tesseract::ClassPruner Class Reference

Public Member Functions
	ClassPruner (int max_classes)
	~ClassPruner ()
void	ComputeScores (const INT_TEMPLATES_STRUCT *int_templates, int num_features, const INT_FEATURE_STRUCT *features)
void	AdjustForExpectedNumFeatures (const uint16_t *expected_num_features, int cutoff_strength)
void	DisableDisabledClasses (const UNICHARSET &unicharset)
void	DisableFragments (const UNICHARSET &unicharset)
void	NormalizeForXheight (int norm_multiplier, const uint8_t *normalization_factors)
void	NoNormalization ()
void	PruneAndSort (int pruning_factor, int keep_this, bool max_of_non_fragments, const UNICHARSET &unicharset)
void	DebugMatch (const Classify &classify, const INT_TEMPLATES_STRUCT *int_templates, const INT_FEATURE_STRUCT *features) const
void	SummarizeResult (const Classify &classify, const INT_TEMPLATES_STRUCT *int_templates, const uint16_t *expected_num_features, int norm_multiplier, const uint8_t *normalization_factors) const
int	SetupResults (GenericVector< CP_RESULT_STRUCT > *results) const

Private Attributes
int *	class_count_
int *	norm_count_
int *	sort_key_
int *	sort_index_
int	max_classes_
int	rounded_classes_
int	pruning_threshold_
int	num_features_
int	num_classes_

Constructor & Destructor Documentation

ClassPruner()

tesseract::ClassPruner::ClassPruner ( int  max_classes )

inline

~ClassPruner()

tesseract::ClassPruner::~ClassPruner ( )

inline

Member Function Documentation

AdjustForExpectedNumFeatures()

void tesseract::ClassPruner::AdjustForExpectedNumFeatures ( const uint16_t *  expected_num_features, int  cutoff_strength  )

inline

Adjusts the scores according to the number of expected features. Used in lieu of a constant bias, this penalizes classes that expect more features than there are present. Thus an actual c will score higher for c than e, even though almost all the features match e as well as c, because e expects more features to be present.

ComputeScores()

void tesseract::ClassPruner::ComputeScores ( const INT_TEMPLATES_STRUCT *  int_templates, int  num_features, const INT_FEATURE_STRUCT *  features  )

inline

Computes the scores for every class in the character set, by summing the weights for each feature and stores the sums internally in class_count_.

DebugMatch()

void tesseract::ClassPruner::DebugMatch ( const Classify &  classify, const INT_TEMPLATES_STRUCT *  int_templates, const INT_FEATURE_STRUCT *  features  ) const

inline

Prints debug info on the class pruner matches for the pruned classes only.

DisableDisabledClasses()

void tesseract::ClassPruner::DisableDisabledClasses ( const UNICHARSET &  unicharset )

inline

Zeros the scores for classes disabled in the unicharset. Implements the black-list to recognize a subset of the character set.

DisableFragments()

void tesseract::ClassPruner::DisableFragments ( const UNICHARSET &  unicharset )

inline

Zeros the scores of fragments.

NoNormalization()

void tesseract::ClassPruner::NoNormalization ( )

inline

The nop normalization copies the class_count_ array to norm_count_.

NormalizeForXheight()

void tesseract::ClassPruner::NormalizeForXheight ( int  norm_multiplier, const uint8_t *  normalization_factors  )

inline

Normalizes the counts for xheight, putting the normalized result in norm_count_. Applies a simple subtractive penalty for incorrect vertical position provided by the normalization_factors array, indexed by character class, and scaled by the norm_multiplier.

PruneAndSort()

void tesseract::ClassPruner::PruneAndSort ( int  pruning_factor, int  keep_this, bool  max_of_non_fragments, const UNICHARSET &  unicharset  )

inline

Prunes the classes using <the maximum count> * pruning_factor/256 as a threshold for keeping classes. If max_of_non_fragments, then ignore fragments in computing the maximum count.

SetupResults()

int tesseract::ClassPruner::SetupResults ( GenericVector< CP_RESULT_STRUCT > *  results ) const

inline

Copies the pruned, sorted classes into the output results and returns the number of classes.

SummarizeResult()

void tesseract::ClassPruner::SummarizeResult ( const Classify &  classify, const INT_TEMPLATES_STRUCT *  int_templates, const uint16_t *  expected_num_features, int  norm_multiplier, const uint8_t *  normalization_factors  ) const

inline

Prints a summary of the pruner result.

Member Data Documentation

class_count_

int* tesseract::ClassPruner::class_count_

private

Array[rounded_classes_] of initial counts for each class.

max_classes_

int tesseract::ClassPruner::max_classes_

private

Number of classes in this class pruner.

norm_count_

int* tesseract::ClassPruner::norm_count_

private

Array[rounded_classes_] of modified counts for each class after normalizing for expected number of features, disabled classes, fragments, and xheights.

num_classes_

int tesseract::ClassPruner::num_classes_

private

Final number of pruned classes.

num_features_

int tesseract::ClassPruner::num_features_

private

The number of features used to compute the scores.

pruning_threshold_

int tesseract::ClassPruner::pruning_threshold_

private

Threshold count applied to prune classes.

rounded_classes_

int tesseract::ClassPruner::rounded_classes_

private

Rounded up number of classes used for array sizes.

sort_index_

int* tesseract::ClassPruner::sort_index_

private

Array[rounded_classes_ +1] of classes corresponding to sort_key_.

sort_key_

int* tesseract::ClassPruner::sort_key_

private

Array[rounded_classes_ +1] of pruned counts that gets sorted

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

• /home/stephane/src/tesseract/src/classify/intmatcher.cpp