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E-raamat: Optical Character Recognition Systems for Different Languages with Soft Computing

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The book offers a comprehensive survey of soft-computing models for optical character recognition systems. The various techniques, including fuzzy and rough sets, artificial neural networks and genetic algorithms, are tested using real texts written in different languages, such as English, French, German, Latin, Hindi and Gujrati, which have been extracted by publicly available datasets. The simulation studies, which are reported in details here, show that soft-computing based modeling of OCR systems performs consistently better than traditional models. Mainly intended as state-of-the-art survey for postgraduates and researchers in pattern recognition, optical character recognition and soft computing, this book will be useful for professionals in computer vision and image processing alike, dealing with different issues related to optical character recognition.

1 Introduction.-2 Optical Character Recognition Systems.-3 Soft Computing Techniques for Optical Character Recognition Systems.-4 Optical Character Recognition Systems for English language.-5 Optical Character Recognition Systems for French language.-6 Optical Character Recognition Systems for German language.-7 Optical Character Recognition Systems for Latin language.-8 Optical Character Recognition Systems for Hindi language.-9 Optical Character Recognition Systems for Gujrati language.-10 Summary and Future Research.-Index.
1 Introduction
1(8)
1.1 Organization of the Monograph
1(2)
1.2 Notation
3(1)
1.3 State of Art
4(1)
1.4 Research Issues and Challenges
5(1)
1.5 Figures
5(1)
1.6 MATLAB OCR Toolbox
5(4)
References
6(3)
2 Optical Character Recognition Systems
9(34)
2.1 Introduction
9(3)
2.2 Optical Character Recognition Systems: Background and History
12(3)
2.3 Techniques of Optical Character Recognition Systems
15(20)
2.3.1 Optical Scanning
15(2)
2.3.2 Location Segmentation
17(1)
2.3.3 Pre-processing
17(5)
2.3.4 Segmentation
22(1)
2.3.5 Representation
23(5)
2.3.6 Feature Extraction
28(1)
2.3.7 Training and Recognition
29(5)
2.3.8 Post-processing
34(1)
2.4 Applications of Optical Character Recognition Systems
35(2)
2.5 Status of Optical Character Recognition Systems
37(3)
2.6 Future of Optical Character Recognition Systems
40(3)
References
40(3)
3 Soft Computing Techniques for Optical Character Recognition Systems
43(42)
3.1 Introduction
43(3)
3.2 Soft Computing Constituents
46(9)
3.2.1 Fuzzy Sets
46(2)
3.2.2 Artificial Neural Networks
48(2)
3.2.3 Genetic Algorithms
50(3)
3.2.4 Rough Sets
53(2)
3.3 Hough Transform for Fuzzy Feature Extraction
55(1)
3.4 Genetic Algorithms for Feature Selection
56(3)
3.5 Rough Fuzzy Multilayer Perceptron
59(7)
3.6 Fuzzy and Fuzzy Rough Support Vector Machines
66(7)
3.7 Hierarchical Fuzzy Bidirectional Recurrent Neural Networks
73(5)
3.8 Fuzzy Markov Random Fields
78(4)
3.9 Other Soft Computing Techniques
82(3)
References
82(3)
4 Optical Character Recognition Systems for English Language
85(24)
4.1 Introduction
85(2)
4.2 English Language Script and Experimental Dataset
87(1)
4.3 Challenges of Optical Character Recognition Systems for English Language
88(2)
4.4 Data Acquisition
90(1)
4.5 Data Pre-processing
90(2)
4.5.1 Binarization
90(1)
4.5.2 Noise Removal
91(1)
4.5.3 Skew Detection and Correction
91(1)
4.5.4 Character Segmentation
91(1)
4.5.5 Thinning
92(1)
4.6 Feature Extraction
92(2)
4.7 Feature Based Classification: Sate of Art
94(2)
4.7.1 Feature Based Classification Through Fuzzy Multilayer Perceptron
95(1)
4.7.2 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron
95(1)
4.7.3 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines
96(1)
4.8 Experimental Results
96(9)
4.8.1 Fuzzy Multilayer Perceptron
96(4)
4.8.2 Rough Fuzzy Multilayer Perceptron
100(1)
4.8.3 Fuzzy and Fuzzy Rough Support Vector Machines
100(5)
4.9 Further Discussions
105(4)
References
106(3)
5 Optical Character Recognition Systems for French Language
109(28)
5.1 Introduction
109(2)
5.2 French Language Script and Experimental Dataset
111(2)
5.3 Challenges of Optical Character Recognition Systems for French Language
113(1)
5.4 Data Acquisition
114(1)
5.5 Data Pre-processing
115(5)
5.5.1 Text Region Extraction
115(1)
5.5.2 Skew Detection and Correction
116(1)
5.5.3 Binarization
117(1)
5.5.4 Noise Removal
118(1)
5.5.5 Character Segmentation
118(2)
5.5.6 Thinning
120(1)
5.6 Feature Extraction Through Fuzzy Hough Transform
120(2)
5.7 Feature Based Classification: Sate of Art
122(2)
5.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron
123(1)
5.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines
123(1)
5.7.3 Feature Based Classification Through Hierarchical Fuzzy Bidirectional Recurrent Neural Networks
124(1)
5.8 Experimental Results
124(8)
5.8.1 Rough Fuzzy Multilayer Perceptron
124(3)
5.8.2 Fuzzy and Fuzzy Rough Support Vector Machines
127(2)
5.8.3 Hierarchical Fuzzy Bidirectional Recurrent Neural Networks
129(3)
5.9 Further Discussions
132(5)
References
135(2)
6 Optical Character Recognition Systems for German Language
137(28)
6.1 Introduction
137(2)
6.2 German Language Script and Experimental Dataset
139(1)
6.3 Challenges of Optical Character Recognition Systems for German Language
140(1)
6.4 Data Acquisition
141(1)
6.5 Data Pre-processing
142(6)
6.5.1 Text Region Extraction
142(1)
6.5.2 Skew Detection and Correction
143(1)
6.5.3 Binarization
144(1)
6.5.4 Noise Removal
145(1)
6.5.5 Character Segmentation
145(1)
6.5.6 Thinning
146(2)
6.6 Feature Selection Through Genetic Algorithms
148(2)
6.7 Feature Based Classification: Sate of Art
150(2)
6.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron
151(1)
6.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines
152(1)
6.7.3 Feature Based Classification Through Hierarchical Fuzzy Bidirectional Recurrent Neural Networks
152(1)
6.8 Experimental Results
152(10)
6.8.1 Rough Fuzzy Multilayer Perceptron
153(2)
6.8.2 Fuzzy and Fuzzy Rough Support Vector Machines
155(6)
6.8.3 Hierarchical Fuzzy Bidirectional Recurrent Neural Networks
161(1)
6.9 Further Discussions
162(3)
References
163(2)
7 Optical Character Recognition Systems for Latin Language
165(28)
7.1 Introduction
165(2)
7.2 Latin Language Script and Experimental Dataset
167(1)
7.3 Challenges of Optical Character Recognition Systems for Latin Language
168(2)
7.4 Data Acquisition
170(1)
7.5 Data Pre-processing
170(5)
7.5.1 Text Region Extraction
170(1)
7.5.2 Skew Detection and Correction
171(1)
7.5.3 Binarization
172(1)
7.5.4 Noise Removal
173(1)
7.5.5 Character Segmentation
173(1)
7.5.6 Thinning
174(1)
7.6 Feature Selection Through Genetic Algorithms
175(3)
7.7 Feature Based Classification: Sate of Art
178(2)
7.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron
178(1)
7.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines
179(1)
7.7.3 Feature Based Classification Through Hierarchical Fuzzy Rough Bidirectional Recurrent Neural Networks
179(1)
7.8 Experimental Results
180(8)
7.8.1 Rough Fuzzy Multilayer Perceptron
180(3)
7.8.2 Fuzzy and Fuzzy Rough Support Vector Machines
183(3)
7.8.3 Hierarchical Fuzzy Rough Bidirectional Recurrent Neural Networks
186(2)
7.9 Further Discussions
188(5)
References
190(3)
8 Optical Character Recognition Systems for Hindi Language
193(24)
8.1 Introduction
193(3)
8.2 Hindi Language Script and Experimental Dataset
196(1)
8.3 Challenges of Optical Character Recognition Systems for Hindi Language
197(3)
8.4 Data Acquisition
200(1)
8.5 Data Pre-processing
200(2)
8.5.1 Binarization
200(1)
8.5.2 Noise Removal
201(1)
8.5.3 Skew Detection and Correction
201(1)
8.5.4 Character Segmentation
201(1)
8.5.5 Thinning
202(1)
8.6 Feature Extraction Through Hough Transform
202(2)
8.7 Feature Based Classification: Sate of Art
204(2)
8.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron
205(1)
8.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines
205(1)
8.7.3 Feature Based Classification Through Fuzzy Markov Random Fields
206(1)
8.8 Experimental Results
206(3)
8.8.1 Rough Fuzzy Multilayer Perceptron
206(2)
8.8.2 Fuzzy and Fuzzy Rough Support Vector Machines
208(1)
8.8.3 Fuzzy Markov Random Fields
208(1)
8.9 Further Discussions
209(8)
References
215(2)
9 Optical Character Recognition Systems for Gujrati Language
217(24)
9.1 Introduction
217(2)
9.2 Gujrati Language Script and Experimental Dataset
219(1)
9.3 Challenges of Optical Character Recognition Systems for Gujrati Language
220(4)
9.4 Data Acquisition
224(1)
9.5 Data Pre-processing
224(2)
9.5.1 Binarization
224(1)
9.5.2 Noise Removal
225(1)
9.5.3 Skew Detection and Correction
225(1)
9.5.4 Character Segmentation
225(1)
9.5.5 Thinning
225(1)
9.6 Feature Selection Through Genetic Algorithms
226(2)
9.7 Feature Based Classification: Sate of Art
228(3)
9.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron
229(1)
9.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines
230(1)
9.7.3 Feature Based Classification Through Fuzzy Markov Random Fields
230(1)
9.8 Experimental Results
231(5)
9.8.1 Rough Fuzzy Multilayer Perceptron
231(1)
9.8.2 Fuzzy and Fuzzy Rough Support Vector Machines
231(4)
9.8.3 Fuzzy Markov Random Fields
235(1)
9.9 Further Discussions
236(5)
References
238(3)
10 Summary and Future Research
241(6)
10.1 Summary
241(2)
10.2 Future Research
243(4)
References
244(3)
Index 247
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