Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/leptonica/src/recog.h @ 40:aa33339d6b8a upstream

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ADD: MuPDF v1.26.10: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.5.
author Franz Glasner <fzglas.hg@dom66.de>
date Sat, 11 Oct 2025 11:31:38 +0200
parents b50eed0cc0ef
children
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1 /*====================================================================*
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2 - Copyright (C) 2001 Leptonica. All rights reserved.
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3 -
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4 - Redistribution and use in source and binary forms, with or without
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5 - modification, are permitted provided that the following conditions
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6 - are met:
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7 - 1. Redistributions of source code must retain the above copyright
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8 - notice, this list of conditions and the following disclaimer.
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9 - 2. Redistributions in binary form must reproduce the above
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10 - copyright notice, this list of conditions and the following
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11 - disclaimer in the documentation and/or other materials
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12 - provided with the distribution.
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13 -
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14 - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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15 - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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16 - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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17 - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ANY
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18 - CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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19 - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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20 - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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21 - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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22 - OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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23 - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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24 - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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25 *====================================================================*/
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26
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27 #ifndef LEPTONICA_RECOG_H
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28 #define LEPTONICA_RECOG_H
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29
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30 /*!
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31 * \file recog.h
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32 *
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33 * <pre>
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34 * This is a simple utility for training and recognizing individual
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35 * machine-printed text characters. It is designed to be adapted
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36 * to a particular set of character images; e.g., from a book.
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37 *
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38 * There are two methods of training the recognizer. In the most
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39 * simple, a set of bitmaps has been labeled by some means, such
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40 * a generic OCR program. This is input either one template at a time
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41 * or as a pixa of templates, to a function that creates a recog.
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42 * If in a pixa, the text string label must be embedded in the
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43 * text field of each pix.
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44 *
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45 * If labeled data is not available, we start with a bootstrap
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46 * recognizer (BSR) that has labeled data from a variety of sources.
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47 * These images are scaled, typically to a fixed height, and then
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48 * fed similarly scaled unlabeled images from the source (e.g., book),
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49 * and the BSR attempts to identify them. All images that have
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50 * a high enough correlation score with one of the templates in the
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51 * BSR are emitted in a pixa, which now holds unscaled and labeled
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52 * templates from the source. This is the generator for a book adapted
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53 * recognizer (BAR).
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54 *
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55 * The pixa should always be thought of as the primary structure.
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56 * It is the generator for the recog, because a recog is built
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57 * from a pixa of unscaled images.
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58 *
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59 * New image templates can be added to a recog as long as it is
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60 * in training mode. Once training is finished, to add templates
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61 * it is necessary to extract the generating pixa, add templates
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62 * to that pixa, and make a new recog. Similarly, we do not
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63 * join two recog; instead, we simply join their generating pixa,
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64 * and make a recog from that.
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65 *
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66 * To remove outliers from a pixa of labeled pix, make a recog,
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67 * determine the outliers, and generate a new pixa with the
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68 * outliers removed. The outliers are determined by building
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69 * special templates for each character set that are scaled averages
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70 * of the individual templates. Then a correlation score is found
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71 * between each template and the averaged templates. There are
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72 * two implementations; outliers are determined as either:
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73 * (1) a template having a correlation score with its class average
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74 * that is below a threshold, or
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75 * (2) a template having a correlation score with its class average
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76 * that is smaller than the correlation score with the average
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77 * of another class.
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78 * Outliers are removed from the generating pixa. Scaled averaging
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79 * is only performed for determining outliers and for splitting
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80 * characters; it is never used in a trained recognizer for identifying
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81 * unlabeled samples.
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82 *
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83 * Two methods using averaged templates are provided for splitting
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84 * touching characters:
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85 * (1) greedy matching
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86 * (2) document image decoding (DID)
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87 * The DID method is the default. It is about 5x faster and
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88 * possibly more accurate.
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89 *
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90 * Once a BAR has been made, unlabeled sample images are identified
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91 * by finding the individual template in the BAR with highest
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92 * correlation. The input images and images in the BAR can be
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93 * represented in two ways:
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94 * (1) as scanned, binarized to 1 bpp
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95 * (2) as a width-normalized outline formed by thinning to a
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96 * skeleton and then dilating by a fixed amount.
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97 *
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98 * The recog can be serialized to file and read back. The serialized
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99 * version holds the templates used for correlation (which may have
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100 * been modified by scaling and turning into lines from the unscaled
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101 * templates), plus, for arbitrary character sets, the UTF8
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102 * representation and the lookup table mapping from the character
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103 * representation to index.
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104 *
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105 * Why do we not use averaged templates for recognition?
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106 * Letterforms can take on significantly different shapes (eg.,
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107 * the letters 'a' and 'g'), and it makes no sense to average these.
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108 * The previous version of this utility allowed multiple recognizers
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109 * to exist, but this is an unnecessary complication if recognition
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110 * is done on all samples instead of on averages.
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111 * </pre>
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112 */
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113
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114 #define RECOG_VERSION_NUMBER 2
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115
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116 struct L_Recog {
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117 l_int32 scalew; /*!< scale all examples to this width; */
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118 /*!< use 0 prevent horizontal scaling */
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119 l_int32 scaleh; /*!< scale all examples to this height; */
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120 /*!< use 0 prevent vertical scaling */
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121 l_int32 linew; /*!< use a value > 0 to convert the bitmap */
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122 /*!< to lines of fixed width; 0 to skip */
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123 l_int32 templ_use; /*!< template use: use either the average */
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124 /*!< or all temmplates (L_USE_AVERAGE or */
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125 /*!< L_USE_ALL) */
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126 l_int32 maxarraysize; /*!< initialize container arrays to this */
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127 l_int32 setsize; /*!< size of character set */
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128 l_int32 threshold; /*!< for binarizing if depth > 1 */
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129 l_int32 maxyshift; /*!< vertical jiggle on nominal centroid */
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130 /*!< alignment; typically 0 or 1 */
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131 l_int32 charset_type; /*!< one of L_ARABIC_NUMERALS, etc. */
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132 l_int32 charset_size; /*!< expected number of classes in charset */
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133 l_int32 min_nopad; /*!< min number of samples without padding */
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134 l_int32 num_samples; /*!< number of training samples */
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135 l_int32 minwidth_u; /*!< min width averaged unscaled templates */
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136 l_int32 maxwidth_u; /*!< max width averaged unscaled templates */
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137 l_int32 minheight_u; /*!< min height averaged unscaled templates */
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138 l_int32 maxheight_u; /*!< max height averaged unscaled templates */
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139 l_int32 minwidth; /*!< min width averaged scaled templates */
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140 l_int32 maxwidth; /*!< max width averaged scaled templates */
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141 l_int32 ave_done; /*!< set to 1 when averaged bitmaps are made */
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142 l_int32 train_done; /*!< set to 1 when training is complete or */
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143 /*!< identification has started */
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144 l_float32 max_wh_ratio; /*!< max width/height ratio to split */
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145 l_float32 max_ht_ratio; /*!< max of max/min template height ratio */
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146 l_int32 min_splitw; /*!< min component width kept in splitting */
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147 l_int32 max_splith; /*!< max component height kept in splitting */
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148 struct Sarray *sa_text; /*!< text array for arbitrary char set */
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149 struct L_Dna *dna_tochar; /*!< index-to-char lut for arbitrary charset */
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150 l_int32 *centtab; /*!< table for finding centroids */
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151 l_int32 *sumtab; /*!< table for finding pixel sums */
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152 struct Pixaa *pixaa_u; /*!< all unscaled templates for each class */
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153 struct Ptaa *ptaa_u; /*!< centroids of all unscaled templates */
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154 struct Numaa *naasum_u; /*!< area of all unscaled templates */
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155 struct Pixaa *pixaa; /*!< all (scaled) templates for each class */
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156 struct Ptaa *ptaa; /*!< centroids of all (scaledl) templates */
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157 struct Numaa *naasum; /*!< area of all (scaled) templates */
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158 struct Pixa *pixa_u; /*!< averaged unscaled templates per class */
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159 struct Pta *pta_u; /*!< centroids of unscaled ave. templates */
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160 struct Numa *nasum_u; /*!< area of unscaled averaged templates */
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161 struct Pixa *pixa; /*!< averaged (scaled) templates per class */
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162 struct Pta *pta; /*!< centroids of (scaled) ave. templates */
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163 struct Numa *nasum; /*!< area of (scaled) averaged templates */
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164 struct Pixa *pixa_tr; /*!< all input training images */
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165 struct Pixa *pixadb_ave; /*!< unscaled and scaled averaged bitmaps */
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166 struct Pixa *pixa_id; /*!< input images for identifying */
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167 struct Pix *pixdb_ave; /*!< debug: best match of input against ave. */
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168 struct Pix *pixdb_range; /*!< debug: best matches within range */
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169 struct Pixa *pixadb_boot; /*!< debug: bootstrap training results */
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170 struct Pixa *pixadb_split; /*!< debug: splitting results */
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171 struct L_Bmf *bmf; /*!< bmf fonts */
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172 l_int32 bmf_size; /*!< font size of bmf; default is 6 pt */
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173 struct L_Rdid *did; /*!< temp data used for image decoding */
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174 struct L_Rch *rch; /*!< temp data used for holding best char */
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175 struct L_Rcha *rcha; /*!< temp data used for array of best chars */
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176 };
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177 typedef struct L_Recog L_RECOG;
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178
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179 /*!
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180 * Data returned from correlation matching on a single character
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181 */
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182 struct L_Rch {
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183 l_int32 index; /*!< index of best template */
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184 l_float32 score; /*!< correlation score of best template */
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185 char *text; /*!< character string of best template */
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186 l_int32 sample; /*!< index of best sample (within the best */
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187 /*!< template class, if all samples are used) */
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188 l_int32 xloc; /*!< x-location of template (delx + shiftx) */
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189 l_int32 yloc; /*!< y-location of template (dely + shifty) */
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190 l_int32 width; /*!< width of best template */
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191 };
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192 typedef struct L_Rch L_RCH;
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193
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194 /*!
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195 * Data returned from correlation matching on an array of characters
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196 */
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197 struct L_Rcha {
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198 struct Numa *naindex; /*!< indices of best templates */
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199 struct Numa *nascore; /*!< correlation scores of best templates */
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200 struct Sarray *satext; /*!< character strings of best templates */
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201 struct Numa *nasample; /*!< indices of best samples */
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202 struct Numa *naxloc; /*!< x-locations of templates (delx + shiftx) */
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203 struct Numa *nayloc; /*!< y-locations of templates (dely + shifty) */
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204 struct Numa *nawidth; /*!< widths of best templates */
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205 };
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206 typedef struct L_Rcha L_RCHA;
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207
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208 /*!
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209 * Data used for decoding a line of characters.
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210 */
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211 struct L_Rdid {
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212 struct Pix *pixs; /*!< clone of pix to be decoded */
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213 l_int32 **counta; /*!< count array for each averaged template */
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214 l_int32 **delya; /*!< best y-shift array per average template */
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215 l_int32 narray; /*!< number of averaged templates */
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216 l_int32 size; /*!< size of count array (width of pixs) */
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217 l_int32 *setwidth; /*!< setwidths for each template */
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218 struct Numa *nasum; /*!< pixel count in pixs by column */
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219 struct Numa *namoment; /*!< first moment of pixels in pixs by cols */
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220 l_int32 fullarrays; /*!< 1 if full arrays are made; 0 otherwise */
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221 l_float32 *beta; /*!< channel coeffs for template fg term */
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222 l_float32 *gamma; /*!< channel coeffs for bit-and term */
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223 l_float32 *trellisscore; /*!< score on trellis */
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224 l_int32 *trellistempl; /*!< template on trellis (for backtrack) */
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225 struct Numa *natempl; /*!< indices of best path templates */
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226 struct Numa *naxloc; /*!< x locations of best path templates */
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227 struct Numa *nadely; /*!< y locations of best path templates */
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228 struct Numa *nawidth; /*!< widths of best path templates */
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229 struct Boxa *boxa; /*!< Viterbi result for splitting input pixs */
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230 struct Numa *nascore; /*!< correlation scores: best path templates */
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231 struct Numa *natempl_r; /*!< indices of best rescored templates */
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232 struct Numa *nasample_r; /*!< samples of best scored templates */
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233 struct Numa *naxloc_r; /*!< x locations of best rescoredtemplates */
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234 struct Numa *nadely_r; /*!< y locations of best rescoredtemplates */
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235 struct Numa *nawidth_r; /*!< widths of best rescoredtemplates */
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236 struct Numa *nascore_r; /*!< correlation scores: rescored templates */
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237 };
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238 typedef struct L_Rdid L_RDID;
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239
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240
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241 /*-------------------------------------------------------------------------*
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242 * Flags for describing limited character sets *
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243 *-------------------------------------------------------------------------*/
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244 /*! Character Set */
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245 enum {
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246 L_UNKNOWN = 0, /*!< character set type is not specified */
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247 L_ARABIC_NUMERALS = 1, /*!< 10 digits */
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248 L_LC_ROMAN_NUMERALS = 2, /*!< 7 lower-case letters (i,v,x,l,c,d,m) */
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249 L_UC_ROMAN_NUMERALS = 3, /*!< 7 upper-case letters (I,V,X,L,C,D,M) */
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250 L_LC_ALPHA = 4, /*!< 26 lower-case letters */
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251 L_UC_ALPHA = 5 /*!< 26 upper-case letters */
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252 };
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253
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254 /*-------------------------------------------------------------------------*
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255 * Flags for selecting between using average and all templates: *
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256 * recog->templ_use *
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257 *-------------------------------------------------------------------------*/
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258 /*! Template Select */
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259 enum {
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260 L_USE_ALL_TEMPLATES = 0, /*!< use all templates; default */
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261 L_USE_AVERAGE_TEMPLATES = 1 /*!< use average templates; special cases */
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262 };
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263
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264 #endif /* LEPTONICA_RECOG_H */