Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/lstm/recodebeam.h @ 46:7ee69f120f19 default tip
>>>>> tag v1.26.5+1 for changeset b74429b0f5c4
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Sat, 11 Oct 2025 17:17:30 +0200 |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: recodebeam.h |
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3 // Description: Beam search to decode from the re-encoded CJK as a sequence of |
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4 // smaller numbers in place of a single large code. |
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5 // Author: Ray Smith |
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6 // |
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7 // (C) Copyright 2015, Google Inc. |
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8 // Licensed under the Apache License, Version 2.0 (the "License"); |
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9 // you may not use this file except in compliance with the License. |
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10 // You may obtain a copy of the License at |
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11 // http://www.apache.org/licenses/LICENSE-2.0 |
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12 // Unless required by applicable law or agreed to in writing, software |
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13 // distributed under the License is distributed on an "AS IS" BASIS, |
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14 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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15 // See the License for the specific language governing permissions and |
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16 // limitations under the License. |
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17 // |
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18 /////////////////////////////////////////////////////////////////////// |
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19 |
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20 #ifndef THIRD_PARTY_TESSERACT_LSTM_RECODEBEAM_H_ |
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21 #define THIRD_PARTY_TESSERACT_LSTM_RECODEBEAM_H_ |
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22 |
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23 #include "dawg.h" |
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24 #include "dict.h" |
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25 #include "genericheap.h" |
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26 #include "genericvector.h" |
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27 #include "kdpair.h" |
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28 #include "networkio.h" |
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29 #include "ratngs.h" |
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30 #include "unicharcompress.h" |
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31 |
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32 #include <unordered_set> // for std::unordered_set |
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33 #include <vector> // for std::vector |
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34 |
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35 namespace tesseract { |
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36 |
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37 // Enum describing what can follow the current node. |
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38 // Consider the following softmax outputs: |
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39 // Timestep 0 1 2 3 4 5 6 7 8 |
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40 // X-score 0.01 0.55 0.98 0.42 0.01 0.01 0.40 0.95 0.01 |
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41 // Y-score 0.00 0.01 0.01 0.01 0.01 0.97 0.59 0.04 0.01 |
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42 // Null-score 0.99 0.44 0.01 0.57 0.98 0.02 0.01 0.01 0.98 |
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43 // Then the correct CTC decoding (in which adjacent equal classes are folded, |
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44 // and then all nulls are dropped) is clearly XYX, but simple decoding (taking |
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45 // the max at each timestep) leads to: |
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46 // Null@0.99 X@0.55 X@0.98 Null@0.57 Null@0.98 Y@0.97 Y@0.59 X@0.95 Null@0.98, |
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47 // which folds to the correct XYX. The conversion to Tesseract rating and |
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48 // certainty uses the sum of the log probs (log of the product of probabilities) |
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49 // for the Rating and the minimum log prob for the certainty, but that yields a |
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50 // minimum certainty of log(0.55), which is poor for such an obvious case. |
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51 // CTC says that the probability of the result is the SUM of the products of the |
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52 // probabilities over ALL PATHS that decode to the same result, which includes: |
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53 // NXXNNYYXN, NNXNNYYN, NXXXNYYXN, NNXXNYXXN, and others including XXXXXYYXX. |
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54 // That is intractable, so some compromise between simple and ideal is needed. |
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55 // Observing that evenly split timesteps rarely happen next to each other, we |
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56 // allow scores at a transition between classes to be added for decoding thus: |
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57 // N@0.99 (N+X)@0.99 X@0.98 (N+X)@0.99 N@0.98 Y@0.97 (X+Y+N)@1.00 X@0.95 N@0.98. |
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58 // This works because NNX and NXX both decode to X, so in the middle we can use |
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59 // N+X. Note that the classes either side of a sum must stand alone, i.e. use a |
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60 // single score, to force all paths to pass through them and decode to the same |
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61 // result. Also in the special case of a transition from X to Y, with only one |
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62 // timestep between, it is possible to add X+Y+N, since XXY, XYY, and XNY all |
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63 // decode to XY. |
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64 // An important condition is that we cannot combine X and Null between two |
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65 // stand-alone Xs, since that can decode as XNX->XX or XXX->X, so the scores for |
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66 // X and Null have to go in separate paths. Combining scores in this way |
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67 // provides a much better minimum certainty of log(0.95). |
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68 // In the implementation of the beam search, we have to place the possibilities |
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69 // X, X+N and X+Y+N in the beam under appropriate conditions of the previous |
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70 // node, and constrain what can follow, to enforce the rules explained above. |
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71 // We therefore have 3 different types of node determined by what can follow: |
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72 enum NodeContinuation { |
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73 NC_ANYTHING, // This node used just its own score, so anything can follow. |
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74 NC_ONLY_DUP, // The current node combined another score with the score for |
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75 // itself, without a stand-alone duplicate before, so must be |
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76 // followed by a stand-alone duplicate. |
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77 NC_NO_DUP, // The current node combined another score with the score for |
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78 // itself, after a stand-alone, so can only be followed by |
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79 // something other than a duplicate of the current node. |
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80 NC_COUNT |
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81 }; |
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82 |
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83 // Enum describing the top-n status of a code. |
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84 enum TopNState { |
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85 TN_TOP2, // Winner or 2nd. |
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86 TN_TOPN, // Runner up in top-n, but not 1st or 2nd. |
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87 TN_ALSO_RAN, // Not in the top-n. |
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88 TN_COUNT |
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89 }; |
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90 |
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91 // Lattice element for Re-encode beam search. |
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92 struct RecodeNode { |
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93 RecodeNode() |
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94 : code(-1) |
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95 , unichar_id(INVALID_UNICHAR_ID) |
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96 , permuter(TOP_CHOICE_PERM) |
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97 , start_of_dawg(false) |
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98 , start_of_word(false) |
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99 , end_of_word(false) |
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100 , duplicate(false) |
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101 , certainty(0.0f) |
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102 , score(0.0f) |
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103 , prev(nullptr) |
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104 , dawgs(nullptr) |
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105 , code_hash(0) {} |
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106 RecodeNode(int c, int uni_id, PermuterType perm, bool dawg_start, bool word_start, bool end, |
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107 bool dup, float cert, float s, const RecodeNode *p, DawgPositionVector *d, |
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108 uint64_t hash) |
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109 : code(c) |
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110 , unichar_id(uni_id) |
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111 , permuter(perm) |
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112 , start_of_dawg(dawg_start) |
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113 , start_of_word(word_start) |
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114 , end_of_word(end) |
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115 , duplicate(dup) |
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116 , certainty(cert) |
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117 , score(s) |
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118 , prev(p) |
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119 , dawgs(d) |
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120 , code_hash(hash) {} |
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121 // NOTE: If we could use C++11, then this would be a move constructor. |
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122 // Instead we have copy constructor that does a move!! This is because we |
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123 // don't want to copy the whole DawgPositionVector each time, and true |
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124 // copying isn't necessary for this struct. It does get moved around a lot |
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125 // though inside the heap and during heap push, hence the move semantics. |
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126 RecodeNode(const RecodeNode &src) : dawgs(nullptr) { |
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127 *this = src; |
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128 ASSERT_HOST(src.dawgs == nullptr); |
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129 } |
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130 RecodeNode &operator=(const RecodeNode &src) { |
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131 delete dawgs; |
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132 memcpy(this, &src, sizeof(src)); |
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133 ((RecodeNode &)src).dawgs = nullptr; |
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134 return *this; |
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135 } |
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136 ~RecodeNode() { |
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137 delete dawgs; |
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138 } |
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139 // Prints details of the node. |
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140 void Print(int null_char, const UNICHARSET &unicharset, int depth) const; |
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141 |
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142 // The re-encoded code here = index to network output. |
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143 int code; |
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144 // The decoded unichar_id is only valid for the final code of a sequence. |
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145 int unichar_id; |
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146 // The type of permuter active at this point. Intervals between start_of_word |
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147 // and end_of_word make valid words of type given by permuter where |
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148 // end_of_word is true. These aren't necessarily delimited by spaces. |
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149 PermuterType permuter; |
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150 // True if this is the initial dawg state. May be attached to a space or, |
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151 // in a non-space-delimited lang, the end of the previous word. |
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152 bool start_of_dawg; |
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153 // True if this is the first node in a dictionary word. |
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154 bool start_of_word; |
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155 // True if this represents a valid candidate end of word position. Does not |
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156 // necessarily mark the end of a word, since a word can be extended beyond a |
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157 // candidate end by a continuation, eg 'the' continues to 'these'. |
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158 bool end_of_word; |
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159 // True if this->code is a duplicate of prev->code. Some training modes |
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160 // allow the network to output duplicate characters and crush them with CTC, |
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161 // but that would mess up the dictionary search, so we just smash them |
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162 // together on the fly using the duplicate flag. |
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163 bool duplicate; |
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164 // Certainty (log prob) of (just) this position. |
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165 float certainty; |
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166 // Total certainty of the path to this position. |
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167 float score; |
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168 // The previous node in this chain. Borrowed pointer. |
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169 const RecodeNode *prev; |
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170 // The currently active dawgs at this position. Owned pointer. |
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171 DawgPositionVector *dawgs; |
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172 // A hash of all codes in the prefix and this->code as well. Used for |
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173 // duplicate path removal. |
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174 uint64_t code_hash; |
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175 }; |
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176 |
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177 using RecodePair = KDPairInc<double, RecodeNode>; |
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178 using RecodeHeap = GenericHeap<RecodePair>; |
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179 |
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180 // Class that holds the entire beam search for recognition of a text line. |
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181 class TESS_API RecodeBeamSearch { |
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182 public: |
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183 // Borrows the pointer, which is expected to survive until *this is deleted. |
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184 RecodeBeamSearch(const UnicharCompress &recoder, int null_char, bool simple_text, Dict *dict); |
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185 ~RecodeBeamSearch(); |
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186 |
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187 // Decodes the set of network outputs, storing the lattice internally. |
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188 // If charset is not null, it enables detailed debugging of the beam search. |
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189 void Decode(const NetworkIO &output, double dict_ratio, double cert_offset, |
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190 double worst_dict_cert, const UNICHARSET *charset, int lstm_choice_mode = 0); |
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191 void Decode(const GENERIC_2D_ARRAY<float> &output, double dict_ratio, double cert_offset, |
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192 double worst_dict_cert, const UNICHARSET *charset); |
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193 |
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194 void DecodeSecondaryBeams(const NetworkIO &output, double dict_ratio, double cert_offset, |
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195 double worst_dict_cert, const UNICHARSET *charset, |
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196 int lstm_choice_mode = 0); |
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197 |
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198 // Returns the best path as labels/scores/xcoords similar to simple CTC. |
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199 void ExtractBestPathAsLabels(std::vector<int> *labels, std::vector<int> *xcoords) const; |
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200 // Returns the best path as unichar-ids/certs/ratings/xcoords skipping |
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201 // duplicates, nulls and intermediate parts. |
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202 void ExtractBestPathAsUnicharIds(bool debug, const UNICHARSET *unicharset, |
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203 std::vector<int> *unichar_ids, std::vector<float> *certs, |
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204 std::vector<float> *ratings, std::vector<int> *xcoords) const; |
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205 |
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206 // Returns the best path as a set of WERD_RES. |
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207 void ExtractBestPathAsWords(const TBOX &line_box, float scale_factor, bool debug, |
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208 const UNICHARSET *unicharset, PointerVector<WERD_RES> *words, |
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209 int lstm_choice_mode = 0); |
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210 |
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211 // Generates debug output of the content of the beams after a Decode. |
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212 void DebugBeams(const UNICHARSET &unicharset) const; |
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213 |
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214 // Extract the best characters from the current decode iteration and block |
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215 // those symbols for the next iteration. In contrast to Tesseract's standard |
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216 // method to chose the best overall node chain, this methods looks at a short |
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217 // node chain segmented by the character boundaries and chooses the best |
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218 // option independent of the remaining node chain. |
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219 void extractSymbolChoices(const UNICHARSET *unicharset); |
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220 |
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221 // Generates debug output of the content of the beams after a Decode. |
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222 void PrintBeam2(bool uids, int num_outputs, const UNICHARSET *charset, bool secondary) const; |
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223 // Segments the timestep bundle by the character_boundaries. |
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224 void segmentTimestepsByCharacters(); |
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225 std::vector<std::vector<std::pair<const char *, float>>> |
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226 // Unions the segmented timestep character bundles to one big bundle. |
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227 combineSegmentedTimesteps( |
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228 std::vector<std::vector<std::vector<std::pair<const char *, float>>>> *segmentedTimesteps); |
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229 // Stores the alternative characters of every timestep together with their |
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230 // probability. |
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231 std::vector<std::vector<std::pair<const char *, float>>> timesteps; |
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232 std::vector<std::vector<std::vector<std::pair<const char *, float>>>> segmentedTimesteps; |
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233 // Stores the character choices found in the ctc algorithm |
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234 std::vector<std::vector<std::pair<const char *, float>>> ctc_choices; |
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235 // Stores all unicharids which are excluded for future iterations |
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236 std::vector<std::unordered_set<int>> excludedUnichars; |
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237 // Stores the character boundaries regarding timesteps. |
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238 std::vector<int> character_boundaries_; |
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239 // Clipping value for certainty inside Tesseract. Reflects the minimum value |
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240 // of certainty that will be returned by ExtractBestPathAsUnicharIds. |
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241 // Supposedly on a uniform scale that can be compared across languages and |
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242 // engines. |
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243 static constexpr float kMinCertainty = -20.0f; |
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244 // Number of different code lengths for which we have a separate beam. |
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245 static const int kNumLengths = RecodedCharID::kMaxCodeLen + 1; |
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246 // Total number of beams: dawg/nodawg * number of NodeContinuation * number |
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247 // of different lengths. |
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248 static const int kNumBeams = 2 * NC_COUNT * kNumLengths; |
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249 // Returns the relevant factor in the beams_ index. |
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250 static int LengthFromBeamsIndex(int index) { |
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251 return index % kNumLengths; |
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252 } |
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253 static NodeContinuation ContinuationFromBeamsIndex(int index) { |
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254 return static_cast<NodeContinuation>((index / kNumLengths) % NC_COUNT); |
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255 } |
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256 static bool IsDawgFromBeamsIndex(int index) { |
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257 return index / (kNumLengths * NC_COUNT) > 0; |
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258 } |
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259 // Computes a beams_ index from the given factors. |
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260 static int BeamIndex(bool is_dawg, NodeContinuation cont, int length) { |
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261 return (is_dawg * NC_COUNT + cont) * kNumLengths + length; |
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262 } |
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263 |
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264 private: |
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265 // Struct for the Re-encode beam search. This struct holds the data for |
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266 // a single time-step position of the output. Use a vector<RecodeBeam> |
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267 // to hold all the timesteps and prevent reallocation of the individual heaps. |
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268 struct RecodeBeam { |
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269 // Resets to the initial state without deleting all the memory. |
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270 void Clear() { |
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271 for (auto &beam : beams_) { |
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272 beam.clear(); |
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273 } |
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274 RecodeNode empty; |
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275 for (auto &best_initial_dawg : best_initial_dawgs_) { |
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276 best_initial_dawg = empty; |
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277 } |
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278 } |
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279 |
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280 // A separate beam for each combination of code length, |
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281 // NodeContinuation, and dictionary flag. Separating out all these types |
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282 // allows the beam to be quite narrow, and yet still have a low chance of |
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283 // losing the best path. |
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284 // We have to keep all these beams separate, since the highest scoring paths |
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285 // come from the paths that are most likely to dead-end at any time, like |
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286 // dawg paths, NC_ONLY_DUP etc. |
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287 // Each heap is stored with the WORST result at the top, so we can quickly |
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288 // get the top-n values. |
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289 RecodeHeap beams_[kNumBeams]; |
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290 // While the language model is only a single word dictionary, we can use |
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291 // word starts as a choke point in the beam, and keep only a single dict |
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292 // start node at each step (for each NodeContinuation type), so we find the |
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293 // best one here and push it on the heap, if it qualifies, after processing |
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294 // all of the step. |
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295 RecodeNode best_initial_dawgs_[NC_COUNT]; |
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296 }; |
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297 using TopPair = KDPairInc<float, int>; |
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298 |
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299 // Generates debug output of the content of a single beam position. |
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300 void DebugBeamPos(const UNICHARSET &unicharset, const RecodeHeap &heap) const; |
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301 |
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302 // Returns the given best_nodes as unichar-ids/certs/ratings/xcoords skipping |
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303 // duplicates, nulls and intermediate parts. |
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304 static void ExtractPathAsUnicharIds(const std::vector<const RecodeNode *> &best_nodes, |
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305 std::vector<int> *unichar_ids, std::vector<float> *certs, |
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306 std::vector<float> *ratings, std::vector<int> *xcoords, |
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307 std::vector<int> *character_boundaries = nullptr); |
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308 |
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309 // Sets up a word with the ratings matrix and fake blobs with boxes in the |
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310 // right places. |
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311 WERD_RES *InitializeWord(bool leading_space, const TBOX &line_box, int word_start, int word_end, |
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312 float space_certainty, const UNICHARSET *unicharset, |
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313 const std::vector<int> &xcoords, float scale_factor); |
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314 |
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315 // Fills top_n_flags_ with bools that are true iff the corresponding output |
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316 // is one of the top_n. |
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317 void ComputeTopN(const float *outputs, int num_outputs, int top_n); |
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318 |
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319 void ComputeSecTopN(std::unordered_set<int> *exList, const float *outputs, int num_outputs, |
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320 int top_n); |
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321 |
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322 // Adds the computation for the current time-step to the beam. Call at each |
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323 // time-step in sequence from left to right. outputs is the activation vector |
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324 // for the current timestep. |
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325 void DecodeStep(const float *outputs, int t, double dict_ratio, double cert_offset, |
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326 double worst_dict_cert, const UNICHARSET *charset, bool debug = false); |
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327 |
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328 void DecodeSecondaryStep(const float *outputs, int t, double dict_ratio, double cert_offset, |
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329 double worst_dict_cert, const UNICHARSET *charset, bool debug = false); |
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330 |
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331 // Saves the most certain choices for the current time-step. |
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332 void SaveMostCertainChoices(const float *outputs, int num_outputs, const UNICHARSET *charset, |
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333 int xCoord); |
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334 |
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335 // Calculates more accurate character boundaries which can be used to |
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336 // provide more accurate alternative symbol choices. |
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337 static void calculateCharBoundaries(std::vector<int> *starts, std::vector<int> *ends, |
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338 std::vector<int> *character_boundaries_, int maxWidth); |
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339 |
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340 // Adds to the appropriate beams the legal (according to recoder) |
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341 // continuations of context prev, which is from the given index to beams_, |
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342 // using the given network outputs to provide scores to the choices. Uses only |
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343 // those choices for which top_n_flags[code] == top_n_flag. |
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344 void ContinueContext(const RecodeNode *prev, int index, const float *outputs, |
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345 TopNState top_n_flag, const UNICHARSET *unicharset, double dict_ratio, |
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346 double cert_offset, double worst_dict_cert, RecodeBeam *step); |
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347 // Continues for a new unichar, using dawg or non-dawg as per flag. |
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348 void ContinueUnichar(int code, int unichar_id, float cert, float worst_dict_cert, |
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349 float dict_ratio, bool use_dawgs, NodeContinuation cont, |
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350 const RecodeNode *prev, RecodeBeam *step); |
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351 // Adds a RecodeNode composed of the args to the correct heap in step if |
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352 // unichar_id is a valid dictionary continuation of whatever is in prev. |
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353 void ContinueDawg(int code, int unichar_id, float cert, NodeContinuation cont, |
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354 const RecodeNode *prev, RecodeBeam *step); |
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355 // Sets the correct best_initial_dawgs_ with a RecodeNode composed of the args |
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356 // if better than what is already there. |
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357 void PushInitialDawgIfBetter(int code, int unichar_id, PermuterType permuter, bool start, |
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358 bool end, float cert, NodeContinuation cont, const RecodeNode *prev, |
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359 RecodeBeam *step); |
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360 // Adds a RecodeNode composed of the args to the correct heap in step for |
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361 // partial unichar or duplicate if there is room or if better than the |
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362 // current worst element if already full. |
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363 void PushDupOrNoDawgIfBetter(int length, bool dup, int code, int unichar_id, float cert, |
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364 float worst_dict_cert, float dict_ratio, bool use_dawgs, |
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365 NodeContinuation cont, const RecodeNode *prev, RecodeBeam *step); |
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366 // Adds a RecodeNode composed of the args to the correct heap in step if there |
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367 // is room or if better than the current worst element if already full. |
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368 void PushHeapIfBetter(int max_size, int code, int unichar_id, PermuterType permuter, |
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369 bool dawg_start, bool word_start, bool end, bool dup, float cert, |
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370 const RecodeNode *prev, DawgPositionVector *d, RecodeHeap *heap); |
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371 // Adds a RecodeNode to heap if there is room |
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372 // or if better than the current worst element if already full. |
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373 void PushHeapIfBetter(int max_size, RecodeNode *node, RecodeHeap *heap); |
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374 // Searches the heap for an entry matching new_node, and updates the entry |
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375 // with reshuffle if needed. Returns true if there was a match. |
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376 bool UpdateHeapIfMatched(RecodeNode *new_node, RecodeHeap *heap); |
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377 // Computes and returns the code-hash for the given code and prev. |
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378 uint64_t ComputeCodeHash(int code, bool dup, const RecodeNode *prev) const; |
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379 // Backtracks to extract the best path through the lattice that was built |
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380 // during Decode. On return the best_nodes vector essentially contains the set |
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381 // of code, score pairs that make the optimal path with the constraint that |
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382 // the recoder can decode the code sequence back to a sequence of unichar-ids. |
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383 void ExtractBestPaths(std::vector<const RecodeNode *> *best_nodes, |
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384 std::vector<const RecodeNode *> *second_nodes) const; |
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385 // Helper backtracks through the lattice from the given node, storing the |
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386 // path and reversing it. |
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387 void ExtractPath(const RecodeNode *node, std::vector<const RecodeNode *> *path) const; |
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388 void ExtractPath(const RecodeNode *node, std::vector<const RecodeNode *> *path, |
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389 int limiter) const; |
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390 // Helper prints debug information on the given lattice path. |
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391 void DebugPath(const UNICHARSET *unicharset, const std::vector<const RecodeNode *> &path) const; |
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392 // Helper prints debug information on the given unichar path. |
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393 void DebugUnicharPath(const UNICHARSET *unicharset, const std::vector<const RecodeNode *> &path, |
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394 const std::vector<int> &unichar_ids, const std::vector<float> &certs, |
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395 const std::vector<float> &ratings, const std::vector<int> &xcoords) const; |
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396 |
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397 static const int kBeamWidths[RecodedCharID::kMaxCodeLen + 1]; |
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398 |
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399 // The encoder/decoder that we will be using. |
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|
400 const UnicharCompress &recoder_; |
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401 // The beam for each timestep in the output. |
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402 std::vector<RecodeBeam *> beam_; |
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403 // Secondary Beam for Results with less Probability |
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|
404 std::vector<RecodeBeam *> secondary_beam_; |
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|
405 // The number of timesteps valid in beam_; |
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406 int beam_size_; |
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407 // A flag to indicate which outputs are the top-n choices. Current timestep |
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408 // only. |
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409 std::vector<TopNState> top_n_flags_; |
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410 // A record of the highest and second scoring codes. |
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411 int top_code_; |
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412 int second_code_; |
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413 // Heap used to compute the top_n_flags_. |
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|
414 GenericHeap<TopPair> top_heap_; |
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415 // Borrowed pointer to the dictionary to use in the search. |
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416 Dict *dict_; |
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417 // True if the language is space-delimited, which is true for most languages |
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418 // except chi*, jpn, tha. |
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419 bool space_delimited_; |
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420 // True if the input is simple text, ie adjacent equal chars are not to be |
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421 // eliminated. |
|
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parents:
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|
422 bool is_simple_text_; |
|
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parents:
diff
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423 // The encoded (class label) of the null/reject character. |
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b50eed0cc0ef
ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
Franz Glasner <fzglas.hg@dom66.de>
parents:
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424 int null_char_; |
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b50eed0cc0ef
ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
Franz Glasner <fzglas.hg@dom66.de>
parents:
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425 }; |
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b50eed0cc0ef
ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
Franz Glasner <fzglas.hg@dom66.de>
parents:
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426 |
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b50eed0cc0ef
ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
Franz Glasner <fzglas.hg@dom66.de>
parents:
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427 } // namespace tesseract. |
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b50eed0cc0ef
ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
Franz Glasner <fzglas.hg@dom66.de>
parents:
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changeset
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428 |
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b50eed0cc0ef
ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
Franz Glasner <fzglas.hg@dom66.de>
parents:
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429 #endif // THIRD_PARTY_TESSERACT_LSTM_RECODEBEAM_H_ |
