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83588bc7a1
tesseract/classify/classify.h
Stefan Weil 83588bc7a1 Classify: Avoid unneeded new / delete operations 
Both class variables BaselineCutoffs and CharNormCutoffs were pointers
to fixed size arrays which were allocated in the constructor and
deallocated in the destructor. These two extra allocations and two
extra deallocations can be avoided.

Signed-off-by: Stefan Weil <sw@weilnetz.de>
2017-04-30 19:45:50 +02:00

542 lines
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///////////////////////////////////////////////////////////////////////
// File: classify.h
// Description: classify class.
// Author: Samuel Charron
//
// (C) Copyright 2006, Google Inc.
// 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.
//
///////////////////////////////////////////////////////////////////////
#ifndef TESSERACT_CLASSIFY_CLASSIFY_H_
#define TESSERACT_CLASSIFY_CLASSIFY_H_
#include "adaptive.h"
#include "ccstruct.h"
#include "classify.h"
#include "dict.h"
#include "featdefs.h"
#include "fontinfo.h"
#include "imagedata.h"
#include "intfx.h"
#include "intmatcher.h"
#include "normalis.h"
#include "ratngs.h"
#include "ocrfeatures.h"
#include "unicity_table.h"
class ScrollView;
class WERD_CHOICE;
class WERD_RES;
struct ADAPT_RESULTS;
struct NORM_PROTOS;
static const int kUnknownFontinfoId = -1;
static const int kBlankFontinfoId = -2;
namespace tesseract {
class ShapeClassifier;
struct ShapeRating;
class ShapeTable;
struct UnicharRating;
// How segmented is a blob. In this enum, character refers to a classifiable
// unit, but that is too long and character is usually easier to understand.
enum CharSegmentationType {
CST_FRAGMENT, // A partial character.
CST_WHOLE, // A correctly segmented character.
CST_IMPROPER, // More than one but less than 2 characters.
CST_NGRAM // Multiple characters.
};
class Classify : public CCStruct {
public:
Classify();
virtual ~Classify();
Dict& getDict() {
return dict_;
}
const ShapeTable* shape_table() const {
return shape_table_;
}
// Takes ownership of the given classifier, and uses it for future calls
// to CharNormClassifier.
void SetStaticClassifier(ShapeClassifier* static_classifier);
// Adds a noise classification result that is a bit worse than the worst
// current result, or the worst possible result if no current results.
void AddLargeSpeckleTo(int blob_length, BLOB_CHOICE_LIST *choices);
// Returns true if the blob is small enough to be a large speckle.
bool LargeSpeckle(const TBLOB &blob);
/* adaptive.cpp ************************************************************/
ADAPT_TEMPLATES NewAdaptedTemplates(bool InitFromUnicharset);
int GetFontinfoId(ADAPT_CLASS Class, uinT8 ConfigId);
// Runs the class pruner from int_templates on the given features, returning
// the number of classes output in results.
// int_templates Class pruner tables
// num_features Number of features in blob
// features Array of features
// normalization_factors (input) Array of int_templates->NumClasses fudge
// factors from blob normalization process.
// (Indexed by CLASS_INDEX)
// expected_num_features (input) Array of int_templates->NumClasses
// expected number of features for each class.
// (Indexed by CLASS_INDEX)
// results (output) Sorted Array of pruned classes.
// Array must be sized to take the maximum possible
// number of outputs : int_templates->NumClasses.
int PruneClasses(const INT_TEMPLATES_STRUCT* int_templates, int num_features,
int keep_this, const INT_FEATURE_STRUCT* features,
const uinT8* normalization_factors,
const uinT16* expected_num_features,
GenericVector<CP_RESULT_STRUCT>* results);
void ReadNewCutoffs(TFile* fp, bool swap, CLASS_CUTOFF_ARRAY Cutoffs);
void PrintAdaptedTemplates(FILE *File, ADAPT_TEMPLATES Templates);
void WriteAdaptedTemplates(FILE *File, ADAPT_TEMPLATES Templates);
ADAPT_TEMPLATES ReadAdaptedTemplates(TFile* File);
/* normmatch.cpp ************************************************************/
FLOAT32 ComputeNormMatch(CLASS_ID ClassId,
const FEATURE_STRUCT& feature, BOOL8 DebugMatch);
void FreeNormProtos();
NORM_PROTOS* ReadNormProtos(TFile* fp);
/* protos.cpp ***************************************************************/
void ConvertProto(PROTO Proto, int ProtoId, INT_CLASS Class);
INT_TEMPLATES CreateIntTemplates(CLASSES FloatProtos,
const UNICHARSET& target_unicharset);
/* adaptmatch.cpp ***********************************************************/
// Learns the given word using its chopped_word, seam_array, denorm,
// box_word, best_state, and correct_text to learn both correctly and
// incorrectly segmented blobs. If fontname is not NULL, then LearnBlob
// is called and the data will be saved in an internal buffer.
// Otherwise AdaptToBlob is called for adaption within a document.
void LearnWord(const char* fontname, WERD_RES* word);
// Builds a blob of length fragments, from the word, starting at start,
// and then learns it, as having the given correct_text.
// If fontname is not NULL, then LearnBlob is called and the data will be
// saved in an internal buffer for static training.
// Otherwise AdaptToBlob is called for adaption within a document.
// threshold is a magic number required by AdaptToChar and generated by
// ComputeAdaptionThresholds.
// Although it can be partly inferred from the string, segmentation is
// provided to explicitly clarify the character segmentation.
void LearnPieces(const char* fontname, int start, int length, float threshold,
CharSegmentationType segmentation, const char* correct_text,
WERD_RES* word);
void InitAdaptiveClassifier(TessdataManager* mgr);
void InitAdaptedClass(TBLOB *Blob,
CLASS_ID ClassId,
int FontinfoId,
ADAPT_CLASS Class,
ADAPT_TEMPLATES Templates);
void AmbigClassifier(const GenericVector<INT_FEATURE_STRUCT>& int_features,
const INT_FX_RESULT_STRUCT& fx_info,
const TBLOB *blob,
INT_TEMPLATES templates,
ADAPT_CLASS *classes,
UNICHAR_ID *ambiguities,
ADAPT_RESULTS *results);
void MasterMatcher(INT_TEMPLATES templates,
inT16 num_features,
const INT_FEATURE_STRUCT* features,
const uinT8* norm_factors,
ADAPT_CLASS* classes,
int debug,
int matcher_multiplier,
const TBOX& blob_box,
const GenericVector<CP_RESULT_STRUCT>& results,
ADAPT_RESULTS* final_results);
// Converts configs to fonts, and if the result is not adapted, and a
// shape_table_ is present, the shape is expanded to include all
// unichar_ids represented, before applying a set of corrections to the
// distance rating in int_result, (see ComputeCorrectedRating.)
// The results are added to the final_results output.
void ExpandShapesAndApplyCorrections(ADAPT_CLASS* classes,
bool debug,
int class_id,
int bottom, int top,
float cp_rating,
int blob_length,
int matcher_multiplier,
const uinT8* cn_factors,
UnicharRating* int_result,
ADAPT_RESULTS* final_results);
// Applies a set of corrections to the distance im_rating,
// including the cn_correction, miss penalty and additional penalty
// for non-alnums being vertical misfits. Returns the corrected distance.
double ComputeCorrectedRating(bool debug, int unichar_id, double cp_rating,
double im_rating, int feature_misses,
int bottom, int top,
int blob_length, int matcher_multiplier,
const uinT8* cn_factors);
void ConvertMatchesToChoices(const DENORM& denorm, const TBOX& box,
ADAPT_RESULTS *Results,
BLOB_CHOICE_LIST *Choices);
void AddNewResult(const UnicharRating& new_result, ADAPT_RESULTS *results);
int GetAdaptiveFeatures(TBLOB *Blob,
INT_FEATURE_ARRAY IntFeatures,
FEATURE_SET *FloatFeatures);
#ifndef GRAPHICS_DISABLED
void DebugAdaptiveClassifier(TBLOB *Blob,
ADAPT_RESULTS *Results);
#endif
PROTO_ID MakeNewTempProtos(FEATURE_SET Features,
int NumBadFeat,
FEATURE_ID BadFeat[],
INT_CLASS IClass,
ADAPT_CLASS Class,
BIT_VECTOR TempProtoMask);
int MakeNewTemporaryConfig(ADAPT_TEMPLATES Templates,
CLASS_ID ClassId,
int FontinfoId,
int NumFeatures,
INT_FEATURE_ARRAY Features,
FEATURE_SET FloatFeatures);
void MakePermanent(ADAPT_TEMPLATES Templates,
CLASS_ID ClassId,
int ConfigId,
TBLOB *Blob);
void PrintAdaptiveMatchResults(const ADAPT_RESULTS& results);
void RemoveExtraPuncs(ADAPT_RESULTS *Results);
void RemoveBadMatches(ADAPT_RESULTS *Results);
void SetAdaptiveThreshold(FLOAT32 Threshold);
void ShowBestMatchFor(int shape_id,
const INT_FEATURE_STRUCT* features,
int num_features);
// Returns a string for the classifier class_id: either the corresponding
// unicharset debug_str or the shape_table_ debug str.
STRING ClassIDToDebugStr(const INT_TEMPLATES_STRUCT* templates,
int class_id, int config_id) const;
// Converts a classifier class_id index with a config ID to:
// shape_table_ present: a shape_table_ index OR
// No shape_table_: a font ID.
// Without shape training, each class_id, config pair represents a single
// unichar id/font combination, so this function looks up the corresponding
// font id.
// With shape training, each class_id, config pair represents a single
// shape table index, so the fontset_table stores the shape table index,
// and the shape_table_ must be consulted to obtain the actual unichar_id/
// font combinations that the shape represents.
int ClassAndConfigIDToFontOrShapeID(int class_id,
int int_result_config) const;
// Converts a shape_table_ index to a classifier class_id index (not a
// unichar-id!). Uses a search, so not fast.
int ShapeIDToClassID(int shape_id) const;
UNICHAR_ID *BaselineClassifier(
TBLOB *Blob, const GenericVector<INT_FEATURE_STRUCT>& int_features,
const INT_FX_RESULT_STRUCT& fx_info,
ADAPT_TEMPLATES Templates, ADAPT_RESULTS *Results);
int CharNormClassifier(TBLOB *blob,
const TrainingSample& sample,
ADAPT_RESULTS *adapt_results);
// As CharNormClassifier, but operates on a TrainingSample and outputs to
// a GenericVector of ShapeRating without conversion to classes.
int CharNormTrainingSample(bool pruner_only, int keep_this,
const TrainingSample& sample,
GenericVector<UnicharRating>* results);
UNICHAR_ID *GetAmbiguities(TBLOB *Blob, CLASS_ID CorrectClass);
void DoAdaptiveMatch(TBLOB *Blob, ADAPT_RESULTS *Results);
void AdaptToChar(TBLOB* Blob, CLASS_ID ClassId, int FontinfoId,
FLOAT32 Threshold, ADAPT_TEMPLATES adaptive_templates);
void DisplayAdaptedChar(TBLOB* blob, INT_CLASS_STRUCT* int_class);
bool AdaptableWord(WERD_RES* word);
void EndAdaptiveClassifier();
void SettupPass1();
void SettupPass2();
void AdaptiveClassifier(TBLOB *Blob, BLOB_CHOICE_LIST *Choices);
void ClassifyAsNoise(ADAPT_RESULTS *Results);
void ResetAdaptiveClassifierInternal();
void SwitchAdaptiveClassifier();
void StartBackupAdaptiveClassifier();
int GetCharNormFeature(const INT_FX_RESULT_STRUCT& fx_info,
INT_TEMPLATES templates,
uinT8* pruner_norm_array,
uinT8* char_norm_array);
// Computes the char_norm_array for the unicharset and, if not NULL, the
// pruner_array as appropriate according to the existence of the shape_table.
// The norm_feature is deleted as it is almost certainly no longer needed.
void ComputeCharNormArrays(FEATURE_STRUCT* norm_feature,
INT_TEMPLATES_STRUCT* templates,
uinT8* char_norm_array,
uinT8* pruner_array);
bool TempConfigReliable(CLASS_ID class_id, const TEMP_CONFIG &config);
void UpdateAmbigsGroup(CLASS_ID class_id, TBLOB *Blob);
bool AdaptiveClassifierIsFull() const { return NumAdaptationsFailed > 0; }
bool AdaptiveClassifierIsEmpty() const {
return AdaptedTemplates->NumPermClasses == 0;
}
bool LooksLikeGarbage(TBLOB *blob);
void RefreshDebugWindow(ScrollView **win, const char *msg,
int y_offset, const TBOX &wbox);
// intfx.cpp
// Computes the DENORMS for bl(baseline) and cn(character) normalization
// during feature extraction. The input denorm describes the current state
// of the blob, which is usually a baseline-normalized word.
// The Transforms setup are as follows:
// Baseline Normalized (bl) Output:
// We center the grapheme by aligning the x-coordinate of its centroid with
// x=128 and leaving the already-baseline-normalized y as-is.
//
// Character Normalized (cn) Output:
// We align the grapheme's centroid at the origin and scale it
// asymmetrically in x and y so that the 2nd moments are a standard value
// (51.2) ie the result is vaguely square.
// If classify_nonlinear_norm is true:
// A non-linear normalization is setup that attempts to evenly distribute
// edges across x and y.
//
// Some of the fields of fx_info are also setup:
// Length: Total length of outline.
// Rx: Rounded y second moment. (Reversed by convention.)
// Ry: rounded x second moment.
// Xmean: Rounded x center of mass of the blob.
// Ymean: Rounded y center of mass of the blob.
static void SetupBLCNDenorms(const TBLOB& blob, bool nonlinear_norm,
DENORM* bl_denorm, DENORM* cn_denorm,
INT_FX_RESULT_STRUCT* fx_info);
// Extracts sets of 3-D features of length kStandardFeatureLength (=12.8), as
// (x,y) position and angle as measured counterclockwise from the vector
// <-1, 0>, from blob using two normalizations defined by bl_denorm and
// cn_denorm. See SetpuBLCNDenorms for definitions.
// If outline_cn_counts is not NULL, on return it contains the cumulative
// number of cn features generated for each outline in the blob (in order).
// Thus after the first outline, there were (*outline_cn_counts)[0] features,
// after the second outline, there were (*outline_cn_counts)[1] features etc.
static void ExtractFeatures(const TBLOB& blob,
bool nonlinear_norm,
GenericVector<INT_FEATURE_STRUCT>* bl_features,
GenericVector<INT_FEATURE_STRUCT>* cn_features,
INT_FX_RESULT_STRUCT* results,
GenericVector<int>* outline_cn_counts);
/* float2int.cpp ************************************************************/
void ClearCharNormArray(uinT8* char_norm_array);
void ComputeIntCharNormArray(const FEATURE_STRUCT& norm_feature,
uinT8* char_norm_array);
void ComputeIntFeatures(FEATURE_SET Features, INT_FEATURE_ARRAY IntFeatures);
/* intproto.cpp *************************************************************/
INT_TEMPLATES ReadIntTemplates(bool swap, TFile* fp);
void WriteIntTemplates(FILE *File, INT_TEMPLATES Templates,
const UNICHARSET& target_unicharset);
CLASS_ID GetClassToDebug(const char *Prompt, bool* adaptive_on,
bool* pretrained_on, int* shape_id);
void ShowMatchDisplay();
/* font detection ***********************************************************/
UnicityTable<FontInfo>& get_fontinfo_table() {
return fontinfo_table_;
}
const UnicityTable<FontInfo>& get_fontinfo_table() const {
return fontinfo_table_;
}
UnicityTable<FontSet>& get_fontset_table() {
return fontset_table_;
}
/* mfoutline.cpp ***********************************************************/
void NormalizeOutlines(LIST Outlines, FLOAT32 *XScale, FLOAT32 *YScale);
/* outfeat.cpp ***********************************************************/
FEATURE_SET ExtractOutlineFeatures(TBLOB *Blob);
/* picofeat.cpp ***********************************************************/
FEATURE_SET ExtractPicoFeatures(TBLOB *Blob);
FEATURE_SET ExtractIntCNFeatures(const TBLOB& blob,
const INT_FX_RESULT_STRUCT& fx_info);
FEATURE_SET ExtractIntGeoFeatures(const TBLOB& blob,
const INT_FX_RESULT_STRUCT& fx_info);
/* blobclass.cpp ***********************************************************/
// Extracts features from the given blob and saves them in the tr_file_data_
// member variable.
// fontname: Name of font that this blob was printed in.
// cn_denorm: Character normalization transformation to apply to the blob.
// fx_info: Character normalization parameters computed with cn_denorm.
// blob_text: Ground truth text for the blob.
void LearnBlob(const STRING& fontname, TBLOB* Blob, const DENORM& cn_denorm,
const INT_FX_RESULT_STRUCT& fx_info, const char* blob_text);
// Writes stored training data to a .tr file based on the given filename.
// Returns false on error.
bool WriteTRFile(const STRING& filename);
// Member variables.
// Parameters.
// Set during training (in lang.config) to indicate whether the divisible
// blobs chopper should be used (true for latin script.)
BOOL_VAR_H(allow_blob_division, true, "Use divisible blobs chopping");
// Set during training (in lang.config) to indicate whether the divisible
// blobs chopper should be used in preference to chopping. Set to true for
// southern Indic scripts.
BOOL_VAR_H(prioritize_division, FALSE,
"Prioritize blob division over chopping");
INT_VAR_H(tessedit_single_match, FALSE, "Top choice only from CP");
BOOL_VAR_H(classify_enable_learning, true, "Enable adaptive classifier");
INT_VAR_H(classify_debug_level, 0, "Classify debug level");
/* mfoutline.cpp ***********************************************************/
/* control knobs used to control normalization of outlines */
INT_VAR_H(classify_norm_method, character, "Normalization Method ...");
double_VAR_H(classify_char_norm_range, 0.2,
"Character Normalization Range ...");
double_VAR_H(classify_min_norm_scale_x, 0.0, "Min char x-norm scale ...");
double_VAR_H(classify_max_norm_scale_x, 0.325, "Max char x-norm scale ...");
double_VAR_H(classify_min_norm_scale_y, 0.0, "Min char y-norm scale ...");
double_VAR_H(classify_max_norm_scale_y, 0.325, "Max char y-norm scale ...");
double_VAR_H(classify_max_rating_ratio, 1.5,
"Veto ratio between classifier ratings");
double_VAR_H(classify_max_certainty_margin, 5.5,
"Veto difference between classifier certainties");
/* adaptmatch.cpp ***********************************************************/
BOOL_VAR_H(tess_cn_matching, 0, "Character Normalized Matching");
BOOL_VAR_H(tess_bn_matching, 0, "Baseline Normalized Matching");
BOOL_VAR_H(classify_enable_adaptive_matcher, 1, "Enable adaptive classifier");
BOOL_VAR_H(classify_use_pre_adapted_templates, 0,
"Use pre-adapted classifier templates");
BOOL_VAR_H(classify_save_adapted_templates, 0,
"Save adapted templates to a file");
BOOL_VAR_H(classify_enable_adaptive_debugger, 0, "Enable match debugger");
BOOL_VAR_H(classify_nonlinear_norm, 0,
"Non-linear stroke-density normalization");
INT_VAR_H(matcher_debug_level, 0, "Matcher Debug Level");
INT_VAR_H(matcher_debug_flags, 0, "Matcher Debug Flags");
INT_VAR_H(classify_learning_debug_level, 0, "Learning Debug Level: ");
double_VAR_H(matcher_good_threshold, 0.125, "Good Match (0-1)");
double_VAR_H(matcher_reliable_adaptive_result, 0.0, "Great Match (0-1)");
double_VAR_H(matcher_perfect_threshold, 0.02, "Perfect Match (0-1)");
double_VAR_H(matcher_bad_match_pad, 0.15, "Bad Match Pad (0-1)");
double_VAR_H(matcher_rating_margin, 0.1, "New template margin (0-1)");
double_VAR_H(matcher_avg_noise_size, 12.0, "Avg. noise blob length: ");
INT_VAR_H(matcher_permanent_classes_min, 1, "Min # of permanent classes");
INT_VAR_H(matcher_min_examples_for_prototyping, 3,
"Reliable Config Threshold");
INT_VAR_H(matcher_sufficient_examples_for_prototyping, 5,
"Enable adaption even if the ambiguities have not been seen");
double_VAR_H(matcher_clustering_max_angle_delta, 0.015,
"Maximum angle delta for prototype clustering");
double_VAR_H(classify_misfit_junk_penalty, 0.0,
"Penalty to apply when a non-alnum is vertically out of "
"its expected textline position");
double_VAR_H(rating_scale, 1.5, "Rating scaling factor");
double_VAR_H(certainty_scale, 20.0, "Certainty scaling factor");
double_VAR_H(tessedit_class_miss_scale, 0.00390625,
"Scale factor for features not used");
double_VAR_H(classify_adapted_pruning_factor, 2.5,
"Prune poor adapted results this much worse than best result");
double_VAR_H(classify_adapted_pruning_threshold, -1.0,
"Threshold at which classify_adapted_pruning_factor starts");
INT_VAR_H(classify_adapt_proto_threshold, 230,
"Threshold for good protos during adaptive 0-255");
INT_VAR_H(classify_adapt_feature_threshold, 230,
"Threshold for good features during adaptive 0-255");
BOOL_VAR_H(disable_character_fragments, TRUE,
"Do not include character fragments in the"
" results of the classifier");
double_VAR_H(classify_character_fragments_garbage_certainty_threshold, -3.0,
"Exclude fragments that do not match any whole character"
" with at least this certainty");
BOOL_VAR_H(classify_debug_character_fragments, FALSE,
"Bring up graphical debugging windows for fragments training");
BOOL_VAR_H(matcher_debug_separate_windows, FALSE,
"Use two different windows for debugging the matching: "
"One for the protos and one for the features.");
STRING_VAR_H(classify_learn_debug_str, "", "Class str to debug learning");
/* intmatcher.cpp **********************************************************/
INT_VAR_H(classify_class_pruner_threshold, 229,
"Class Pruner Threshold 0-255");
INT_VAR_H(classify_class_pruner_multiplier, 15,
"Class Pruner Multiplier 0-255: ");
INT_VAR_H(classify_cp_cutoff_strength, 7,
"Class Pruner CutoffStrength: ");
INT_VAR_H(classify_integer_matcher_multiplier, 10,
"Integer Matcher Multiplier 0-255: ");
// Use class variables to hold onto built-in templates and adapted templates.
INT_TEMPLATES PreTrainedTemplates;
ADAPT_TEMPLATES AdaptedTemplates;
// The backup adapted templates are created from the previous page (only)
// so they are always ready and reasonably well trained if the primary
// adapted templates become full.
ADAPT_TEMPLATES BackupAdaptedTemplates;
// Create dummy proto and config masks for use with the built-in templates.
BIT_VECTOR AllProtosOn;
BIT_VECTOR AllConfigsOn;
BIT_VECTOR AllConfigsOff;
BIT_VECTOR TempProtoMask;
bool EnableLearning;
/* normmatch.cpp */
NORM_PROTOS *NormProtos;
/* font detection ***********************************************************/
UnicityTable<FontInfo> fontinfo_table_;
// Without shape training, each class_id, config pair represents a single
// unichar id/font combination, so each fontset_table_ entry holds font ids
// for each config in the class.
// With shape training, each class_id, config pair represents a single
// shape_table_ index, so the fontset_table_ stores the shape_table_ index,
// and the shape_table_ must be consulted to obtain the actual unichar_id/
// font combinations that the shape represents.
UnicityTable<FontSet> fontset_table_;
INT_VAR_H(il1_adaption_test, 0, "Don't adapt to i/I at beginning of word");
BOOL_VAR_H(classify_bln_numeric_mode, 0,
"Assume the input is numbers [0-9].");
double_VAR_H(speckle_large_max_size, 0.30, "Max large speckle size");
double_VAR_H(speckle_rating_penalty, 10.0,
"Penalty to add to worst rating for noise");
protected:
IntegerMatcher im_;
FEATURE_DEFS_STRUCT feature_defs_;
// If a shape_table_ is present, it is used to remap classifier output in
// ExpandShapesAndApplyCorrections. font_ids referenced by configs actually
// mean an index to the shape_table_ and the choices returned are *all* the
// shape_table_ entries at that index.
ShapeTable* shape_table_;
private:
Dict dict_;
// The currently active static classifier.
ShapeClassifier* static_classifier_;
/* variables used to hold performance statistics */
int NumAdaptationsFailed;
// Training data gathered here for all the images in a document.
STRING tr_file_data_;
// Expected number of features in the class pruner, used to penalize
// unknowns that have too few features (like a c being classified as e) so
// it doesn't recognize everything as '@' or '#'.
// CharNormCutoffs is for the static classifier (with no shapetable).
// BaselineCutoffs gets a copy of CharNormCutoffs as an estimate of the real
// value in the adaptive classifier. Both are indexed by unichar_id.
// shapetable_cutoffs_ provides a similar value for each shape in the
// shape_table_
uinT16 CharNormCutoffs[MAX_NUM_CLASSES];
uinT16 BaselineCutoffs[MAX_NUM_CLASSES];
GenericVector<uinT16> shapetable_cutoffs_;
ScrollView* learn_debug_win_;
ScrollView* learn_fragmented_word_debug_win_;
ScrollView* learn_fragments_debug_win_;
};
} // namespace tesseract
#endif // TESSERACT_CLASSIFY_CLASSIFY_H_
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