1 Introduction |
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1 | (16) |
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1.1 Feature Extraction Methods |
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2 | (4) |
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1.1.1 Extracting Features from Binary Images |
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3 | (2) |
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1.1.2 Extracting Features from Gray-Scale Images |
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5 | (1) |
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1.2 Pattern Recognition Methods |
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6 | (11) |
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1.2.1 Statistical Pattern Recognition |
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6 | (3) |
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1.2.2 Structural Pattern Recognition |
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9 | (2) |
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1.2.3 Neural Networks for Pattern Recognition |
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11 | (1) |
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1.2.4 Soft Computing in Handwriting Pattern Recognition |
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12 | (5) |
2 Pre-processing and Feature Extraction |
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17 | (44) |
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2.1 Pre-processing of Handwritten Images |
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17 | (13) |
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2.1.1 Pre-processing for Handwritten Characters |
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17 | (4) |
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2.1.2 Pre-processing for Handwritten Words |
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21 | (9) |
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2.2 Feature Extraction from Binarized Images |
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30 | (1) |
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2.3 Feature Extraction Using Gabor Filters |
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31 | (26) |
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2.3.1 Skeletonization Using Gabor Filters |
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42 | (7) |
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2.3.2 Results of Skeletonization |
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49 | (8) |
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2.3.3 Extracting Oriented Segments Using Gabor Filters |
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57 | (1) |
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57 | (4) |
3 Hidden Markov Model Based Method for Recognizing Hand written Digits |
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61 | (28) |
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3.1 Theory of Hidden Markov Models |
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61 | (15) |
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61 | (2) |
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3.1.2 Hidden Markov Models |
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63 | (2) |
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3.1.3 Basic Algorithms for HMMs |
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65 | (8) |
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3.1.4 Continuous Observation Hidden Markov Models |
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73 | (3) |
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3.2 Recognizing Handwritten Numerals Using Statistical and Structural Information |
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76 | (10) |
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3.2.1 Statistical Modeling |
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76 | (6) |
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3.2.2 Structural Modeling |
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82 | (4) |
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86 | (1) |
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87 | (2) |
4 Markov Models with Spectral Features for Handwritten Numeral Recognition |
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89 | (18) |
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4.1 Related Work Using Contour Information |
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89 | (2) |
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91 | (4) |
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93 | (2) |
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4.3 Hidden Markov Model in Spectral Space |
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95 | (8) |
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95 | (3) |
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4.3.2 Semi-Continuous Markov Model |
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98 | (2) |
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4.3.3 Evaluation, Re-Estimation and Initiation |
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100 | (3) |
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103 | (1) |
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104 | (3) |
5 Markov Random Field Model for Recognizing Handwritten Digits |
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107 | (24) |
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5.1 Fundamentals of Markov Random Fields |
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107 | (7) |
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5.1.1 One-Dimensional Markov Processes |
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107 | (2) |
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5.1.2 Markov Random Fields |
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109 | (3) |
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5.1.3 Markov Mesh Random Fields |
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112 | (2) |
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5.2 Markov Random Field for Pattern Recognition |
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114 | (8) |
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5.2.1 Maximum a posteriori Probability |
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115 | (1) |
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5.2.2 Markov Random Fields for Modeling Statistical and Structural Information |
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116 | (1) |
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5.2.3 Neighborhood System and Cliques |
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117 | (1) |
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5.2.4 Minimizing the Likelihood Energy |
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118 | (4) |
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5.3 Recognition of Handwritten Numerals Using MRF Models |
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122 | (6) |
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122 | (2) |
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124 | (1) |
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5.3.3 Maximizing the Global Compatibility |
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125 | (3) |
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5.3.4 Experimental Results |
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128 | (1) |
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128 | (3) |
6 Markov Random Field Models for Recognizing Handwritten Words |
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131 | (14) |
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6.1 Markov Random Field for Handwritten Word Recognition |
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131 | (3) |
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6.1.1 Markov Random Field for Structural Modeling |
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132 | (1) |
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6.1.2 Recognition based on Maximum a posteriori Probability |
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133 | (1) |
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6.2 Neighborhood Systems and Cliques |
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134 | (1) |
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135 | (3) |
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6.4 Maximizing the Compatibility with Relaxation Labeling |
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138 | (2) |
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6.4.1 Relaxation Labeling |
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138 | (1) |
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6.4.2 Maximizing the Compatibilities |
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139 | (1) |
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140 | (1) |
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141 | (2) |
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141 | (1) |
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142 | (1) |
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143 | (2) |
7 A Structural and Relational Approach to Handwritten Word Recognition |
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145 | (28) |
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145 | (1) |
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7.2 Gabor Parameter Estimation |
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146 | (8) |
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154 | (14) |
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154 | (8) |
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162 | (1) |
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163 | (5) |
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7.4 Conditional Rule Generation System |
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168 | (1) |
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169 | (3) |
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172 | (1) |
8 Handwritten Word Recognition Using Fuzzy Logic |
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173 | (22) |
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173 | (1) |
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8.2 Extraction of Oriented Parts |
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173 | (1) |
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8.2.1 Slant and Tilt Correction |
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174 | (1) |
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174 | (10) |
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176 | (4) |
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8.3.2 2-D Fuzzy Membership Functions |
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180 | (4) |
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184 | (7) |
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8.4.1 Fuzzy Decision Making Process |
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186 | (5) |
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191 | (1) |
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192 | (3) |
9 Conclusion |
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195 | (28) |
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9.1 Summary and Discussions |
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195 | (2) |
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197 | (4) |
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201 | (22) |
Index |
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223 | |