Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/lstm/lstmrecognizer.h @ 21:2f43e400f144
Provide an "all" target to build both the sdist and the wheel
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Fri, 19 Sep 2025 10:28:53 +0200 |
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| rev | line source |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: lstmrecognizer.h |
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3 // Description: Top-level line recognizer class for LSTM-based networks. |
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4 // Author: Ray Smith |
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5 // |
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6 // (C) Copyright 2013, Google Inc. |
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7 // Licensed under the Apache License, Version 2.0 (the "License"); |
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8 // you may not use this file except in compliance with the License. |
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9 // You may obtain a copy of the License at |
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10 // http://www.apache.org/licenses/LICENSE-2.0 |
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11 // Unless required by applicable law or agreed to in writing, software |
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12 // distributed under the License is distributed on an "AS IS" BASIS, |
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13 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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14 // See the License for the specific language governing permissions and |
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15 // limitations under the License. |
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16 /////////////////////////////////////////////////////////////////////// |
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17 |
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18 #ifndef TESSERACT_LSTM_LSTMRECOGNIZER_H_ |
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19 #define TESSERACT_LSTM_LSTMRECOGNIZER_H_ |
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20 |
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21 #include "ccutil.h" |
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22 #include "helpers.h" |
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23 #include "matrix.h" |
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24 #include "network.h" |
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25 #include "networkscratch.h" |
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26 #include "params.h" |
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27 #include "recodebeam.h" |
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28 #include "series.h" |
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29 #include "unicharcompress.h" |
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30 |
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31 class BLOB_CHOICE_IT; |
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32 struct Pix; |
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33 class ROW_RES; |
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34 class ScrollView; |
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35 class TBOX; |
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36 class WERD_RES; |
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37 |
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38 namespace tesseract { |
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39 |
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40 class Dict; |
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41 class ImageData; |
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42 |
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43 // Enum indicating training mode control flags. |
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44 enum TrainingFlags { |
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45 TF_INT_MODE = 1, |
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46 TF_COMPRESS_UNICHARSET = 64, |
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47 }; |
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48 |
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49 // Top-level line recognizer class for LSTM-based networks. |
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50 // Note that a sub-class, LSTMTrainer is used for training. |
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51 class TESS_API LSTMRecognizer { |
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52 public: |
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53 LSTMRecognizer(); |
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54 LSTMRecognizer(const std::string &language_data_path_prefix); |
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55 ~LSTMRecognizer(); |
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56 |
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57 int NumOutputs() const { |
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58 return network_->NumOutputs(); |
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59 } |
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60 |
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61 // Return the training iterations. |
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62 int training_iteration() const { |
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63 return training_iteration_; |
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64 } |
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65 |
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66 // Return the sample iterations. |
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67 int sample_iteration() const { |
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68 return sample_iteration_; |
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69 } |
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70 |
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71 // Return the learning rate. |
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72 float learning_rate() const { |
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73 return learning_rate_; |
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74 } |
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75 |
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76 LossType OutputLossType() const { |
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77 if (network_ == nullptr) { |
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78 return LT_NONE; |
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79 } |
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80 StaticShape shape; |
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81 shape = network_->OutputShape(shape); |
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82 return shape.loss_type(); |
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83 } |
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84 bool SimpleTextOutput() const { |
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85 return OutputLossType() == LT_SOFTMAX; |
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86 } |
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87 bool IsIntMode() const { |
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88 return (training_flags_ & TF_INT_MODE) != 0; |
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89 } |
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90 // True if recoder_ is active to re-encode text to a smaller space. |
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91 bool IsRecoding() const { |
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92 return (training_flags_ & TF_COMPRESS_UNICHARSET) != 0; |
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93 } |
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94 // Returns true if the network is a TensorFlow network. |
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95 bool IsTensorFlow() const { |
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96 return network_->type() == NT_TENSORFLOW; |
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97 } |
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98 // Returns a vector of layer ids that can be passed to other layer functions |
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99 // to access a specific layer. |
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100 std::vector<std::string> EnumerateLayers() const { |
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101 ASSERT_HOST(network_ != nullptr && network_->type() == NT_SERIES); |
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102 auto *series = static_cast<Series *>(network_); |
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103 std::vector<std::string> layers; |
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104 series->EnumerateLayers(nullptr, layers); |
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105 return layers; |
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106 } |
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107 // Returns a specific layer from its id (from EnumerateLayers). |
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108 Network *GetLayer(const std::string &id) const { |
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109 ASSERT_HOST(network_ != nullptr && network_->type() == NT_SERIES); |
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110 ASSERT_HOST(id.length() > 1 && id[0] == ':'); |
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111 auto *series = static_cast<Series *>(network_); |
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112 return series->GetLayer(&id[1]); |
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113 } |
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114 // Returns the learning rate of the layer from its id. |
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115 float GetLayerLearningRate(const std::string &id) const { |
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116 ASSERT_HOST(network_ != nullptr && network_->type() == NT_SERIES); |
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117 if (network_->TestFlag(NF_LAYER_SPECIFIC_LR)) { |
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118 ASSERT_HOST(id.length() > 1 && id[0] == ':'); |
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119 auto *series = static_cast<Series *>(network_); |
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120 return series->LayerLearningRate(&id[1]); |
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121 } else { |
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122 return learning_rate_; |
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123 } |
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124 } |
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125 |
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126 // Return the network string. |
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127 const char *GetNetwork() const { |
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128 return network_str_.c_str(); |
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129 } |
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130 |
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131 // Return the adam beta. |
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132 float GetAdamBeta() const { |
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133 return adam_beta_; |
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134 } |
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135 |
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136 // Return the momentum. |
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137 float GetMomentum() const { |
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138 return momentum_; |
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139 } |
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140 |
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141 // Multiplies the all the learning rate(s) by the given factor. |
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142 void ScaleLearningRate(double factor) { |
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143 ASSERT_HOST(network_ != nullptr && network_->type() == NT_SERIES); |
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144 learning_rate_ *= factor; |
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145 if (network_->TestFlag(NF_LAYER_SPECIFIC_LR)) { |
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146 std::vector<std::string> layers = EnumerateLayers(); |
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147 for (auto &layer : layers) { |
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148 ScaleLayerLearningRate(layer, factor); |
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149 } |
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150 } |
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151 } |
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152 // Multiplies the learning rate of the layer with id, by the given factor. |
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153 void ScaleLayerLearningRate(const std::string &id, double factor) { |
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154 ASSERT_HOST(network_ != nullptr && network_->type() == NT_SERIES); |
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155 ASSERT_HOST(id.length() > 1 && id[0] == ':'); |
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156 auto *series = static_cast<Series *>(network_); |
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157 series->ScaleLayerLearningRate(&id[1], factor); |
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158 } |
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159 |
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160 // Set the all the learning rate(s) to the given value. |
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161 void SetLearningRate(float learning_rate) |
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162 { |
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163 ASSERT_HOST(network_ != nullptr && network_->type() == NT_SERIES); |
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164 learning_rate_ = learning_rate; |
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165 if (network_->TestFlag(NF_LAYER_SPECIFIC_LR)) { |
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166 for (auto &id : EnumerateLayers()) { |
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167 SetLayerLearningRate(id, learning_rate); |
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168 } |
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169 } |
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170 } |
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171 // Set the learning rate of the layer with id, by the given value. |
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172 void SetLayerLearningRate(const std::string &id, float learning_rate) |
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173 { |
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174 ASSERT_HOST(network_ != nullptr && network_->type() == NT_SERIES); |
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175 ASSERT_HOST(id.length() > 1 && id[0] == ':'); |
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176 auto *series = static_cast<Series *>(network_); |
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177 series->SetLayerLearningRate(&id[1], learning_rate); |
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178 } |
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179 |
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180 // Converts the network to int if not already. |
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181 void ConvertToInt() { |
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182 if ((training_flags_ & TF_INT_MODE) == 0) { |
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183 network_->ConvertToInt(); |
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184 training_flags_ |= TF_INT_MODE; |
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185 } |
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186 } |
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187 |
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188 // Provides access to the UNICHARSET that this classifier works with. |
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189 const UNICHARSET &GetUnicharset() const { |
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190 return ccutil_.unicharset; |
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191 } |
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192 UNICHARSET &GetUnicharset() { |
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193 return ccutil_.unicharset; |
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194 } |
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195 // Provides access to the UnicharCompress that this classifier works with. |
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196 const UnicharCompress &GetRecoder() const { |
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197 return recoder_; |
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198 } |
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199 // Provides access to the Dict that this classifier works with. |
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200 const Dict *GetDict() const { |
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201 return dict_; |
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202 } |
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203 Dict *GetDict() { |
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204 return dict_; |
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205 } |
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206 // Sets the sample iteration to the given value. The sample_iteration_ |
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207 // determines the seed for the random number generator. The training |
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208 // iteration is incremented only by a successful training iteration. |
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209 void SetIteration(int iteration) { |
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210 sample_iteration_ = iteration; |
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211 } |
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212 // Accessors for textline image normalization. |
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213 int NumInputs() const { |
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214 return network_->NumInputs(); |
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215 } |
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216 |
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217 // Return the null char index. |
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218 int null_char() const { |
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219 return null_char_; |
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220 } |
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221 |
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222 // Loads a model from mgr, including the dictionary only if lang is not null. |
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223 bool Load(const ParamsVectors *params, const std::string &lang, TessdataManager *mgr); |
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224 |
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225 // Writes to the given file. Returns false in case of error. |
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226 // If mgr contains a unicharset and recoder, then they are not encoded to fp. |
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227 bool Serialize(const TessdataManager *mgr, TFile *fp) const; |
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228 // Reads from the given file. Returns false in case of error. |
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229 // If mgr contains a unicharset and recoder, then they are taken from there, |
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230 // otherwise, they are part of the serialization in fp. |
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231 bool DeSerialize(const TessdataManager *mgr, TFile *fp); |
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232 // Loads the charsets from mgr. |
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233 bool LoadCharsets(const TessdataManager *mgr); |
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234 // Loads the Recoder. |
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235 bool LoadRecoder(TFile *fp); |
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236 // Loads the dictionary if possible from the traineddata file. |
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237 // Prints a warning message, and returns false but otherwise fails silently |
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238 // and continues to work without it if loading fails. |
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239 // Note that dictionary load is independent from DeSerialize, but dependent |
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240 // on the unicharset matching. This enables training to deserialize a model |
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241 // from checkpoint or restore without having to go back and reload the |
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242 // dictionary. |
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243 bool LoadDictionary(const ParamsVectors *params, const std::string &lang, TessdataManager *mgr); |
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244 |
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245 // Recognizes the line image, contained within image_data, returning the |
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246 // recognized tesseract WERD_RES for the words. |
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247 // If invert_threshold > 0, tries inverted as well if the normal |
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248 // interpretation doesn't produce a result which at least reaches |
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249 // that threshold. The line_box is used for computing the |
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250 // box_word in the output words. worst_dict_cert is the worst certainty that |
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251 // will be used in a dictionary word. |
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252 void RecognizeLine(const ImageData &image_data, float invert_threshold, bool debug, double worst_dict_cert, |
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253 const TBOX &line_box, PointerVector<WERD_RES> *words, int lstm_choice_mode = 0, |
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254 int lstm_choice_amount = 5); |
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255 |
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256 // Helper computes min and mean best results in the output. |
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257 void OutputStats(const NetworkIO &outputs, float *min_output, float *mean_output, float *sd); |
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258 // Recognizes the image_data, returning the labels, |
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259 // scores, and corresponding pairs of start, end x-coords in coords. |
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260 // Returned in scale_factor is the reduction factor |
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261 // between the image and the output coords, for computing bounding boxes. |
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262 // If re_invert is true, the input is inverted back to its original |
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263 // photometric interpretation if inversion is attempted but fails to |
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264 // improve the results. This ensures that outputs contains the correct |
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265 // forward outputs for the best photometric interpretation. |
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266 // inputs is filled with the used inputs to the network. |
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267 bool RecognizeLine(const ImageData &image_data, float invert_threshold, bool debug, bool re_invert, |
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268 bool upside_down, float *scale_factor, NetworkIO *inputs, NetworkIO *outputs); |
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269 |
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270 // Converts an array of labels to utf-8, whether or not the labels are |
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271 // augmented with character boundaries. |
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272 std::string DecodeLabels(const std::vector<int> &labels); |
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273 |
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274 // Displays the forward results in a window with the characters and |
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275 // boundaries as determined by the labels and label_coords. |
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276 void DisplayForward(const NetworkIO &inputs, const std::vector<int> &labels, |
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277 const std::vector<int> &label_coords, const char *window_name, |
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278 ScrollView **window); |
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279 // Converts the network output to a sequence of labels. Outputs labels, scores |
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280 // and start xcoords of each char, and each null_char_, with an additional |
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281 // final xcoord for the end of the output. |
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282 // The conversion method is determined by internal state. |
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283 void LabelsFromOutputs(const NetworkIO &outputs, std::vector<int> *labels, |
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284 std::vector<int> *xcoords); |
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285 |
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286 protected: |
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287 // Sets the random seed from the sample_iteration_; |
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288 void SetRandomSeed() { |
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289 int64_t seed = static_cast<int64_t>(sample_iteration_) * 0x10000001; |
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290 randomizer_.set_seed(seed); |
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291 randomizer_.IntRand(); |
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292 } |
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293 |
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294 // Displays the labels and cuts at the corresponding xcoords. |
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295 // Size of labels should match xcoords. |
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296 void DisplayLSTMOutput(const std::vector<int> &labels, const std::vector<int> &xcoords, |
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297 int height, ScrollView *window); |
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298 |
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299 // Prints debug output detailing the activation path that is implied by the |
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300 // xcoords. |
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301 void DebugActivationPath(const NetworkIO &outputs, const std::vector<int> &labels, |
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302 const std::vector<int> &xcoords); |
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303 |
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304 // Prints debug output detailing activations and 2nd choice over a range |
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305 // of positions. |
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306 void DebugActivationRange(const NetworkIO &outputs, const char *label, int best_choice, |
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307 int x_start, int x_end); |
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308 |
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309 // As LabelsViaCTC except that this function constructs the best path that |
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310 // contains only legal sequences of subcodes for recoder_. |
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311 void LabelsViaReEncode(const NetworkIO &output, std::vector<int> *labels, |
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312 std::vector<int> *xcoords); |
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313 // Converts the network output to a sequence of labels, with scores, using |
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314 // the simple character model (each position is a char, and the null_char_ is |
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315 // mainly intended for tail padding.) |
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316 void LabelsViaSimpleText(const NetworkIO &output, std::vector<int> *labels, |
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317 std::vector<int> *xcoords); |
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318 |
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319 // Returns a string corresponding to the label starting at start. Sets *end |
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320 // to the next start and if non-null, *decoded to the unichar id. |
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321 const char *DecodeLabel(const std::vector<int> &labels, unsigned start, unsigned *end, int *decoded); |
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322 |
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323 // Returns a string corresponding to a given single label id, falling back to |
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324 // a default of ".." for part of a multi-label unichar-id. |
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325 const char *DecodeSingleLabel(int label); |
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326 |
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327 protected: |
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328 // The network hierarchy. |
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329 Network *network_; |
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330 // The unicharset. Only the unicharset element is serialized. |
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331 // Has to be a CCUtil, so Dict can point to it. |
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332 CCUtil ccutil_; |
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333 // For backward compatibility, recoder_ is serialized iff |
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334 // training_flags_ & TF_COMPRESS_UNICHARSET. |
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335 // Further encode/decode ccutil_.unicharset's ids to simplify the unicharset. |
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336 UnicharCompress recoder_; |
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337 |
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338 // ==Training parameters that are serialized to provide a record of them.== |
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339 std::string network_str_; |
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340 // Flags used to determine the training method of the network. |
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341 // See enum TrainingFlags above. |
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342 int32_t training_flags_; |
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343 // Number of actual backward training steps used. |
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344 int32_t training_iteration_; |
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345 // Index into training sample set. sample_iteration >= training_iteration_. |
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346 int32_t sample_iteration_; |
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347 // Index in softmax of null character. May take the value UNICHAR_BROKEN or |
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348 // ccutil_.unicharset.size(). |
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349 int32_t null_char_; |
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350 // Learning rate and momentum multipliers of deltas in backprop. |
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351 float learning_rate_; |
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352 float momentum_; |
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353 // Smoothing factor for 2nd moment of gradients. |
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354 float adam_beta_; |
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355 |
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356 // === NOT SERIALIZED. |
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357 TRand randomizer_; |
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358 NetworkScratch scratch_space_; |
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359 // Language model (optional) to use with the beam search. |
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360 Dict *dict_; |
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361 // Beam search held between uses to optimize memory allocation/use. |
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362 RecodeBeamSearch *search_; |
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363 |
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364 // == Debugging parameters.== |
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365 // Recognition debug display window. |
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366 ScrollView *debug_win_; |
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367 }; |
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368 |
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369 } // namespace tesseract. |
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370 |
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371 #endif // TESSERACT_LSTM_LSTMRECOGNIZER_H_ |
