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E-raamat: Handwriting Recognition: Soft Computing and Probabilistic Approaches

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Over the last few decades, research on handwriting recognition has made impressive progress. The research and development on handwritten word recognition are to a large degree motivated by many application areas, such as automated postal address and code reading, data acquisition in banks, text-voice conversion, security, etc. As the prices of scanners, com­ puters and handwriting-input devices are falling steadily, we have seen an increased demand for handwriting recognition systems and software pack­ ages. Some commercial handwriting recognition systems are now available in the market. Current commercial systems have an impressive performance in recognizing machine-printed characters and neatly written texts. For in­ stance, High-Tech Solutions in Israel has developed several products for container ID recognition, car license plate recognition and package label recognition. Xerox in the U. S. has developed TextBridge for converting hardcopy documents into electronic document files. In spite of the impressive progress, there is still a significant perfor­ mance gap between the human and the machine in recognizing off-line unconstrained handwritten characters and words. The difficulties encoun­ tered in recognizing unconstrained handwritings are mainly caused by huge variations in writing styles and the overlapping and the interconnection of neighboring characters. Furthermore, many applications demand very high recognition accuracy and reliability. For example, in the banking sector, although automated teller machines (ATMs) and networked banking sys­ tems are now widely available, many transactions are still carried out in the form of cheques.
1 Introduction 1(16)
1.1 Feature Extraction Methods
2(4)
1.1.1 Extracting Features from Binary Images
3(2)
1.1.2 Extracting Features from Gray-Scale Images
5(1)
1.2 Pattern Recognition Methods
6(11)
1.2.1 Statistical Pattern Recognition
6(3)
1.2.2 Structural Pattern Recognition
9(2)
1.2.3 Neural Networks for Pattern Recognition
11(1)
1.2.4 Soft Computing in Handwriting Pattern Recognition
12(5)
2 Pre-processing and Feature Extraction 17(44)
2.1 Pre-processing of Handwritten Images
17(13)
2.1.1 Pre-processing for Handwritten Characters
17(4)
2.1.2 Pre-processing for Handwritten Words
21(9)
2.2 Feature Extraction from Binarized Images
30(1)
2.3 Feature Extraction Using Gabor Filters
31(26)
2.3.1 Skeletonization Using Gabor Filters
42(7)
2.3.2 Results of Skeletonization
49(8)
2.3.3 Extracting Oriented Segments Using Gabor Filters
57(1)
2.4 Concluding Remarks
57(4)
3 Hidden Markov Model Based Method for Recognizing Hand written Digits 61(28)
3.1 Theory of Hidden Markov Models
61(15)
3.1.1 Markov Process
61(2)
3.1.2 Hidden Markov Models
63(2)
3.1.3 Basic Algorithms for HMMs
65(8)
3.1.4 Continuous Observation Hidden Markov Models
73(3)
3.2 Recognizing Handwritten Numerals Using Statistical and Structural Information
76(10)
3.2.1 Statistical Modeling
76(6)
3.2.2 Structural Modeling
82(4)
3.3 Experimental Results
86(1)
3.4 Conclusion
87(2)
4 Markov Models with Spectral Features for Handwritten Numeral Recognition 89(18)
4.1 Related Work Using Contour Information
89(2)
4.2 Fourier Descriptors
91(4)
4.2.1 Feature Extraction
93(2)
4.3 Hidden Markov Model in Spectral Space
95(8)
4.3.1 Spectral Space
95(3)
4.3.2 Semi-Continuous Markov Model
98(2)
4.3.3 Evaluation, Re-Estimation and Initiation
100(3)
4.4 Experimental Results
103(1)
4.5 Discussion
104(3)
5 Markov Random Field Model for Recognizing Handwritten Digits 107(24)
5.1 Fundamentals of Markov Random Fields
107(7)
5.1.1 One-Dimensional Markov Processes
107(2)
5.1.2 Markov Random Fields
109(3)
5.1.3 Markov Mesh Random Fields
112(2)
5.2 Markov Random Field for Pattern Recognition
114(8)
5.2.1 Maximum a posteriori Probability
115(1)
5.2.2 Markov Random Fields for Modeling Statistical and Structural Information
116(1)
5.2.3 Neighborhood System and Cliques
117(1)
5.2.4 Minimizing the Likelihood Energy
118(4)
5.3 Recognition of Handwritten Numerals Using MRF Models
122(6)
5.3.1 Feature
122(2)
5.3.2 Clique Function
124(1)
5.3.3 Maximizing the Global Compatibility
125(3)
5.3.4 Experimental Results
128(1)
5.4 Conclusion
128(3)
6 Markov Random Field Models for Recognizing Handwritten Words 131(14)
6.1 Markov Random Field for Handwritten Word Recognition
131(3)
6.1.1 Markov Random Field for Structural Modeling
132(1)
6.1.2 Recognition based on Maximum a posteriori Probability
133(1)
6.2 Neighborhood Systems and Cliques
134(1)
6.3 Clique Functions
135(3)
6.4 Maximizing the Compatibility with Relaxation Labeling
138(2)
6.4.1 Relaxation Labeling
138(1)
6.4.2 Maximizing the Compatibilities
139(1)
6.5 Design of Weights
140(1)
6.6 Experimental Results
141(2)
6.6.1 Neighborhood Size
141(1)
6.6.2 Iterations
142(1)
6.7 Conclusion
143(2)
7 A Structural and Relational Approach to Handwritten Word Recognition 145(28)
7.1 Introduction
145(1)
7.2 Gabor Parameter Estimation
146(8)
7.3 Feature Extraction
154(14)
7.3.1 Slant Correction
154(8)
7.3.2 Part Extraction
162(1)
7.3.3 Feature Extraction
163(5)
7.4 Conditional Rule Generation System
168(1)
7.5 Experimental Results
169(3)
7.6 Conclusion
172(1)
8 Handwritten Word Recognition Using Fuzzy Logic 173(22)
8.1 Introduction
173(1)
8.2 Extraction of Oriented Parts
173(1)
8.2.1 Slant and Tilt Correction
174(1)
8.3 System Training
174(10)
8.3.1 Word Alignment
176(4)
8.3.2 2-D Fuzzy Membership Functions
180(4)
8.4 Word Recognition
184(7)
8.4.1 Fuzzy Decision Making Process
186(5)
8.5 Experimental Results
191(1)
8.6 Conclusion
192(3)
9 Conclusion 195(28)
9.1 Summary and Discussions
195(2)
9.2 Future Directions
197(4)
9.3 References
201(22)
Index 223