Tesseract  3.02
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commontraining.cpp File Reference
#include "commontraining.h"
#include "base/init_google.h"
#include "base/commandlineflags.h"
#include "allheaders.h"
#include "ccutil.h"
#include "classify.h"
#include "oldlist.h"
#include "globals.h"
#include "mf.h"
#include "clusttool.h"
#include "cluster.h"
#include "tessopt.h"
#include "efio.h"
#include "emalloc.h"
#include "featdefs.h"
#include "fontinfo.h"
#include "intfeaturespace.h"
#include "mastertrainer.h"
#include "tessdatamanager.h"
#include "tprintf.h"
#include "freelist.h"
#include "params.h"
#include "shapetable.h"
#include "unicity_table.h"
#include <math.h>

Go to the source code of this file.

Namespaces

namespace  tesseract

Functions

 INT_PARAM_FLAG (debug_level, 0,"Level of Trainer debugging")
 INT_PARAM_FLAG (load_images, 0,"Load images with tr files")
 STRING_PARAM_FLAG (configfile,"","File to load more configs from")
 STRING_PARAM_FLAG (D,"","Directory to write output files to")
 STRING_PARAM_FLAG (F,"font_properties","File listing font properties")
 STRING_PARAM_FLAG (X,"","File listing font xheights")
 STRING_PARAM_FLAG (U,"unicharset","File to load unicharset from")
 STRING_PARAM_FLAG (O,"","File to write unicharset to")
 STRING_PARAM_FLAG (input_trainer,"","File to load trainer from")
 STRING_PARAM_FLAG (output_trainer,"","File to write trainer to")
 STRING_PARAM_FLAG (test_ch,"","UTF8 test character string")
void ParseArguments (int *argc, char ***argv)
ShapeTabletesseract::LoadShapeTable (const STRING &file_prefix)
void tesseract::WriteShapeTable (const STRING &file_prefix, const ShapeTable &shape_table)
MasterTrainer * tesseract::LoadTrainingData (int argc, const char *const *argv, bool replication, ShapeTable **shape_table, STRING *file_prefix)
const char * GetNextFilename (int argc, const char *const *argv)
LABELEDLIST FindList (LIST List, char *Label)
LABELEDLIST NewLabeledList (const char *Label)
void ReadTrainingSamples (const FEATURE_DEFS_STRUCT &feature_defs, const char *feature_name, int max_samples, UNICHARSET *unicharset, FILE *file, LIST *training_samples)
void FreeTrainingSamples (LIST CharList)
void FreeLabeledList (LABELEDLIST LabeledList)
CLUSTERERSetUpForClustering (const FEATURE_DEFS_STRUCT &FeatureDefs, LABELEDLIST char_sample, const char *program_feature_type)
void MergeInsignificantProtos (LIST ProtoList, const char *label, CLUSTERER *Clusterer, CLUSTERCONFIG *Config)
void CleanUpUnusedData (LIST ProtoList)
LIST RemoveInsignificantProtos (LIST ProtoList, BOOL8 KeepSigProtos, BOOL8 KeepInsigProtos, int N)
MERGE_CLASS FindClass (LIST List, const char *Label)
MERGE_CLASS NewLabeledClass (const char *Label)
void FreeLabeledClassList (LIST ClassList)
CLASS_STRUCTSetUpForFloat2Int (const UNICHARSET &unicharset, LIST LabeledClassList)
void Normalize (float *Values)
void FreeNormProtoList (LIST CharList)
void AddToNormProtosList (LIST *NormProtoList, LIST ProtoList, char *CharName)
int NumberOfProtos (LIST ProtoList, BOOL8 CountSigProtos, BOOL8 CountInsigProtos)

Variables

CLUSTERCONFIG Config = { elliptical, 0.625, 0.05, 1.0, 1e-6, 0 }
const char * kUsage = "[flags] [ .tr files ... ]\n"
FEATURE_DEFS_STRUCT feature_defs
CCUtil ccutil

Function Documentation

void AddToNormProtosList ( LIST NormProtoList,
LIST  ProtoList,
char *  CharName 
)

Definition at line 935 of file commontraining.cpp.

{
PROTOTYPE* Proto;
LABELEDLIST LabeledProtoList;
LabeledProtoList = NewLabeledList(CharName);
iterate(ProtoList)
{
Proto = (PROTOTYPE *) first_node (ProtoList);
LabeledProtoList->List = push(LabeledProtoList->List, Proto);
}
*NormProtoList = push(*NormProtoList, LabeledProtoList);
}
void CleanUpUnusedData ( LIST  ProtoList)

Definition at line 683 of file commontraining.cpp.

{
PROTOTYPE* Prototype;
iterate(ProtoList)
{
Prototype = (PROTOTYPE *) first_node (ProtoList);
if(Prototype->Variance.Elliptical != NULL)
{
Prototype->Variance.Elliptical = NULL;
}
if(Prototype->Magnitude.Elliptical != NULL)
{
Prototype->Magnitude.Elliptical = NULL;
}
if(Prototype->Weight.Elliptical != NULL)
{
memfree(Prototype->Weight.Elliptical);
Prototype->Weight.Elliptical = NULL;
}
}
}
MERGE_CLASS FindClass ( LIST  List,
const char *  Label 
)

Definition at line 778 of file commontraining.cpp.

{
MERGE_CLASS MergeClass;
iterate (List)
{
MergeClass = (MERGE_CLASS) first_node (List);
if (strcmp (MergeClass->Label, Label) == 0)
return (MergeClass);
}
return (NULL);
} /* FindClass */
LABELEDLIST FindList ( LIST  List,
char *  Label 
)

Definition at line 385 of file commontraining.cpp.

{
LABELEDLIST LabeledList;
iterate (List)
{
LabeledList = (LABELEDLIST) first_node (List);
if (strcmp (LabeledList->Label, Label) == 0)
return (LabeledList);
}
return (NULL);
} /* FindList */
void FreeLabeledClassList ( LIST  ClassList)

Definition at line 809 of file commontraining.cpp.

{
MERGE_CLASS MergeClass;
iterate (ClassList) /* iterate thru all of the fonts */
{
MergeClass = (MERGE_CLASS) first_node (ClassList);
free (MergeClass->Label);
FreeClass(MergeClass->Class);
delete MergeClass;
}
destroy (ClassList);
} /* FreeLabeledClassList */
void FreeLabeledList ( LABELEDLIST  LabeledList)

Definition at line 548 of file commontraining.cpp.

{
/*
** Parameters:
** LabeledList labeled list to be freed
** Globals: none
** Operation:
** This routine deallocates all of the memory consumed by
** a labeled list. It does not free any memory which may be
** consumed by the items in the list.
** Return: none
** Exceptions: none
** History: Fri Aug 18 17:52:45 1989, DSJ, Created.
*/
destroy(LabeledList->List);
free(LabeledList->Label);
free(LabeledList);
} /* FreeLabeledList */
void FreeNormProtoList ( LIST  CharList)

Definition at line 919 of file commontraining.cpp.

{
LABELEDLIST char_sample;
iterate (CharList) /* iterate thru all of the fonts */
{
char_sample = (LABELEDLIST) first_node (CharList);
FreeLabeledList (char_sample);
}
destroy (CharList);
} // FreeNormProtoList
void FreeTrainingSamples ( LIST  CharList)

Definition at line 518 of file commontraining.cpp.

{
/*
** Parameters:
** FontList list of all fonts in document
** Globals: none
** Operation:
** This routine deallocates all of the space allocated to
** the specified list of training samples.
** Return: none
** Exceptions: none
** History: Fri Aug 18 17:44:27 1989, DSJ, Created.
*/
LABELEDLIST char_sample;
FEATURE_SET FeatureSet;
LIST FeatureList;
iterate(CharList) { /* iterate thru all of the fonts */
char_sample = (LABELEDLIST) first_node(CharList);
FeatureList = char_sample->List;
iterate(FeatureList) { /* iterate thru all of the classes */
FeatureSet = (FEATURE_SET) first_node(FeatureList);
FreeFeatureSet(FeatureSet);
}
FreeLabeledList(char_sample);
}
destroy(CharList);
} /* FreeTrainingSamples */
const char* GetNextFilename ( int  argc,
const char *const *  argv 
)

Definition at line 362 of file commontraining.cpp.

{
/*
** Parameters: none
** Globals:
** tessoptind defined by tessopt sys call
** Operation:
** This routine returns the next command line argument. If
** there are no remaining command line arguments, it returns
** NULL. This routine should only be called after all option
** arguments have been parsed and removed with ParseArguments.
** Return: Next command line argument or NULL.
** Exceptions: none
** History: Fri Aug 18 09:34:12 1989, DSJ, Created.
*/
if (tessoptind < argc)
return argv[tessoptind++];
else
return NULL;
} /* GetNextFilename */
INT_PARAM_FLAG ( debug_level  ,
,
"Level of Trainer debugging"   
)
INT_PARAM_FLAG ( load_images  ,
,
"Load images with tr files"   
)
void MergeInsignificantProtos ( LIST  ProtoList,
const char *  label,
CLUSTERER Clusterer,
CLUSTERCONFIG Config 
)

Definition at line 618 of file commontraining.cpp.

{
PROTOTYPE *Prototype;
bool debug = strcmp(FLAGS_test_ch.c_str(), label) == 0;
LIST pProtoList = ProtoList;
iterate(pProtoList) {
Prototype = (PROTOTYPE *) first_node (pProtoList);
if (Prototype->Significant || Prototype->Merged)
continue;
FLOAT32 best_dist = 0.125;
PROTOTYPE* best_match = NULL;
// Find the nearest alive prototype.
LIST list_it = ProtoList;
iterate(list_it) {
PROTOTYPE* test_p = (PROTOTYPE *) first_node (list_it);
if (test_p != Prototype && !test_p->Merged) {
FLOAT32 dist = ComputeDistance(Clusterer->SampleSize,
Clusterer->ParamDesc,
Prototype->Mean, test_p->Mean);
if (dist < best_dist) {
best_match = test_p;
best_dist = dist;
}
}
}
if (best_match != NULL && !best_match->Significant) {
if (debug)
tprintf("Merging red clusters (%d+%d) at %g,%g and %g,%g\n",
best_match->NumSamples, Prototype->NumSamples,
best_match->Mean[0], best_match->Mean[1],
Prototype->Mean[0], Prototype->Mean[1]);
best_match->NumSamples = MergeClusters(Clusterer->SampleSize,
Clusterer->ParamDesc,
best_match->NumSamples,
Prototype->NumSamples,
best_match->Mean,
best_match->Mean, Prototype->Mean);
Prototype->NumSamples = 0;
Prototype->Merged = 1;
} else if (best_match != NULL) {
if (debug)
tprintf("Red proto at %g,%g matched a green one at %g,%g\n",
Prototype->Mean[0], Prototype->Mean[1],
best_match->Mean[0], best_match->Mean[1]);
Prototype->Merged = 1;
}
}
// Mark significant those that now have enough samples.
int min_samples = (inT32) (Config->MinSamples * Clusterer->NumChar);
pProtoList = ProtoList;
iterate(pProtoList) {
Prototype = (PROTOTYPE *) first_node (pProtoList);
// Process insignificant protos that do not match a green one
if (!Prototype->Significant && Prototype->NumSamples >= min_samples &&
!Prototype->Merged) {
if (debug)
tprintf("Red proto at %g,%g becoming green\n",
Prototype->Mean[0], Prototype->Mean[1]);
Prototype->Significant = true;
}
}
} /* MergeInsignificantProtos */
MERGE_CLASS NewLabeledClass ( const char *  Label)

Definition at line 795 of file commontraining.cpp.

{
MERGE_CLASS MergeClass;
MergeClass = new MERGE_CLASS_NODE;
MergeClass->Label = (char*)Emalloc (strlen (Label)+1);
strcpy (MergeClass->Label, Label);
return (MergeClass);
} /* NewLabeledClass */
LABELEDLIST NewLabeledList ( const char *  Label)

Definition at line 417 of file commontraining.cpp.

{
LABELEDLIST LabeledList;
LabeledList = (LABELEDLIST) Emalloc (sizeof (LABELEDLISTNODE));
LabeledList->Label = (char*)Emalloc (strlen (Label)+1);
strcpy (LabeledList->Label, Label);
LabeledList->List = NIL_LIST;
LabeledList->SampleCount = 0;
LabeledList->font_sample_count = 0;
return (LabeledList);
} /* NewLabeledList */
void Normalize ( float *  Values)

Definition at line 902 of file commontraining.cpp.

{
register float Slope;
register float Intercept;
register float Normalizer;
Slope = tan (Values [2] * 2 * PI);
Intercept = Values [1] - Slope * Values [0];
Normalizer = 1 / sqrt (Slope * Slope + 1.0);
Values [0] = Slope * Normalizer;
Values [1] = - Normalizer;
Values [2] = Intercept * Normalizer;
} // Normalize
int NumberOfProtos ( LIST  ProtoList,
BOOL8  CountSigProtos,
BOOL8  CountInsigProtos 
)

Definition at line 953 of file commontraining.cpp.

{
int N = 0;
PROTOTYPE *Proto;
iterate(ProtoList)
{
Proto = (PROTOTYPE *) first_node ( ProtoList );
if (( Proto->Significant && CountSigProtos ) ||
( ! Proto->Significant && CountInsigProtos ) )
N++;
}
return(N);
}
void ParseArguments ( int *  argc,
char ***  argv 
)

Definition at line 88 of file commontraining.cpp.

{
/*
** Parameters:
** argc number of command line arguments to parse
** argv command line arguments
** Globals:
** ShowSignificantProtos flag controlling proto display
** ShowInsignificantProtos flag controlling proto display
** Config current clustering parameters
** tessoptarg, tessoptind defined by tessopt sys call
** Argc, Argv global copies of argc and argv
** Operation:
** This routine parses the command line arguments that were
** passed to the program. The legal arguments are shown in the usage
** message below:
** Return: none
** Exceptions: Illegal options terminate the program.
** History: 7/24/89, DSJ, Created.
*/
#ifndef USE_STD_NAMESPACE
InitGoogle(kUsage, argc, argv, true);
#else
int Option;
int ParametersRead;
BOOL8 Error;
Error = FALSE;
while ((Option = tessopt(*argc, *argv, "F:O:U:D:C:I:M:B:S:X:c:")) != EOF) {
switch (Option) {
case 'C':
ParametersRead = sscanf(tessoptarg, "%lf", &(Config.Confidence) );
if ( ParametersRead != 1 ) Error = TRUE;
else if ( Config.Confidence > 1 ) Config.Confidence = 1;
else if ( Config.Confidence < 0 ) Config.Confidence = 0;
break;
case 'I':
ParametersRead = sscanf(tessoptarg, "%f", &(Config.Independence) );
if ( ParametersRead != 1 ) Error = TRUE;
else if ( Config.Independence > 1 ) Config.Independence = 1;
else if ( Config.Independence < 0 ) Config.Independence = 0;
break;
case 'M':
ParametersRead = sscanf(tessoptarg, "%f", &(Config.MinSamples) );
if ( ParametersRead != 1 ) Error = TRUE;
else if ( Config.MinSamples > 1 ) Config.MinSamples = 1;
else if ( Config.MinSamples < 0 ) Config.MinSamples = 0;
break;
case 'B':
ParametersRead = sscanf(tessoptarg, "%f", &(Config.MaxIllegal) );
if ( ParametersRead != 1 ) Error = TRUE;
else if ( Config.MaxIllegal > 1 ) Config.MaxIllegal = 1;
else if ( Config.MaxIllegal < 0 ) Config.MaxIllegal = 0;
break;
case 'c':
FLAGS_configfile.set_value(tessoptarg);
break;
case 'D':
FLAGS_D.set_value(tessoptarg);
break;
case 'U':
FLAGS_U.set_value(tessoptarg);
break;
case 'O':
FLAGS_O.set_value(tessoptarg);
break;
case 'F':
FLAGS_F.set_value(tessoptarg);
break;
case 'X':
FLAGS_X.set_value(tessoptarg);
break;
case '?':
Error = TRUE;
break;
}
if (Error) {
fprintf(stderr, "Usage: %s %s\n", (*argv)[0], kUsage);
exit(2);
}
}
#endif
// Set additional parameters from config file if specified.
if (!FLAGS_configfile.empty()) {
FLAGS_configfile.c_str(),
}
} // ParseArguments
void ReadTrainingSamples ( const FEATURE_DEFS_STRUCT feature_defs,
const char *  feature_name,
int  max_samples,
UNICHARSET unicharset,
FILE *  file,
LIST training_samples 
)

Definition at line 448 of file commontraining.cpp.

{
/*
** Parameters:
** file open text file to read samples from
** Globals: none
** Operation:
** This routine reads training samples from a file and
** places them into a data structure which organizes the
** samples by FontName and CharName. It then returns this
** data structure.
** Return: none
** Exceptions: none
** History: Fri Aug 18 13:11:39 1989, DSJ, Created.
** Tue May 17 1998 simplifications to structure, illiminated
** font, and feature specification levels of structure.
*/
char buffer[2048];
char unichar[UNICHAR_LEN + 1];
LABELEDLIST char_sample;
FEATURE_SET feature_samples;
CHAR_DESC char_desc;
int i;
int feature_type = ShortNameToFeatureType(feature_defs, feature_name);
// Zero out the font_sample_count for all the classes.
LIST it = *training_samples;
iterate(it) {
char_sample = reinterpret_cast<LABELEDLIST>(first_node(it));
char_sample->font_sample_count = 0;
}
while (fgets(buffer, 2048, file) != NULL) {
if (buffer[0] == '\n')
continue;
sscanf(buffer, "%*s %s", unichar);
if (unicharset != NULL && !unicharset->contains_unichar(unichar)) {
unicharset->unichar_insert(unichar);
if (unicharset->size() > MAX_NUM_CLASSES) {
tprintf("Error: Size of unicharset in training is "
"greater than MAX_NUM_CLASSES\n");
exit(1);
}
}
char_sample = FindList(*training_samples, unichar);
if (char_sample == NULL) {
char_sample = NewLabeledList(unichar);
*training_samples = push(*training_samples, char_sample);
}
char_desc = ReadCharDescription(feature_defs, file);
feature_samples = char_desc->FeatureSets[feature_type];
if (char_sample->font_sample_count < max_samples || max_samples <= 0) {
char_sample->List = push(char_sample->List, feature_samples);
char_sample->SampleCount++;
char_sample->font_sample_count++;
} else {
FreeFeatureSet(feature_samples);
}
for (i = 0; i < char_desc->NumFeatureSets; i++) {
if (feature_type != i)
FreeFeatureSet(char_desc->FeatureSets[i]);
}
free(char_desc);
}
} // ReadTrainingSamples
LIST RemoveInsignificantProtos ( LIST  ProtoList,
BOOL8  KeepSigProtos,
BOOL8  KeepInsigProtos,
int  N 
)

Definition at line 710 of file commontraining.cpp.

{
LIST NewProtoList = NIL_LIST;
LIST pProtoList;
PROTOTYPE* Proto;
PROTOTYPE* NewProto;
int i;
pProtoList = ProtoList;
iterate(pProtoList)
{
Proto = (PROTOTYPE *) first_node (pProtoList);
if ((Proto->Significant && KeepSigProtos) ||
(!Proto->Significant && KeepInsigProtos))
{
NewProto = (PROTOTYPE *)Emalloc(sizeof(PROTOTYPE));
NewProto->Mean = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
NewProto->Significant = Proto->Significant;
NewProto->Style = Proto->Style;
NewProto->NumSamples = Proto->NumSamples;
NewProto->Cluster = NULL;
NewProto->Distrib = NULL;
for (i=0; i < N; i++)
NewProto->Mean[i] = Proto->Mean[i];
if (Proto->Variance.Elliptical != NULL)
{
NewProto->Variance.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
for (i=0; i < N; i++)
NewProto->Variance.Elliptical[i] = Proto->Variance.Elliptical[i];
}
else
NewProto->Variance.Elliptical = NULL;
//---------------------------------------------
if (Proto->Magnitude.Elliptical != NULL)
{
NewProto->Magnitude.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
for (i=0; i < N; i++)
NewProto->Magnitude.Elliptical[i] = Proto->Magnitude.Elliptical[i];
}
else
NewProto->Magnitude.Elliptical = NULL;
//------------------------------------------------
if (Proto->Weight.Elliptical != NULL)
{
NewProto->Weight.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
for (i=0; i < N; i++)
NewProto->Weight.Elliptical[i] = Proto->Weight.Elliptical[i];
}
else
NewProto->Weight.Elliptical = NULL;
NewProto->TotalMagnitude = Proto->TotalMagnitude;
NewProto->LogMagnitude = Proto->LogMagnitude;
NewProtoList = push_last(NewProtoList, NewProto);
}
}
FreeProtoList(&ProtoList);
return (NewProtoList);
} /* RemoveInsignificantProtos */
CLUSTERER* SetUpForClustering ( const FEATURE_DEFS_STRUCT FeatureDefs,
LABELEDLIST  char_sample,
const char *  program_feature_type 
)

Definition at line 567 of file commontraining.cpp.

{
/*
** Parameters:
** char_sample: LABELEDLIST that holds all the feature information for a
** given character.
** Globals:
** None
** Operation:
** This routine reads samples from a LABELEDLIST and enters
** those samples into a clusterer data structure. This
** data structure is then returned to the caller.
** Return:
** Pointer to new clusterer data structure.
** Exceptions:
** None
** History:
** 8/16/89, DSJ, Created.
*/
uinT16 N;
int i, j;
FLOAT32 *Sample = NULL;
CLUSTERER *Clusterer;
inT32 CharID;
LIST FeatureList = NULL;
FEATURE_SET FeatureSet = NULL;
int desc_index = ShortNameToFeatureType(FeatureDefs, program_feature_type);
N = FeatureDefs.FeatureDesc[desc_index]->NumParams;
Clusterer = MakeClusterer(N, FeatureDefs.FeatureDesc[desc_index]->ParamDesc);
FeatureList = char_sample->List;
CharID = 0;
iterate(FeatureList) {
FeatureSet = (FEATURE_SET) first_node(FeatureList);
for (i = 0; i < FeatureSet->MaxNumFeatures; i++) {
if (Sample == NULL)
Sample = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
for (j = 0; j < N; j++)
Sample[j] = FeatureSet->Features[i]->Params[j];
MakeSample (Clusterer, Sample, CharID);
}
CharID++;
}
if ( Sample != NULL ) free( Sample );
return( Clusterer );
} /* SetUpForClustering */
CLASS_STRUCT* SetUpForFloat2Int ( const UNICHARSET unicharset,
LIST  LabeledClassList 
)

SetUpForFloat2Int

Definition at line 839 of file commontraining.cpp.

{
MERGE_CLASS MergeClass;
CLASS_TYPE Class;
int NumProtos;
int NumConfigs;
int NumWords;
int i, j;
float Values[3];
PROTO NewProto;
PROTO OldProto;
BIT_VECTOR NewConfig;
BIT_VECTOR OldConfig;
// printf("Float2Int ...\n");
CLASS_STRUCT* float_classes = new CLASS_STRUCT[unicharset.size()];
iterate(LabeledClassList)
{
MergeClass = (MERGE_CLASS) first_node (LabeledClassList);
Class = &float_classes[unicharset.unichar_to_id(MergeClass->Label)];
NumProtos = MergeClass->Class->NumProtos;
NumConfigs = MergeClass->Class->NumConfigs;
font_set.move(&MergeClass->Class->font_set);
Class->NumProtos = NumProtos;
Class->MaxNumProtos = NumProtos;
Class->Prototypes = (PROTO) Emalloc (sizeof(PROTO_STRUCT) * NumProtos);
for(i=0; i < NumProtos; i++)
{
NewProto = ProtoIn(Class, i);
OldProto = ProtoIn(MergeClass->Class, i);
Values[0] = OldProto->X;
Values[1] = OldProto->Y;
Values[2] = OldProto->Angle;
Normalize(Values);
NewProto->X = OldProto->X;
NewProto->Y = OldProto->Y;
NewProto->Length = OldProto->Length;
NewProto->Angle = OldProto->Angle;
NewProto->A = Values[0];
NewProto->B = Values[1];
NewProto->C = Values[2];
}
Class->NumConfigs = NumConfigs;
Class->MaxNumConfigs = NumConfigs;
Class->font_set.move(&font_set);
Class->Configurations = (BIT_VECTOR*) Emalloc (sizeof(BIT_VECTOR) * NumConfigs);
NumWords = WordsInVectorOfSize(NumProtos);
for(i=0; i < NumConfigs; i++)
{
NewConfig = NewBitVector(NumProtos);
OldConfig = MergeClass->Class->Configurations[i];
for(j=0; j < NumWords; j++)
NewConfig[j] = OldConfig[j];
Class->Configurations[i] = NewConfig;
}
}
return float_classes;
} // SetUpForFloat2Int
STRING_PARAM_FLAG ( configfile  ,
""  ,
"File to load more configs from"   
)
STRING_PARAM_FLAG ( ,
""  ,
"Directory to write output files to"   
)
STRING_PARAM_FLAG ( ,
"font_properties"  ,
"File listing font properties"   
)
STRING_PARAM_FLAG ( ,
""  ,
"File listing font xheights"   
)
STRING_PARAM_FLAG ( ,
"unicharset"  ,
"File to load unicharset from"   
)
STRING_PARAM_FLAG ( ,
""  ,
"File to write unicharset to"   
)
STRING_PARAM_FLAG ( input_trainer  ,
""  ,
"File to load trainer from"   
)
STRING_PARAM_FLAG ( output_trainer  ,
""  ,
"File to write trainer to"   
)
STRING_PARAM_FLAG ( test_ch  ,
""  ,
"UTF8 test character string"   
)

Variable Documentation

CCUtil ccutil

Definition at line 85 of file commontraining.cpp.

CLUSTERCONFIG Config = { elliptical, 0.625, 0.05, 1.0, 1e-6, 0 }

Definition at line 53 of file commontraining.cpp.

FEATURE_DEFS_STRUCT feature_defs

Definition at line 84 of file commontraining.cpp.

const char* kUsage = "[flags] [ .tr files ... ]\n"

Definition at line 71 of file commontraining.cpp.