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tesseract/classify/classify.h
Stefan Weil 55fde61a8f classify: Fix typos in comments and strings 
All of them were found by codespell.

Signed-off-by: Stefan Weil <sw@weilnetz.de>
2015-09-14 22:12:06 +02:00

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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(FILE *CutoffFile, bool swap, inT64 end_offset,
CLASS_CUTOFF_ARRAY Cutoffs);
void PrintAdaptedTemplates(FILE *File, ADAPT_TEMPLATES Templates);
void WriteAdaptedTemplates(FILE *File, ADAPT_TEMPLATES Templates);
ADAPT_TEMPLATES ReadAdaptedTemplates(FILE *File);
/* normmatch.cpp ************************************************************/
FLOAT32 ComputeNormMatch(CLASS_ID ClassId,
const FEATURE_STRUCT& feature, BOOL8 DebugMatch);
void FreeNormProtos();
NORM_PROTOS *ReadNormProtos(FILE *File, inT64 end_offset);
/* 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(bool load_pre_trained_templates);
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(FILE *File);
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;
uinT16* BaselineCutoffs;
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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