.2.1. Threshold Based Segmentation
9
2.2.2. Clustering Techniques
10
2.2.3. Matching
10
2.2.4. Edge Based Segmentation
10
2.2.5. Region Based Segmentation
11
Categories of Variance Text
11
2.3.1.
Lighting Variance
12
2.3.2.
Scale Variance
12
2.3.3.
Orientation Variance
12
2.3
2.4
2.5
Recognition Text
13
2.4.1.
Text Detection
15
2.4.2.
Text Area Identification
15
2.4.3.
Text Region Localization
15
2.4.4.
Text Extraction and Binary Image
16
Analytic Segmentation
17
2.5.1.
Pattern Recognition
17
2.5.2.
Statistical Pattern Recognition
18
2.5.3.
Data Clustering
18
2.5.4.
Fuzzy Logic
19
2.5.5.
Neural Networks
19
2.5.6.
Structural Pattern Recognition
20
2.5.7.
Syntactic Pattern Recognition
20
2.5.8.
Approximate Reasoning Approach to
Pattern Recognition
2.5.9.
Application of Support Vector Machine
(SVM)
2.6
21
21
Pattern Recognition System
21
2.6.1.
22
The Structure of Pattern Recognition
ix
2.7
2.8
2.9
2.6.2.
Application of Pattern Recognition
23
2.6.3.
Character Recognition
23
Run-Length Coding Algorithm
24
2.7.1.
Neighbors
25
2.7.2.
Path
26
2.7.3.
Foreground
26
2.7.4.
Connectivity
27
2.7.5.
Connected Component
27
2.7.6.
Background
28
2.7.7.
Boundary
29
2.7.8.
Interior
29
2.7.9.
Surrounds
30
2.7.10. Component Labeling
30
Properties Text
32
2.8.1.
Removing the Borders
32
2.8.2.
Divide the Text into Rows
32
2.8.3.
Divide the Row “Lines” into the Words
32
2.8.4.
Divide the Word into Characters
34
Identify Character
2.10 Fuzzy Logic
35
35
2.10.1. What Fuzzy Logic?
37
2.10.2. What is the Fuzzy Logic Toolbox?
38
2.10.3. Fuzzy Sets
38
2.10.4. Membership Function
39
2.10.5. If-Then Rules
40
2.10.6. Fuzzy Inference System
41
2.10.7. Rule Review
41
2.10.8. Surface Review
42
2.11 Summary
43
x
3
METHODOLOGY
44
3.1. Introduction
44
3.2. Problem Statement and Literature Review
46
3.3. System Development
46
3.4. Performance Evaluation
47
3.5. General Steps of Proposed Techniques
47
3.6. Proposed Algorithm for Edge Based Text
Region Extraction
3.7. Detection
48
49
3.8. Feature Map and Candidate Text Region
4
Detection
55
3.8.1. Directional Filtering
55
3.8.2. Edge Selection
55
3.8.3. Feature Map Generation
58
3.8.4. Localization
59
3.8.5. Character Extraction
59
3.9. Connection Component
60
3.10.
Fuzzy Logic
65
3.11.
Summary
67
IMPLEMENTATION
68
4.1. Introduction
68
4.2. Input Image
69
4.3. Complement Edge Detect with them
83
4.4. Eight Edge Detection
85
4.5. Image Localization
85
4.6. Separate Text From Background
86
4.7. Reduce Size
88
4.7.1. Determine Borders
88
4.7.2. Divide Text into Rows
89
4.8. Determine Character by Run-Length
90
xi
5
6
REFERENCES
Appendices
RESULTS DISCUSION
95
5.1. Introduction
95
5.2. Discussion on Results
96
5.3. Experimental results and discussion
98
5.4. Project Advantage
108
5.5. Suggestion and Future Works
109
5.6. Conclusion
110
CONCLUSION
111
113-115
116
xi
LIST OF TABLES
TITLE
TABLE NO.
3.1
Results to object to rows
4.2
Results after image scan ,where ST=start, EN=end and
PAGE
63
RW=row
64
4.1
Running time of major step
67
5.1
Performance evaluation 1
105
5.2
Performance evaluation 2
107
5.3
Performance evaluation 3
108
xii
LIST OF FIGURES
TITLE
FIGURE NO.
PAGE
2.1
General model of extraction text
13
2.2
The composition of PR system
22
2.3
Horizontal projection calculated from run-length code
24
2.4
4-and 8-neighborhood for rectangular image location
Pixel [i,j] is located in center
25
2.5
4-path and 8-path
26
2.6
Border of an image
28
2.7
Ambiguous border
28
2.8
A binary image with its boundary ,interior and
surrounds
30
2.9
An image (a) and its connected component image (b)
31
2.10
Divide the text into rows
33
2.11
Divide the rows into the words
34
2.12
Divide the word into characters
35
2.13
Identify character
35
2.14
A classical set and fuzzy set representation of “warm
room temperature”
37
2.15
(a) input of pixel (b) input of location for pixel
39
2.16
Output variable “letter”
40
2.17
Building the system with fuzzy logic
42
xiii
3.1
Proposed method
45
3.2
Block diagram of general steps of proposed approach
48
3.3
Gaussian filter
49
3.4
Sample gaussian pyramid with 8 levels
50
3.5
Extraction Operation
50
3.6
Edge detection
53
3.7
U shape object with runs after pixeltoruns
63
3.8
8-neighborhoods for rectangular image location pixel
[i,j] is located in center of each figure
65
3.9
Identify the character
66
3.10
(a) example of fuzzy input (b) example of fuzzy output
4.1
Original image
56
4.2
Structure 3x3(filter)
70
4.3
Our example of convolution operation
71
4.4
Kernel used
73
4.5
Directions of edge-detection
74
4.6
Structure of convolution
4.7
Operation of kernel 0
4.8
Edge detection
4.9
Effect of adding two edge
84
4.10
Total of edges detection
85
4.11
Localized of text
86
4.12
Separate text from background
87
4.13
Test image1 (a)image (b)localization(c)result
87
4.14
Test image2 (a)image (b)localization(c) result
88
4.15
Determine borders
88
4.16
(a)row one (b) row two
89
4.17
Identified character
90
4.18
Ten input and one output
91
4.19
Input one n1
92
4.20
Output
92
54-55
75-76
77
78-83
xiv
4.21
Output of extracted text
93
5.1
Sample 1
98
5.2
Sample 2
99
5.3
Sample 3
99
5.4
Sample 4
100
5.5
Sample 5
100
5.6
Sample 6
101
5.7
Sample 7
101
5.8
Sample 8
102
5.9
Sample 9
102
5.10
Sample 10
103
xv
LIST OF SYMBOLS
OCR
-
Optical character recognition
CC
-
Connected components
BAG
-
Black adjacency graph
AMA
-
Aligning-and merging analysis
SVM
-
Support vector machine
RLC
-
Run-length code
PR
-
Pattern recognition
SE
-
Structuring element
MFs
-
membership functions
FIS
-
Fuzzy Inference System
xvii
LIST OF APPENDICES
TITLE
APPENDIX
PAGE
A1
Matlab command to find binary image
116
A2
Matlab command used fuzzy logic for identify
116
character