#include <classify.h>

Inheritance diagram for tesseract::Classify:

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Collaboration diagram for tesseract::Classify:

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Public Member Functions
	Classify ()

virtual	~Classify ()

virtual Dict &	getDict ()

const ShapeTable *	shape_table () const

void	SetStaticClassifier (ShapeClassifier *static_classifier)

void	AddLargeSpeckleTo (int blob_length, BLOB_CHOICE_LIST *choices)

bool	LargeSpeckle (const TBLOB &blob)

ADAPT_TEMPLATES	NewAdaptedTemplates (bool InitFromUnicharset)

int	GetFontinfoId (ADAPT_CLASS Class, uint8_t ConfigId)

int	PruneClasses (const INT_TEMPLATES_STRUCT int_templates, int num_features, int keep_this, const INT_FEATURE_STRUCT features, const uint8_t normalization_factors, const uint16_t expected_num_features, GenericVector< CP_RESULT_STRUCT > *results)

void	ReadNewCutoffs (TFile *fp, CLASS_CUTOFF_ARRAY Cutoffs)

void	PrintAdaptedTemplates (FILE *File, ADAPT_TEMPLATES Templates)

void	WriteAdaptedTemplates (FILE *File, ADAPT_TEMPLATES Templates)

ADAPT_TEMPLATES	ReadAdaptedTemplates (TFile *File)

float	ComputeNormMatch (CLASS_ID ClassId, const FEATURE_STRUCT &feature, bool DebugMatch)

void	FreeNormProtos ()

NORM_PROTOS *	ReadNormProtos (TFile *fp)

void	ConvertProto (PROTO Proto, int ProtoId, INT_CLASS Class)

INT_TEMPLATES	CreateIntTemplates (CLASSES FloatProtos, const UNICHARSET &target_unicharset)

void	LearnWord (const char fontname, WERD_RES word)

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_t num_features, const INT_FEATURE_STRUCT features, const uint8_t norm_factors, ADAPT_CLASS classes, int debug, int matcher_multiplier, const TBOX &blob_box, const GenericVector< CP_RESULT_STRUCT > &results, ADAPT_RESULTS final_results)

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_t cn_factors, UnicharRating int_result, ADAPT_RESULTS final_results)

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_t *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)

void	DebugAdaptiveClassifier (TBLOB Blob, ADAPT_RESULTS Results)

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 (float Threshold)

void	ShowBestMatchFor (int shape_id, const INT_FEATURE_STRUCT *features, int num_features)

STRING	ClassIDToDebugStr (const INT_TEMPLATES_STRUCT *templates, int class_id, int config_id) const

int	ClassAndConfigIDToFontOrShapeID (int class_id, int int_result_config) const

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)

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, float 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_t pruner_norm_array, uint8_t char_norm_array)

void	ComputeCharNormArrays (FEATURE_STRUCT norm_feature, INT_TEMPLATES_STRUCT templates, uint8_t char_norm_array, uint8_t pruner_array)

bool	TempConfigReliable (CLASS_ID class_id, const TEMP_CONFIG &config)

void	UpdateAmbigsGroup (CLASS_ID class_id, TBLOB *Blob)

bool	AdaptiveClassifierIsFull () const

bool	AdaptiveClassifierIsEmpty () const

bool	LooksLikeGarbage (TBLOB *blob)

void	RefreshDebugWindow (ScrollView *win, const char msg, int y_offset, const TBOX &wbox)

void	ClearCharNormArray (uint8_t *char_norm_array)

void	ComputeIntCharNormArray (const FEATURE_STRUCT &norm_feature, uint8_t *char_norm_array)

void	ComputeIntFeatures (FEATURE_SET Features, INT_FEATURE_ARRAY IntFeatures)

INT_TEMPLATES	ReadIntTemplates (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 ()

UnicityTable< FontInfo > &	get_fontinfo_table ()

const UnicityTable< FontInfo > &	get_fontinfo_table () const

UnicityTable< FontSet > &	get_fontset_table ()

void	NormalizeOutlines (LIST Outlines, float XScale, float YScale)

FEATURE_SET	ExtractOutlineFeatures (TBLOB *Blob)

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)

void	LearnBlob (const STRING &fontname, TBLOB Blob, const DENORM &cn_denorm, const INT_FX_RESULT_STRUCT &fx_info, const char blob_text)

bool	WriteTRFile (const STRING &filename)

	BOOL_VAR_H (allow_blob_division, true, "Use divisible blobs chopping")

	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")

	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")

	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")

	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: ")

	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")

Public Member Functions inherited from tesseract::CCStruct
	CCStruct ()=default

virtual	~CCStruct ()

Public Member Functions inherited from tesseract::CUtil
	CUtil ()=default

virtual	~CUtil ()

void	read_variables (const char *filename, bool global_only)

Public Member Functions inherited from tesseract::CCUtil
	CCUtil ()

virtual	~CCUtil ()

void	main_setup (const char argv0, const char basename)
	CCUtil::main_setup - set location of tessdata and name of image. More...

ParamsVectors *	params ()

Static Public Member Functions
static void	SetupBLCNDenorms (const TBLOB &blob, bool nonlinear_norm, DENORM bl_denorm, DENORM cn_denorm, INT_FX_RESULT_STRUCT *fx_info)

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)

Public Attributes
INT_TEMPLATES	PreTrainedTemplates

ADAPT_TEMPLATES	AdaptedTemplates

ADAPT_TEMPLATES	BackupAdaptedTemplates

BIT_VECTOR	AllProtosOn

BIT_VECTOR	AllConfigsOn

BIT_VECTOR	AllConfigsOff

BIT_VECTOR	TempProtoMask

bool	EnableLearning

NORM_PROTOS *	NormProtos

UnicityTable< FontInfo >	fontinfo_table_

UnicityTable< FontSet >	fontset_table_

Public Attributes inherited from tesseract::CCUtil
STRING	datadir

STRING	imagebasename

STRING	lang

STRING	language_data_path_prefix

UNICHARSET	unicharset

UnicharAmbigs	unichar_ambigs

STRING	imagefile

STRING	directory

tesseract::IntParam	ambigs_debug_level

tesseract::BoolParam	use_definite_ambigs_for_classifier

tesseract::BoolParam	use_ambigs_for_adaption

Protected Attributes
IntegerMatcher	im_

FEATURE_DEFS_STRUCT	feature_defs_

ShapeTable *	shape_table_

Private Attributes
Dict	dict_

ShapeClassifier *	static_classifier_

int	NumAdaptationsFailed

STRING	tr_file_data_

uint16_t	CharNormCutoffs [MAX_NUM_CLASSES]

uint16_t	BaselineCutoffs [MAX_NUM_CLASSES]

GenericVector< uint16_t >	shapetable_cutoffs_

ScrollView *	learn_debug_win_

ScrollView *	learn_fragmented_word_debug_win_

ScrollView *	learn_fragments_debug_win_

Additional Inherited Members
Static Public Attributes inherited from tesseract::CCStruct
static const double	kDescenderFraction = 0.25

static const double	kXHeightFraction = 0.5

static const double	kAscenderFraction = 0.25

static const double	kXHeightCapRatio

Constructor & Destructor Documentation

◆ Classify()

tesseract::Classify::Classify ( )

◆ ~Classify()

tesseract::Classify::~Classify ( )

virtual

Member Function Documentation

◆ AdaptableWord()

bool tesseract::Classify::AdaptableWord ( WERD_RES * word )

Return TRUE if the specified word is acceptable for adaptation.

Globals: none

Parameters

word	current word

Returns: true or false

◆ AdaptiveClassifier()

void tesseract::Classify::AdaptiveClassifier	(	TBLOB *	Blob,
		BLOB_CHOICE_LIST *	Choices
	)

This routine calls the adaptive matcher which returns (in an array) the class id of each class matched.

It also returns the number of classes matched. For each class matched it places the best rating found for that class into the Ratings array.

Bad matches are then removed so that they don't need to be sorted. The remaining good matches are then sorted and converted to choices.

This routine also performs some simple speckle filtering.

Parameters

	Blob	blob to be classified
[out]	Choices	List of choices found by adaptive matcher. filled on return with the choices found by the class pruner and the ratings therefrom. Also contains the detailed results of the integer matcher.

◆ AdaptiveClassifierIsEmpty()

bool tesseract::Classify::AdaptiveClassifierIsEmpty ( ) const

inline

◆ AdaptiveClassifierIsFull()

bool tesseract::Classify::AdaptiveClassifierIsFull ( ) const

inline

◆ AdaptToChar()

void tesseract::Classify::AdaptToChar	(	TBLOB *	Blob,
		CLASS_ID	ClassId,
		int	FontinfoId,
		float	Threshold,
		ADAPT_TEMPLATES	adaptive_templates
	)

Parameters

Blob	blob to add to templates for ClassId
ClassId	class to add blob to
FontinfoId	font information from pre-trained templates
Threshold	minimum match rating to existing template
adaptive_templates	current set of adapted templates

Globals:

AllProtosOn dummy mask to match against all protos
AllConfigsOn dummy mask to match against all configs

Returns: none

◆ AddLargeSpeckleTo()

void tesseract::Classify::AddLargeSpeckleTo	(	int	blob_length,
		BLOB_CHOICE_LIST *	choices
	)

◆ AddNewResult()

void tesseract::Classify::AddNewResult	(	const UnicharRating &	new_result,
		ADAPT_RESULTS *	results
	)

This routine adds the result of a classification into Results. If the new rating is much worse than the current best rating, it is not entered into results because it would end up being stripped later anyway. If the new rating is better than the old rating for the class, it replaces the old rating. If this is the first rating for the class, the class is added to the list of matched classes in Results. If the new rating is better than the best so far, it becomes the best so far.

Globals:

#matcher_bad_match_pad defines limits of an acceptable match

Parameters

	new_result	new result to add
[out]	results	results to add new result to

◆ AmbigClassifier()

void tesseract::Classify::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
	)

This routine is identical to CharNormClassifier() except that it does no class pruning. It simply matches the unknown blob against the classes listed in Ambiguities.

Globals:

AllProtosOn mask that enables all protos
AllConfigsOn mask that enables all configs

Parameters

	blob	blob to be classified
	templates	built-in templates to classify against
	classes	adapted class templates
	ambiguities	array of unichar id's to match against
[out]	results	place to put match results
	int_features
	fx_info

◆ BaselineClassifier()

UNICHAR_ID * tesseract::Classify::BaselineClassifier	(	TBLOB *	Blob,
		const GenericVector< INT_FEATURE_STRUCT > &	int_features,
		const INT_FX_RESULT_STRUCT &	fx_info,
		ADAPT_TEMPLATES	Templates,
		ADAPT_RESULTS *	Results
	)

This routine extracts baseline normalized features from the unknown character and matches them against the specified set of templates. The classes which match are added to Results.

Globals:

BaselineCutoffs expected num features for each class

Parameters

Blob	blob to be classified
Templates	current set of adapted templates
Results	place to put match results
int_features
fx_info

Returns: Array of possible ambiguous chars that should be checked.

◆ BOOL_VAR_H() [1/14]

tesseract::Classify::BOOL_VAR_H	(	allow_blob_division	,
		true	,
		"Use divisible blobs chopping"
	)

◆ BOOL_VAR_H() [2/14]

tesseract::Classify::BOOL_VAR_H	(	prioritize_division	,
		FALSE	,
		"Prioritize blob division over chopping"
	)

◆ BOOL_VAR_H() [3/14]

tesseract::Classify::BOOL_VAR_H	(	classify_enable_learning	,
		true	,
		"Enable adaptive classifier"
	)

◆ BOOL_VAR_H() [4/14]

tesseract::Classify::BOOL_VAR_H	(	tess_cn_matching	,
		0	,
		"Character Normalized Matching"
	)

◆ BOOL_VAR_H() [5/14]

tesseract::Classify::BOOL_VAR_H	(	tess_bn_matching	,
		0	,
		"Baseline Normalized Matching"
	)

◆ BOOL_VAR_H() [6/14]

tesseract::Classify::BOOL_VAR_H	(	classify_enable_adaptive_matcher	,
		1	,
		"Enable adaptive classifier"
	)

◆ BOOL_VAR_H() [7/14]

tesseract::Classify::BOOL_VAR_H	(	classify_use_pre_adapted_templates	,
		0	,
		"Use pre-adapted classifier templates"
	)

◆ BOOL_VAR_H() [8/14]

tesseract::Classify::BOOL_VAR_H	(	classify_save_adapted_templates	,
		0	,
		"Save adapted templates to a file"
	)

◆ BOOL_VAR_H() [9/14]

tesseract::Classify::BOOL_VAR_H	(	classify_enable_adaptive_debugger	,
		0	,
		"Enable match debugger"
	)

◆ BOOL_VAR_H() [10/14]

tesseract::Classify::BOOL_VAR_H	(	classify_nonlinear_norm	,
		0	,
		"Non-linear stroke-density normalization"
	)

◆ BOOL_VAR_H() [11/14]

tesseract::Classify::BOOL_VAR_H	(	disable_character_fragments	,
		TRUE	,
		"Do not include character fragments in the" " results of the classifier"
	)

◆ BOOL_VAR_H() [12/14]

tesseract::Classify::BOOL_VAR_H	(	classify_debug_character_fragments	,
		FALSE	,
		"Bring up graphical debugging windows for fragments training"
	)

◆ BOOL_VAR_H() [13/14]

tesseract::Classify::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."
	)

◆ BOOL_VAR_H() [14/14]

tesseract::Classify::BOOL_VAR_H	(	classify_bln_numeric_mode	,
		0	,
		"Assume the input is numbers ."	[0-9]
	)

◆ CharNormClassifier()

int tesseract::Classify::CharNormClassifier	(	TBLOB *	blob,
		const TrainingSample &	sample,
		ADAPT_RESULTS *	adapt_results
	)

This routine extracts character normalized features from the unknown character and matches them against the specified set of templates. The classes which match are added to Results.

Parameters

blob	blob to be classified
sample	templates to classify unknown against
adapt_results	place to put match results

Globals:

CharNormCutoffs expected num features for each class
AllProtosOn mask that enables all protos
AllConfigsOn mask that enables all configs

◆ CharNormTrainingSample()

int tesseract::Classify::CharNormTrainingSample	(	bool	pruner_only,
		int	keep_this,
		const TrainingSample &	sample,
		GenericVector< UnicharRating > *	results
	)

◆ ClassAndConfigIDToFontOrShapeID()

int tesseract::Classify::ClassAndConfigIDToFontOrShapeID	(	int	class_id,
		int	int_result_config
	)		const

◆ ClassIDToDebugStr()

STRING tesseract::Classify::ClassIDToDebugStr	(	const INT_TEMPLATES_STRUCT *	templates,
		int	class_id,
		int	config_id
	)		const

◆ ClassifyAsNoise()

void tesseract::Classify::ClassifyAsNoise ( ADAPT_RESULTS * results )

This routine computes a rating which reflects the likelihood that the blob being classified is a noise blob. NOTE: assumes that the blob length has already been computed and placed into Results.

Parameters

results results to add noise classification to

Globals:

matcher_avg_noise_size avg. length of a noise blob

◆ ClearCharNormArray()

void tesseract::Classify::ClearCharNormArray ( uint8_t * char_norm_array )

For each class in the unicharset, clears the corresponding entry in char_norm_array. char_norm_array is indexed by unichar_id.

Globals:

none

Parameters

char_norm_array array to be cleared

◆ ComputeCharNormArrays()

void tesseract::Classify::ComputeCharNormArrays	(	FEATURE_STRUCT *	norm_feature,
		INT_TEMPLATES_STRUCT *	templates,
		uint8_t *	char_norm_array,
		uint8_t *	pruner_array
	)

◆ ComputeCorrectedRating()

double tesseract::Classify::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_t *	cn_factors
	)

◆ ComputeIntCharNormArray()

void tesseract::Classify::ComputeIntCharNormArray	(	const FEATURE_STRUCT &	norm_feature,
		uint8_t *	char_norm_array
	)

For each class in unicharset, computes the match between norm_feature and the normalization protos for that class. Converts this number to the range from 0 - 255 and stores it into char_norm_array. CharNormArray is indexed by unichar_id.

Globals:

PreTrainedTemplates current set of built-in templates

Parameters

	norm_feature	character normalization feature
[out]	char_norm_array	place to put results of size unicharset.size()

◆ ComputeIntFeatures()

void tesseract::Classify::ComputeIntFeatures	(	FEATURE_SET	Features,
		INT_FEATURE_ARRAY	IntFeatures
	)

This routine converts each floating point pico-feature in Features into integer format and saves it into IntFeatures.

Globals:

none

Parameters

	Features	floating point pico-features to be converted
[out]	IntFeatures	array to put converted features into

◆ ComputeNormMatch()

float tesseract::Classify::ComputeNormMatch	(	CLASS_ID	ClassId,
		const FEATURE_STRUCT &	feature,
		bool	DebugMatch
	)

This routine compares Features against each character normalization proto for ClassId and returns the match rating of the best match.

Parameters

ClassId	id of class to match against
feature	character normalization feature
DebugMatch	controls dump of debug info

Globals: NormProtos character normalization prototypes

Returns: Best match rating for Feature against protos of ClassId.

◆ ConvertMatchesToChoices()

void tesseract::Classify::ConvertMatchesToChoices	(	const DENORM &	denorm,
		const TBOX &	box,
		ADAPT_RESULTS *	Results,
		BLOB_CHOICE_LIST *	Choices
	)

The function converts the given match ratings to the list of blob choices with ratings and certainties (used by the context checkers). If character fragments are present in the results, this function also makes sure that there is at least one non-fragmented classification included. For each classification result check the unicharset for "definite" ambiguities and modify the resulting Choices accordingly.

◆ ConvertProto()

void tesseract::Classify::ConvertProto	(	PROTO	Proto,
		int	ProtoId,
		INT_CLASS	Class
	)

This routine converts Proto to integer format and installs it as ProtoId in Class.

Parameters

Proto	floating-pt proto to be converted to integer format
ProtoId	id of proto
Class	integer class to add converted proto to

Returns: none

Note: Globals: none

◆ CreateIntTemplates()

INT_TEMPLATES tesseract::Classify::CreateIntTemplates	(	CLASSES	FloatProtos,
		const UNICHARSET &	target_unicharset
	)

This routine converts from the old floating point format to the new integer format.

Parameters

FloatProtos	prototypes in old floating pt format
target_unicharset	the UNICHARSET to use

Returns: New set of training templates in integer format.

Note: Globals: none

◆ DebugAdaptiveClassifier()

void tesseract::Classify::DebugAdaptiveClassifier	(	TBLOB *	blob,
		ADAPT_RESULTS *	Results
	)

Parameters

blob	blob whose classification is being debugged
Results	results of match being debugged

Globals: none

◆ DisplayAdaptedChar()

void tesseract::Classify::DisplayAdaptedChar	(	TBLOB *	blob,
		INT_CLASS_STRUCT *	int_class
	)

◆ DoAdaptiveMatch()

void tesseract::Classify::DoAdaptiveMatch	(	TBLOB *	Blob,
		ADAPT_RESULTS *	Results
	)

This routine performs an adaptive classification. If we have not yet adapted to enough classes, a simple classification to the pre-trained templates is performed. Otherwise, we match the blob against the adapted templates. If the adapted templates do not match well, we try a match against the pre-trained templates. If an adapted template match is found, we do a match to any pre-trained templates which could be ambiguous. The results from all of these classifications are merged together into Results.

Parameters

Blob	blob to be classified
Results	place to put match results

Globals:

PreTrainedTemplates built-in training templates
AdaptedTemplates templates adapted for this page
matcher_reliable_adaptive_result rating limit for a great match

◆ double_VAR_H() [1/23]

tesseract::Classify::double_VAR_H	(	classify_char_norm_range	,
		0.	2,
		"Character Normalization Range ..."
	)

◆ double_VAR_H() [2/23]

tesseract::Classify::double_VAR_H	(	classify_min_norm_scale_x	,
		0.	0,
		"Min char x-norm scale ..."
	)

◆ double_VAR_H() [3/23]

tesseract::Classify::double_VAR_H	(	classify_max_norm_scale_x	,
		0.	325,
		"Max char x-norm scale ..."
	)

◆ double_VAR_H() [4/23]

tesseract::Classify::double_VAR_H	(	classify_min_norm_scale_y	,
		0.	0,
		"Min char y-norm scale ..."
	)

◆ double_VAR_H() [5/23]

tesseract::Classify::double_VAR_H	(	classify_max_norm_scale_y	,
		0.	325,
		"Max char y-norm scale ..."
	)

◆ double_VAR_H() [6/23]

tesseract::Classify::double_VAR_H	(	classify_max_rating_ratio	,
		1.	5,
		"Veto ratio between classifier ratings"
	)

◆ double_VAR_H() [7/23]

tesseract::Classify::double_VAR_H	(	classify_max_certainty_margin	,
		5.	5,
		"Veto difference between classifier certainties"
	)

◆ double_VAR_H() [8/23]

tesseract::Classify::double_VAR_H	(	matcher_good_threshold	,
		0.	125,
		"Good Match (0-1)"
	)

◆ double_VAR_H() [9/23]

tesseract::Classify::double_VAR_H	(	matcher_reliable_adaptive_result	,
		0.	0,
		"Great Match (0-1)"
	)

◆ double_VAR_H() [10/23]

tesseract::Classify::double_VAR_H	(	matcher_perfect_threshold	,
		0.	02,
		"Perfect Match (0-1)"
	)

◆ double_VAR_H() [11/23]

tesseract::Classify::double_VAR_H	(	matcher_bad_match_pad	,
		0.	15,
		"Bad Match Pad (0-1)"
	)

◆ double_VAR_H() [12/23]

tesseract::Classify::double_VAR_H	(	matcher_rating_margin	,
		0.	1,
		"New template margin (0-1)"
	)

◆ double_VAR_H() [13/23]

tesseract::Classify::double_VAR_H	(	matcher_avg_noise_size	,
		12.	0,
		"Avg. noise blob length: "
	)

◆ double_VAR_H() [14/23]

tesseract::Classify::double_VAR_H	(	matcher_clustering_max_angle_delta	,
		0.	015,
		"Maximum angle delta for prototype clustering"
	)

◆ double_VAR_H() [15/23]

tesseract::Classify::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() [16/23]

tesseract::Classify::double_VAR_H	(	rating_scale	,
		1.	5,
		"Rating scaling factor"
	)

◆ double_VAR_H() [17/23]

tesseract::Classify::double_VAR_H	(	certainty_scale	,
		20.	0,
		"Certainty scaling factor"
	)

◆ double_VAR_H() [18/23]

tesseract::Classify::double_VAR_H	(	tessedit_class_miss_scale	,
		0.	00390625,
		"Scale factor for features not used"
	)

◆ double_VAR_H() [19/23]

tesseract::Classify::double_VAR_H	(	classify_adapted_pruning_factor	,
		2.	5,
		"Prune poor adapted results this much worse than best result"
	)

◆ double_VAR_H() [20/23]

tesseract::Classify::double_VAR_H	(	classify_adapted_pruning_threshold	,
		-1.	0,
		"Threshold at which classify_adapted_pruning_factor starts"
	)

◆ double_VAR_H() [21/23]

tesseract::Classify::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"
	)

◆ double_VAR_H() [22/23]

tesseract::Classify::double_VAR_H	(	speckle_large_max_size	,
		0.	30,
		"Max large speckle size"
	)

◆ double_VAR_H() [23/23]

tesseract::Classify::double_VAR_H	(	speckle_rating_penalty	,
		10.	0,
		"Penalty to add to worst rating for noise"
	)

◆ EndAdaptiveClassifier()

void tesseract::Classify::EndAdaptiveClassifier ( )

This routine performs cleanup operations on the adaptive classifier. It should be called before the program is terminated. Its main function is to save the adapted templates to a file.

Globals:

AdaptedTemplates current set of adapted templates
#classify_save_adapted_templates TRUE if templates should be saved
#classify_enable_adaptive_matcher TRUE if adaptive matcher is enabled

◆ ExpandShapesAndApplyCorrections()

void tesseract::Classify::ExpandShapesAndApplyCorrections	(	ADAPT_CLASS *	classes,
		bool	debug,
		int	class_id,
		int	bottom,
		int	top,
		float	cp_rating,
		int	blob_length,
		int	matcher_multiplier,
		const uint8_t *	cn_factors,
		UnicharRating *	int_result,
		ADAPT_RESULTS *	final_results
	)

◆ ExtractFeatures()

void tesseract::Classify::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
	)

static

◆ ExtractIntCNFeatures()

FEATURE_SET tesseract::Classify::ExtractIntCNFeatures	(	const TBLOB &	blob,
		const INT_FX_RESULT_STRUCT &	fx_info
	)

Parameters

blob	blob to extract features from
fx_info

Returns: Integer character-normalized features for blob.

◆ ExtractIntGeoFeatures()

FEATURE_SET tesseract::Classify::ExtractIntGeoFeatures	(	const TBLOB &	blob,
		const INT_FX_RESULT_STRUCT &	fx_info
	)

Parameters

blob	blob to extract features from
fx_info

Returns: Geometric (top/bottom/width) features for blob.

◆ ExtractOutlineFeatures()

FEATURE_SET tesseract::Classify::ExtractOutlineFeatures ( TBLOB * Blob )

Convert each segment in the outline to a feature and return the features.

Parameters

Blob	blob to extract pico-features from

Returns: Outline-features for Blob.

Note: Globals: none

◆ ExtractPicoFeatures()

FEATURE_SET tesseract::Classify::ExtractPicoFeatures ( TBLOB * Blob )

Operation: Dummy for now.

Globals:

classify_norm_method normalization method currently specified
Parameters

Blob blob to extract pico-features from

Returns
Pico-features for Blob.

◆ FreeNormProtos()

void tesseract::Classify::FreeNormProtos ( )

◆ get_fontinfo_table() [1/2]

UnicityTable<FontInfo>& tesseract::Classify::get_fontinfo_table ( )

inline

◆ get_fontinfo_table() [2/2]

const UnicityTable<FontInfo>& tesseract::Classify::get_fontinfo_table ( ) const

inline

◆ get_fontset_table()

UnicityTable<FontSet>& tesseract::Classify::get_fontset_table ( )

inline

◆ GetAdaptiveFeatures()

int tesseract::Classify::GetAdaptiveFeatures	(	TBLOB *	Blob,
		INT_FEATURE_ARRAY	IntFeatures,
		FEATURE_SET *	FloatFeatures
	)

This routine sets up the feature extractor to extract baseline normalized pico-features.

The extracted pico-features are converted to integer form and placed in IntFeatures. The original floating-pt. features are returned in FloatFeatures.

Globals: none

Parameters

	Blob	blob to extract features from
[out]	IntFeatures	array to fill with integer features
[out]	FloatFeatures	place to return actual floating-pt features

Returns: Number of pico-features returned (0 if an error occurred)

◆ GetAmbiguities()

UNICHAR_ID * tesseract::Classify::GetAmbiguities	(	TBLOB *	Blob,
		CLASS_ID	CorrectClass
	)

This routine matches blob to the built-in templates to find out if there are any classes other than the correct class which are potential ambiguities.

Parameters

Blob	blob to get classification ambiguities for
CorrectClass	correct class for Blob

Globals:

CurrentRatings used by qsort compare routine
PreTrainedTemplates built-in templates

Returns: String containing all possible ambiguous classes.

◆ GetCharNormFeature()

int tesseract::Classify::GetCharNormFeature	(	const INT_FX_RESULT_STRUCT &	fx_info,
		INT_TEMPLATES	templates,
		uint8_t *	pruner_norm_array,
		uint8_t *	char_norm_array
	)

This routine calls the integer (Hardware) feature extractor if it has not been called before for this blob.

The results from the feature extractor are placed into globals so that they can be used in other routines without re-extracting the features.

It then copies the char norm features into the IntFeatures array provided by the caller.

Parameters

templates	used to compute char norm adjustments
pruner_norm_array	Array of factors from blob normalization process
char_norm_array	array to fill with dummy char norm adjustments
fx_info	Globals:

Returns: Number of features extracted or 0 if an error occurred.

◆ GetClassToDebug()

CLASS_ID tesseract::Classify::GetClassToDebug	(	const char *	Prompt,
		bool *	adaptive_on,
		bool *	pretrained_on,
		int *	shape_id
	)

This routine prompts the user with Prompt and waits for the user to enter something in the debug window.

Parameters

Prompt	prompt to print while waiting for input from window
adaptive_on
pretrained_on
shape_id

Returns: Character entered in the debug window.

Note: Globals: none

◆ getDict()

virtual Dict& tesseract::Classify::getDict ( )

inlinevirtual

Reimplemented in tesseract::Tesseract.

◆ GetFontinfoId()

int tesseract::Classify::GetFontinfoId	(	ADAPT_CLASS	Class,
		uint8_t	ConfigId
	)

◆ InitAdaptedClass()

void tesseract::Classify::InitAdaptedClass	(	TBLOB *	Blob,
		CLASS_ID	ClassId,
		int	FontinfoId,
		ADAPT_CLASS	Class,
		ADAPT_TEMPLATES	Templates
	)

This routine creates a new adapted class and uses Blob as the model for the first config in that class.

Parameters

Blob	blob to model new class after
ClassId	id of the class to be initialized
FontinfoId	font information inferred from pre-trained templates
Class	adapted class to be initialized
Templates	adapted templates to add new class to

Globals:

AllProtosOn dummy mask with all 1's
BaselineCutoffs kludge needed to get cutoffs
PreTrainedTemplates kludge needed to get cutoffs

◆ InitAdaptiveClassifier()

void tesseract::Classify::InitAdaptiveClassifier ( TessdataManager * mgr )

This routine reads in the training information needed by the adaptive classifier and saves it into global variables. Parameters: load_pre_trained_templates Indicates whether the pre-trained templates (inttemp, normproto and pffmtable components) should be loaded. Should only be set to true if the necessary classifier components are present in the [lang].traineddata file. Globals: BuiltInTemplatesFile file to get built-in temps from BuiltInCutoffsFile file to get avg. feat per class from classify_use_pre_adapted_templates enables use of pre-adapted templates

◆ INT_VAR_H() [1/16]

tesseract::Classify::INT_VAR_H	(	tessedit_single_match	,
		FALSE	,
		"Top choice only from CP"
	)

◆ INT_VAR_H() [2/16]

tesseract::Classify::INT_VAR_H	(	classify_debug_level	,
		0	,
		"Classify debug level"
	)

◆ INT_VAR_H() [3/16]

tesseract::Classify::INT_VAR_H	(	classify_norm_method	,
		character	,
		"Normalization Method ..."
	)

◆ INT_VAR_H() [4/16]

tesseract::Classify::INT_VAR_H	(	matcher_debug_level	,
		0	,
		"Matcher Debug Level"
	)

◆ INT_VAR_H() [5/16]

tesseract::Classify::INT_VAR_H	(	matcher_debug_flags	,
		0	,
		"Matcher Debug Flags"
	)

◆ INT_VAR_H() [6/16]

tesseract::Classify::INT_VAR_H	(	classify_learning_debug_level	,
		0	,
		"Learning Debug Level: "
	)

◆ INT_VAR_H() [7/16]

tesseract::Classify::INT_VAR_H	(	matcher_permanent_classes_min	,
		1	,
		"Min # of permanent classes"
	)

◆ INT_VAR_H() [8/16]

tesseract::Classify::INT_VAR_H	(	matcher_min_examples_for_prototyping	,
		3	,
		"Reliable Config Threshold"
	)

◆ INT_VAR_H() [9/16]

tesseract::Classify::INT_VAR_H	(	matcher_sufficient_examples_for_prototyping	,
		5	,
		"Enable adaption even if the ambiguities have not been seen"
	)

◆ INT_VAR_H() [10/16]

tesseract::Classify::INT_VAR_H	(	classify_adapt_proto_threshold	,
		230	,
		"Threshold for good protos during adaptive 0-255"
	)

◆ INT_VAR_H() [11/16]

tesseract::Classify::INT_VAR_H	(	classify_adapt_feature_threshold	,
		230	,
		"Threshold for good features during adaptive 0-255"
	)

◆ INT_VAR_H() [12/16]

tesseract::Classify::INT_VAR_H	(	classify_class_pruner_threshold	,
		229	,
		"Class Pruner Threshold 0-255"
	)

◆ INT_VAR_H() [13/16]

tesseract::Classify::INT_VAR_H	(	classify_class_pruner_multiplier	,
		15	,
		"Class Pruner Multiplier 0-255: "
	)

◆ INT_VAR_H() [14/16]

tesseract::Classify::INT_VAR_H	(	classify_cp_cutoff_strength	,
		7	,
		"Class Pruner CutoffStrength: "
	)

◆ INT_VAR_H() [15/16]

tesseract::Classify::INT_VAR_H	(	classify_integer_matcher_multiplier	,
		10	,
		"Integer Matcher Multiplier 0-255: "
	)

◆ INT_VAR_H() [16/16]

tesseract::Classify::INT_VAR_H	(	il1_adaption_test	,
		0	,
		"Don't adapt to i/I at beginning of word"
	)

◆ LargeSpeckle()

bool tesseract::Classify::LargeSpeckle ( const TBLOB & blob )

◆ LearnBlob()

void tesseract::Classify::LearnBlob	(	const STRING &	fontname,
		TBLOB *	Blob,
		const DENORM &	cn_denorm,
		const INT_FX_RESULT_STRUCT &	fx_info,
		const char *	blob_text
	)

◆ LearnPieces()

void tesseract::Classify::LearnPieces	(	const char *	fontname,
		int	start,
		int	length,
		float	threshold,
		CharSegmentationType	segmentation,
		const char *	correct_text,
		WERD_RES *	word
	)

◆ LearnWord()

void tesseract::Classify::LearnWord	(	const char *	fontname,
		WERD_RES *	word
	)

◆ LooksLikeGarbage()

bool tesseract::Classify::LooksLikeGarbage ( TBLOB * blob )

◆ MakeNewTemporaryConfig()

int tesseract::Classify::MakeNewTemporaryConfig	(	ADAPT_TEMPLATES	Templates,
		CLASS_ID	ClassId,
		int	FontinfoId,
		int	NumFeatures,
		INT_FEATURE_ARRAY	Features,
		FEATURE_SET	FloatFeatures
	)

Parameters

Templates	adapted templates to add new config to
ClassId	class id to associate with new config
FontinfoId	font information inferred from pre-trained templates
NumFeatures	number of features in IntFeatures
Features	features describing model for new config
FloatFeatures	floating-pt representation of features

Returns: The id of the new config created, a negative integer in case of error.

◆ MakeNewTempProtos()

PROTO_ID tesseract::Classify::MakeNewTempProtos	(	FEATURE_SET	Features,
		int	NumBadFeat,
		FEATURE_ID	BadFeat[],
		INT_CLASS	IClass,
		ADAPT_CLASS	Class,
		BIT_VECTOR	TempProtoMask
	)

This routine finds sets of sequential bad features that all have the same angle and converts each set into a new temporary proto. The temp proto is added to the proto pruner for IClass, pushed onto the list of temp protos in Class, and added to TempProtoMask.

Parameters

Features	floating-pt features describing new character
NumBadFeat	number of bad features to turn into protos
BadFeat	feature id's of bad features
IClass	integer class templates to add new protos to
Class	adapted class templates to add new protos to
TempProtoMask	proto mask to add new protos to

Globals: none

Returns: Max proto id in class after all protos have been added.

◆ MakePermanent()

void tesseract::Classify::MakePermanent	(	ADAPT_TEMPLATES	Templates,
		CLASS_ID	ClassId,
		int	ConfigId,
		TBLOB *	Blob
	)

Parameters

Templates	current set of adaptive templates
ClassId	class containing config to be made permanent
ConfigId	config to be made permanent
Blob	current blob being adapted to

Globals: none

◆ MasterMatcher()

void tesseract::Classify::MasterMatcher	(	INT_TEMPLATES	templates,
		int16_t	num_features,
		const INT_FEATURE_STRUCT *	features,
		const uint8_t *	norm_factors,
		ADAPT_CLASS *	classes,
		int	debug,
		int	matcher_multiplier,
		const TBOX &	blob_box,
		const GenericVector< CP_RESULT_STRUCT > &	results,
		ADAPT_RESULTS *	final_results
	)

Factored-out calls to IntegerMatcher based on class pruner results. Returns integer matcher results inside CLASS_PRUNER_RESULTS structure.

◆ NewAdaptedTemplates()

ADAPT_TEMPLATES tesseract::Classify::NewAdaptedTemplates ( bool InitFromUnicharset )

Allocates memory for adapted tempates. each char in unicharset to the newly created templates

Parameters

InitFromUnicharset if true, add an empty class for

Returns: Ptr to new adapted templates.

Note: Globals: none

◆ NormalizeOutlines()

void tesseract::Classify::NormalizeOutlines	(	LIST	Outlines,
		float *	XScale,
		float *	YScale
	)

This routine normalizes every outline in Outlines according to the currently selected normalization method. It also returns the scale factors that it used to do this scaling. The scale factors returned represent the x and y sizes in the normalized coordinate system that correspond to 1 pixel in the original coordinate system.

Globals:

classify_norm_method method being used for normalization
classify_char_norm_range map radius of gyration to this value
Parameters

Outlines list of outlines to be normalized

XScale x-direction scale factor used by routine

YScale y-direction scale factor used by routine

Returns
none (Outlines are changed and XScale and YScale are updated)

◆ PrintAdaptedTemplates()

void tesseract::Classify::PrintAdaptedTemplates	(	FILE *	File,
		ADAPT_TEMPLATES	Templates
	)

This routine prints a summary of the adapted templates in Templates to File.

Parameters

File	open text file to print Templates to
Templates	adapted templates to print to File

Note: Globals: none

◆ PrintAdaptiveMatchResults()

void tesseract::Classify::PrintAdaptiveMatchResults ( const ADAPT_RESULTS & results )

This routine writes the matches in Results to File.

Parameters

results match results to write to File

Globals: none

◆ PruneClasses()

int tesseract::Classify::PruneClasses	(	const INT_TEMPLATES_STRUCT *	int_templates,
		int	num_features,
		int	keep_this,
		const INT_FEATURE_STRUCT *	features,
		const uint8_t *	normalization_factors,
		const uint16_t *	expected_num_features,
		GenericVector< CP_RESULT_STRUCT > *	results
	)

Runs the class pruner from int_templates on the given features, returning the number of classes output in results.

Parameters

int_templates	Class pruner tables
num_features	Number of features in blob
features	Array of features
normalization_factors	Array of fudge factors from blob normalization process (by CLASS_INDEX)
expected_num_features	Array of expected number of features for each class (by CLASS_INDEX)
results	Sorted Array of pruned classes. Must be an array of size at least int_templates->NumClasses.
keep_this

◆ ReadAdaptedTemplates()

ADAPT_TEMPLATES tesseract::Classify::ReadAdaptedTemplates ( TFile * fp )

Read a set of adapted templates from file and return a ptr to the templates.

Parameters

fp	open text file to read adapted templates from

Returns: Ptr to adapted templates read from file.

Note: Globals: none

◆ ReadIntTemplates()

INT_TEMPLATES tesseract::Classify::ReadIntTemplates ( TFile * fp )

This routine reads a set of integer templates from File. File must already be open and must be in the correct binary format.

Parameters

fp	open file to read templates from

Returns: Pointer to integer templates read from File.

Note: Globals: none

◆ ReadNewCutoffs()

void tesseract::Classify::ReadNewCutoffs	(	TFile *	fp,
		CLASS_CUTOFF_ARRAY	Cutoffs
	)

Open file, read in all of the class-id/cutoff pairs and insert them into the Cutoffs array. Cutoffs are indexed in the array by class id. Unused entries in the array are set to an arbitrarily high cutoff value.

Parameters

fp	file containing cutoff definitions
Cutoffs	array to put cutoffs into

Returns: none

Note: Globals: none

◆ ReadNormProtos()

NORM_PROTOS * tesseract::Classify::ReadNormProtos ( TFile * fp )

This routine allocates a new data structure to hold a set of character normalization protos. It then fills in the data structure by reading from the specified File.

Parameters

fp	open text file to read normalization protos from Globals: none

Returns: Character normalization protos.

◆ RefreshDebugWindow()

void tesseract::Classify::RefreshDebugWindow	(	ScrollView **	win,
		const char *	msg,
		int	y_offset,
		const TBOX &	wbox
	)

◆ RemoveBadMatches()

void tesseract::Classify::RemoveBadMatches ( ADAPT_RESULTS * Results )

This routine steps through each matching class in Results and removes it from the match list if its rating is worse than the BestRating plus a pad. In other words, all good matches get moved to the front of the classes array.

Parameters

Results contains matches to be filtered

Globals:

matcher_bad_match_pad defines a "bad match"

◆ RemoveExtraPuncs()

void tesseract::Classify::RemoveExtraPuncs ( ADAPT_RESULTS * Results )

This routine discards extra digits or punctuation from the results. We keep only the top 2 punctuation answers and the top 1 digit answer if present.

Parameters

Results contains matches to be filtered

◆ ResetAdaptiveClassifierInternal()

void tesseract::Classify::ResetAdaptiveClassifierInternal ( )

◆ SetAdaptiveThreshold()

void tesseract::Classify::SetAdaptiveThreshold ( float Threshold )

This routine resets the internal thresholds inside the integer matcher to correspond to the specified threshold.

Parameters

Threshold threshold for creating new templates

Globals:

matcher_good_threshold default good match rating

◆ SetStaticClassifier()

void tesseract::Classify::SetStaticClassifier ( ShapeClassifier * static_classifier )

◆ SettupPass1()

void tesseract::Classify::SettupPass1 ( )

This routine prepares the adaptive matcher for the start of the first pass. Learning is enabled (unless it is disabled for the whole program).

Note: this is somewhat redundant, it simply says that if learning is enabled then it will remain enabled on the first pass. If it is disabled, then it will remain disabled. This is only put here to make it very clear that learning is controlled directly by the global setting of EnableLearning.

Globals:

EnableLearning set to TRUE by this routine

◆ SettupPass2()

void tesseract::Classify::SettupPass2 ( )

This routine prepares the adaptive matcher for the start of the second pass. Further learning is disabled.

Globals:

EnableLearning set to FALSE by this routine

◆ SetupBLCNDenorms()

void tesseract::Classify::SetupBLCNDenorms	(	const TBLOB &	blob,
		bool	nonlinear_norm,
		DENORM *	bl_denorm,
		DENORM *	cn_denorm,
		INT_FX_RESULT_STRUCT *	fx_info
	)

static

◆ shape_table()

const ShapeTable* tesseract::Classify::shape_table ( ) const

inline

◆ ShapeIDToClassID()

int tesseract::Classify::ShapeIDToClassID ( int shape_id ) const

◆ ShowBestMatchFor()

void tesseract::Classify::ShowBestMatchFor	(	int	shape_id,
		const INT_FEATURE_STRUCT *	features,
		int	num_features
	)

This routine displays debug information for the best config of the given shape_id for the given set of features.

Parameters

shape_id	classifier id to work with
features	features of the unknown character
num_features	Number of features in the features array.

◆ ShowMatchDisplay()

void tesseract::Classify::ShowMatchDisplay ( )

This routine sends the shapes in the global display lists to the match debugger window.

Globals:

FeatureShapes display list containing feature matches
ProtoShapes display list containing proto matches
Returns
none

◆ StartBackupAdaptiveClassifier()

void tesseract::Classify::StartBackupAdaptiveClassifier ( )

◆ STRING_VAR_H()

tesseract::Classify::STRING_VAR_H	(	classify_learn_debug_str	,
		""	,
		"Class str to debug learning"
	)

◆ SwitchAdaptiveClassifier()

void tesseract::Classify::SwitchAdaptiveClassifier ( )

◆ TempConfigReliable()

bool tesseract::Classify::TempConfigReliable	(	CLASS_ID	class_id,
		const TEMP_CONFIG &	config
	)

◆ UpdateAmbigsGroup()

void tesseract::Classify::UpdateAmbigsGroup	(	CLASS_ID	class_id,
		TBLOB *	Blob
	)

◆ WriteAdaptedTemplates()

void tesseract::Classify::WriteAdaptedTemplates	(	FILE *	File,
		ADAPT_TEMPLATES	Templates
	)

This routine saves Templates to File in a binary format.

Parameters

File	open text file to write Templates to
Templates	set of adapted templates to write to File

Note: Globals: none

◆ WriteIntTemplates()

void tesseract::Classify::WriteIntTemplates	(	FILE *	File,
		INT_TEMPLATES	Templates,
		const UNICHARSET &	target_unicharset
	)

This routine writes Templates to File. The format is an efficient binary format. File must already be open for writing.

Parameters

File	open file to write templates to
Templates	templates to save into File
target_unicharset	the UNICHARSET to use

Returns: none

Note: Globals: none

◆ WriteTRFile()

bool tesseract::Classify::WriteTRFile ( const STRING & filename )

Member Data Documentation

◆ AdaptedTemplates

ADAPT_TEMPLATES tesseract::Classify::AdaptedTemplates

◆ AllConfigsOff

BIT_VECTOR tesseract::Classify::AllConfigsOff

◆ AllConfigsOn

BIT_VECTOR tesseract::Classify::AllConfigsOn

◆ AllProtosOn

BIT_VECTOR tesseract::Classify::AllProtosOn

◆ BackupAdaptedTemplates

ADAPT_TEMPLATES tesseract::Classify::BackupAdaptedTemplates

◆ BaselineCutoffs

uint16_t tesseract::Classify::BaselineCutoffs[MAX_NUM_CLASSES]

private

◆ CharNormCutoffs

uint16_t tesseract::Classify::CharNormCutoffs[MAX_NUM_CLASSES]

private

◆ dict_

Dict tesseract::Classify::dict_

private

◆ EnableLearning

bool tesseract::Classify::EnableLearning

◆ feature_defs_

FEATURE_DEFS_STRUCT tesseract::Classify::feature_defs_

protected

◆ fontinfo_table_

UnicityTable<FontInfo> tesseract::Classify::fontinfo_table_

◆ fontset_table_

UnicityTable<FontSet> tesseract::Classify::fontset_table_

◆ im_

IntegerMatcher tesseract::Classify::im_

protected

◆ learn_debug_win_

ScrollView* tesseract::Classify::learn_debug_win_

private

◆ learn_fragmented_word_debug_win_

ScrollView* tesseract::Classify::learn_fragmented_word_debug_win_

private

◆ learn_fragments_debug_win_

ScrollView* tesseract::Classify::learn_fragments_debug_win_

private

◆ NormProtos

NORM_PROTOS* tesseract::Classify::NormProtos

◆ NumAdaptationsFailed

int tesseract::Classify::NumAdaptationsFailed

private

◆ PreTrainedTemplates

INT_TEMPLATES tesseract::Classify::PreTrainedTemplates

◆ shape_table_

ShapeTable* tesseract::Classify::shape_table_

protected

◆ shapetable_cutoffs_

GenericVector<uint16_t> tesseract::Classify::shapetable_cutoffs_

private

◆ static_classifier_

ShapeClassifier* tesseract::Classify::static_classifier_

private

◆ TempProtoMask

BIT_VECTOR tesseract::Classify::TempProtoMask

◆ tr_file_data_

STRING tesseract::Classify::tr_file_data_

private

The documentation for this class was generated from the following files:

/home/stephane/src/tesseract/src/classify/classify.h
/home/stephane/src/tesseract/src/classify/adaptive.cpp
/home/stephane/src/tesseract/src/classify/adaptmatch.cpp
/home/stephane/src/tesseract/src/classify/blobclass.cpp
/home/stephane/src/tesseract/src/classify/classify.cpp
/home/stephane/src/tesseract/src/classify/cutoffs.cpp
/home/stephane/src/tesseract/src/classify/float2int.cpp
/home/stephane/src/tesseract/src/classify/intfx.cpp
/home/stephane/src/tesseract/src/classify/intmatcher.cpp
/home/stephane/src/tesseract/src/classify/intproto.cpp
/home/stephane/src/tesseract/src/classify/mfoutline.cpp
/home/stephane/src/tesseract/src/classify/normmatch.cpp
/home/stephane/src/tesseract/src/classify/outfeat.cpp
/home/stephane/src/tesseract/src/classify/picofeat.cpp

Outlines	list of outlines to be normalized
XScale	x-direction scale factor used by routine
YScale	y-direction scale factor used by routine

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Attributes

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ Classify()

◆ ~Classify()

Member Function Documentation

◆ AdaptableWord()

◆ AdaptiveClassifier()

◆ AdaptiveClassifierIsEmpty()

◆ AdaptiveClassifierIsFull()

◆ AdaptToChar()

◆ AddLargeSpeckleTo()

◆ AddNewResult()

◆ AmbigClassifier()

◆ BaselineClassifier()

◆ BOOL_VAR_H() [1/14]

◆ BOOL_VAR_H() [2/14]

◆ BOOL_VAR_H() [3/14]

◆ BOOL_VAR_H() [4/14]

◆ BOOL_VAR_H() [5/14]

◆ BOOL_VAR_H() [6/14]

◆ BOOL_VAR_H() [7/14]

◆ BOOL_VAR_H() [8/14]

◆ BOOL_VAR_H() [9/14]

◆ BOOL_VAR_H() [10/14]

◆ BOOL_VAR_H() [11/14]

◆ BOOL_VAR_H() [12/14]

◆ BOOL_VAR_H() [13/14]

◆ BOOL_VAR_H() [14/14]

◆ CharNormClassifier()

◆ CharNormTrainingSample()

◆ ClassAndConfigIDToFontOrShapeID()

◆ ClassIDToDebugStr()

◆ ClassifyAsNoise()

◆ ClearCharNormArray()

◆ ComputeCharNormArrays()

◆ ComputeCorrectedRating()

◆ ComputeIntCharNormArray()

◆ ComputeIntFeatures()

◆ ComputeNormMatch()

◆ ConvertMatchesToChoices()

◆ ConvertProto()

◆ CreateIntTemplates()

◆ DebugAdaptiveClassifier()

◆ DisplayAdaptedChar()

◆ DoAdaptiveMatch()

◆ double_VAR_H() [1/23]

◆ double_VAR_H() [2/23]

◆ double_VAR_H() [3/23]

◆ double_VAR_H() [4/23]

◆ double_VAR_H() [5/23]

◆ double_VAR_H() [6/23]

◆ double_VAR_H() [7/23]

◆ double_VAR_H() [8/23]

◆ double_VAR_H() [9/23]

◆ double_VAR_H() [10/23]

◆ double_VAR_H() [11/23]

◆ double_VAR_H() [12/23]

◆ double_VAR_H() [13/23]

◆ double_VAR_H() [14/23]

◆ double_VAR_H() [15/23]

◆ double_VAR_H() [16/23]

◆ double_VAR_H() [17/23]

◆ double_VAR_H() [18/23]

◆ double_VAR_H() [19/23]

◆ double_VAR_H() [20/23]

◆ double_VAR_H() [21/23]

◆ double_VAR_H() [22/23]

◆ double_VAR_H() [23/23]

◆ EndAdaptiveClassifier()

◆ ExpandShapesAndApplyCorrections()

◆ ExtractFeatures()

◆ ExtractIntCNFeatures()

◆ ExtractIntGeoFeatures()

◆ ExtractOutlineFeatures()

◆ ExtractPicoFeatures()