--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mupdf-source/thirdparty/tesseract/src/training/cntraining.cpp	Mon Sep 15 11:43:07 2025 +0200
@@ -0,0 +1,245 @@
+/******************************************************************************
+ **  Filename:  cntraining.cpp
+ **  Purpose:  Generates a normproto and pffmtable.
+ **  Author:    Dan Johnson
+ **  Revisment:  Christy Russon
+ **
+ **  (c) Copyright Hewlett-Packard Company, 1988.
+ ** Licensed under the Apache License, Version 2.0 (the "License");
+ ** you may not use this file except in compliance with the License.
+ ** You may obtain a copy of the License at
+ ** http://www.apache.org/licenses/LICENSE-2.0
+ ** Unless required by applicable law or agreed to in writing, software
+ ** distributed under the License is distributed on an "AS IS" BASIS,
+ ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ ** See the License for the specific language governing permissions and
+ ** limitations under the License.
+ ******************************************************************************/
+
+/*----------------------------------------------------------------------------
+          Include Files and Type Defines
+----------------------------------------------------------------------------*/
+#include <tesseract/unichar.h>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include "cluster.h"
+#include "clusttool.h"
+#include "commontraining.h"
+#include "featdefs.h"
+#include "ocrfeatures.h"
+#include "oldlist.h"
+
+#define PROGRAM_FEATURE_TYPE "cn"
+
+using namespace tesseract;
+
+/*----------------------------------------------------------------------------
+          Private Function Prototypes
+----------------------------------------------------------------------------*/
+
+static void WriteNormProtos(const char *Directory, LIST LabeledProtoList,
+                            const FEATURE_DESC_STRUCT *feature_desc);
+
+static void WriteProtos(FILE *File, uint16_t N, LIST ProtoList, bool WriteSigProtos,
+                        bool WriteInsigProtos);
+
+/*----------------------------------------------------------------------------
+          Global Data Definitions and Declarations
+----------------------------------------------------------------------------*/
+/* global variable to hold configuration parameters to control clustering */
+//-M 0.025   -B 0.05   -I 0.8   -C 1e-3
+static const CLUSTERCONFIG CNConfig = {elliptical, 0.025, 0.05, 0.8, 1e-3, 0};
+
+/*----------------------------------------------------------------------------
+              Public Code
+----------------------------------------------------------------------------*/
+
+/**
+* This program reads in a text file consisting of feature
+* samples from a training page in the following format:
+* @verbatim
+   FontName CharName NumberOfFeatureTypes(N)
+      FeatureTypeName1 NumberOfFeatures(M)
+         Feature1
+         ...
+         FeatureM
+      FeatureTypeName2 NumberOfFeatures(M)
+         Feature1
+         ...
+         FeatureM
+      ...
+      FeatureTypeNameN NumberOfFeatures(M)
+         Feature1
+         ...
+         FeatureM
+   FontName CharName ...
+@endverbatim
+* It then appends these samples into a separate file for each
+* character.  The name of the file is
+*
+*   DirectoryName/FontName/CharName.FeatureTypeName
+*
+* The DirectoryName can be specified via a command
+* line argument.  If not specified, it defaults to the
+* current directory.  The format of the resulting files is:
+* @verbatim
+   NumberOfFeatures(M)
+      Feature1
+      ...
+      FeatureM
+   NumberOfFeatures(M)
+   ...
+@endverbatim
+* The output files each have a header which describes the
+* type of feature which the file contains.  This header is
+* in the format required by the clusterer.  A command line
+* argument can also be used to specify that only the first
+* N samples of each class should be used.
+* @param argc  number of command line arguments
+* @param argv  array of command line arguments
+* @return 0 on success
+*/
+int main(int argc, char *argv[]) {
+  tesseract::CheckSharedLibraryVersion();
+
+  // Set the global Config parameters before parsing the command line.
+  Config = CNConfig;
+
+  LIST CharList = NIL_LIST;
+  CLUSTERER *Clusterer = nullptr;
+  LIST ProtoList = NIL_LIST;
+  LIST NormProtoList = NIL_LIST;
+  LIST pCharList;
+  LABELEDLIST CharSample;
+  FEATURE_DEFS_STRUCT FeatureDefs;
+  InitFeatureDefs(&FeatureDefs);
+
+  ParseArguments(&argc, &argv);
+#if !defined(NDEBUG)
+  int num_fonts = 0;
+#endif
+  for (const char *PageName = *++argv; PageName != nullptr; PageName = *++argv) {
+    printf("Reading %s ...\n", PageName);
+    FILE *TrainingPage = fopen(PageName, "rb");
+    ASSERT_HOST(TrainingPage);
+    if (TrainingPage) {
+      ReadTrainingSamples(FeatureDefs, PROGRAM_FEATURE_TYPE, 100, nullptr, TrainingPage, &CharList);
+      fclose(TrainingPage);
+#if !defined(NDEBUG)
+      ++num_fonts;
+#endif
+    }
+  }
+  printf("Clustering ...\n");
+  // To allow an individual font to form a separate cluster,
+  // reduce the min samples:
+  // Config.MinSamples = 0.5 / num_fonts;
+  pCharList = CharList;
+  // The norm protos will count the source protos, so we keep them here in
+  // freeable_protos, so they can be freed later.
+  std::vector<LIST> freeable_protos;
+  iterate(pCharList) {
+    // Cluster
+    CharSample = reinterpret_cast<LABELEDLIST>(pCharList->first_node());
+    Clusterer = SetUpForClustering(FeatureDefs, CharSample, PROGRAM_FEATURE_TYPE);
+    if (Clusterer == nullptr) { // To avoid a SIGSEGV
+      fprintf(stderr, "Error: nullptr clusterer!\n");
+      return EXIT_FAILURE;
+    }
+    float SavedMinSamples = Config.MinSamples;
+    // To disable the tendency to produce a single cluster for all fonts,
+    // make MagicSamples an impossible to achieve number:
+    // Config.MagicSamples = CharSample->SampleCount * 10;
+    Config.MagicSamples = CharSample->SampleCount;
+    while (Config.MinSamples > 0.001) {
+      ProtoList = ClusterSamples(Clusterer, &Config);
+      if (NumberOfProtos(ProtoList, true, false) > 0) {
+        break;
+      } else {
+        Config.MinSamples *= 0.95;
+        printf(
+            "0 significant protos for %s."
+            " Retrying clustering with MinSamples = %f%%\n",
+            CharSample->Label.c_str(), Config.MinSamples);
+      }
+    }
+    Config.MinSamples = SavedMinSamples;
+    AddToNormProtosList(&NormProtoList, ProtoList, CharSample->Label);
+    freeable_protos.push_back(ProtoList);
+    FreeClusterer(Clusterer);
+  }
+  FreeTrainingSamples(CharList);
+  int desc_index = ShortNameToFeatureType(FeatureDefs, PROGRAM_FEATURE_TYPE);
+  WriteNormProtos(FLAGS_D.c_str(), NormProtoList, FeatureDefs.FeatureDesc[desc_index]);
+  FreeNormProtoList(NormProtoList);
+  for (auto &freeable_proto : freeable_protos) {
+    FreeProtoList(&freeable_proto);
+  }
+  printf("\n");
+  return EXIT_SUCCESS;
+} // main
+
+/*----------------------------------------------------------------------------
+              Private Code
+----------------------------------------------------------------------------*/
+
+/*----------------------------------------------------------------------------*/
+/**
+ * This routine writes the specified samples into files which
+ * are organized according to the font name and character name
+ * of the samples.
+ * @param Directory  directory to place sample files into
+ * @param LabeledProtoList List of labeled protos
+ * @param feature_desc Description of the features
+ */
+static void WriteNormProtos(const char *Directory, LIST LabeledProtoList,
+                            const FEATURE_DESC_STRUCT *feature_desc) {
+  FILE *File;
+  LABELEDLIST LabeledProto;
+  int N;
+
+  std::string Filename = "";
+  if (Directory != nullptr && Directory[0] != '\0') {
+    Filename += Directory;
+    Filename += "/";
+  }
+  Filename += "normproto";
+  printf("\nWriting %s ...", Filename.c_str());
+  File = fopen(Filename.c_str(), "wb");
+  ASSERT_HOST(File);
+  fprintf(File, "%0d\n", feature_desc->NumParams);
+  WriteParamDesc(File, feature_desc->NumParams, feature_desc->ParamDesc);
+  iterate(LabeledProtoList) {
+    LabeledProto = reinterpret_cast<LABELEDLIST>(LabeledProtoList->first_node());
+    N = NumberOfProtos(LabeledProto->List, true, false);
+    if (N < 1) {
+      printf(
+          "\nError! Not enough protos for %s: %d protos"
+          " (%d significant protos"
+          ", %d insignificant protos)\n",
+          LabeledProto->Label.c_str(), N, NumberOfProtos(LabeledProto->List, true, false),
+          NumberOfProtos(LabeledProto->List, false, true));
+      exit(1);
+    }
+    fprintf(File, "\n%s %d\n", LabeledProto->Label.c_str(), N);
+    WriteProtos(File, feature_desc->NumParams, LabeledProto->List, true, false);
+  }
+  fclose(File);
+
+} // WriteNormProtos
+
+/*-------------------------------------------------------------------------*/
+
+static void WriteProtos(FILE *File, uint16_t N, LIST ProtoList, bool WriteSigProtos,
+                        bool WriteInsigProtos) {
+  PROTOTYPE *Proto;
+
+  // write prototypes
+  iterate(ProtoList) {
+    Proto = reinterpret_cast<PROTOTYPE *>(ProtoList->first_node());
+    if ((Proto->Significant && WriteSigProtos) || (!Proto->Significant && WriteInsigProtos)) {
+      WritePrototype(File, N, Proto);
+    }
+  }
+} // WriteProtos