#include <wordrec.h>

Inheritance diagram for tesseract::Wordrec:

Public Member Functions

Wordrec ()

virtual ~Wordrec ()

void SaveAltChoices (const LIST &best_choices, WERD_RES *word)

void FillLattice (const MATRIX &ratings, const WERD_CHOICE_LIST &best_choices, const UNICHARSET &unicharset, BlamerBundle *blamer_bundle)

void CallFillLattice (const MATRIX &ratings, const WERD_CHOICE_LIST &best_choices, const UNICHARSET &unicharset, BlamerBundle *blamer_bundle)

void SegSearch (WERD_RES *word_res, BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle)

void WordSearch (WERD_RES *word_res)

void InitialSegSearch (WERD_RES *word_res, LMPainPoints *pain_points, GenericVector< SegSearchPending > *pending, BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle)

void DoSegSearch (WERD_RES *word_res)

SEAM * attempt_blob_chop (TWERD *word, TBLOB *blob, inT32 blob_number, bool italic_blob, const GenericVector< SEAM *> &seams)

SEAM * chop_numbered_blob (TWERD *word, inT32 blob_number, bool italic_blob, const GenericVector< SEAM *> &seams)

SEAM * chop_overlapping_blob (const GenericVector< TBOX > &boxes, bool italic_blob, WERD_RES *word_res, int *blob_number)

void add_seam_to_queue (float new_priority, SEAM *new_seam, SeamQueue *seams)

void choose_best_seam (SeamQueue *seam_queue, const SPLIT *split, PRIORITY priority, SEAM **seam_result, TBLOB *blob, SeamPile *seam_pile)

void combine_seam (const SeamPile &seam_pile, const SEAM *seam, SeamQueue *seam_queue)

SEAM * pick_good_seam (TBLOB *blob)

void try_point_pairs (EDGEPT *points[MAX_NUM_POINTS], inT16 num_points, SeamQueue *seam_queue, SeamPile *seam_pile, SEAM **seam, TBLOB *blob)

void try_vertical_splits (EDGEPT *points[MAX_NUM_POINTS], inT16 num_points, EDGEPT_CLIST *new_points, SeamQueue *seam_queue, SeamPile *seam_pile, SEAM **seam, TBLOB *blob)

PRIORITY grade_split_length (register SPLIT *split)

PRIORITY grade_sharpness (register SPLIT *split)

bool near_point (EDGEPT *point, EDGEPT *line_pt_0, EDGEPT *line_pt_1, EDGEPT **near_pt)

virtual BLOB_CHOICE_LIST * classify_piece (const GenericVector< SEAM *> &seams, inT16 start, inT16 end, const char *description, TWERD *word, BlamerBundle *blamer_bundle)

void merge_fragments (MATRIX *ratings, inT16 num_blobs)

void get_fragment_lists (inT16 current_frag, inT16 current_row, inT16 start, inT16 num_frag_parts, inT16 num_blobs, MATRIX *ratings, BLOB_CHOICE_LIST *choice_lists)

void merge_and_put_fragment_lists (inT16 row, inT16 column, inT16 num_frag_parts, BLOB_CHOICE_LIST *choice_lists, MATRIX *ratings)

void fill_filtered_fragment_list (BLOB_CHOICE_LIST *choices, int fragment_pos, int num_frag_parts, BLOB_CHOICE_LIST *filtered_choices)

program_editup

Initialize all the things in the program that need to be initialized. init_permute determines whether to initialize the permute functions and Dawg models.

void program_editup (const char *textbase, TessdataManager *init_classifier, TessdataManager *init_dict)

cc_recog

Recognize a word.

void cc_recog (WERD_RES *word)

program_editdown

This function holds any nessessary post processing for the Wise Owl program.

void program_editdown (inT32 elasped_time)

set_pass1

Get ready to do some pass 1 stuff.

void set_pass1 ()

set_pass2

Get ready to do some pass 2 stuff.

void set_pass2 ()

end_recog

Cleanup and exit the recog program.

int end_recog ()

call_matcher

Called from Tess with a blob in tess form. The blob may need rotating to the correct orientation for classification.

BLOB_CHOICE_LIST * call_matcher (TBLOB *blob)

dict_word()

Test the dictionaries, returning NO_PERM (0) if not found, or one of the PermuterType values if found, according to the dictionary.

int dict_word (const WERD_CHOICE &word)

classify_blob

Classify the this blob if it is not already recorded in the match table. Attempt to recognize this blob as a character. The recognition rating for this blob will be stored as a part of the blob. This value will also be returned to the caller.

Parameters

blob	Current blob
string	The string to display in ScrollView
color	The colour to use when displayed with ScrollView

BLOB_CHOICE_LIST * classify_blob (TBLOB *blob, const char *string, C_COL color, BlamerBundle *blamer_bundle)

point_priority

Assign a priority to and edge point that might be used as part of a split. The argument should be of type EDGEPT.

PRIORITY point_priority (EDGEPT *point)

add_point_to_list

Add an edge point to a POINT_GROUP containg a list of other points.

void add_point_to_list (PointHeap *point_heap, EDGEPT *point)

bool is_inside_angle (EDGEPT *pt)

angle_change

Return the change in angle (degrees) of the line segments between points one and two, and two and three.

int angle_change (EDGEPT *point1, EDGEPT *point2, EDGEPT *point3)

pick_close_point

Choose the edge point that is closest to the critical point. This point may not be exactly vertical from the critical point.

EDGEPT * pick_close_point (EDGEPT *critical_point, EDGEPT *vertical_point, int *best_dist)

prioritize_points

Find a list of edge points from the outer outline of this blob. For each of these points assign a priority. Sort these points using a heap structure so that they can be visited in order.

void prioritize_points (TESSLINE *outline, PointHeap *points)

new_min_point

Found a new minimum point try to decide whether to save it or not. Return the new value for the local minimum. If a point is saved then the local minimum is reset to NULL.

void new_min_point (EDGEPT *local_min, PointHeap *points)

new_max_point

Found a new minimum point try to decide whether to save it or not. Return the new value for the local minimum. If a point is saved then the local minimum is reset to NULL.

void new_max_point (EDGEPT *local_max, PointHeap *points)

vertical_projection_point

For one point on the outline, find the corresponding point on the other side of the outline that is a likely projection for a split point. This is done by iterating through the edge points until the X value of the point being looked at is greater than the X value of the split point. Ensure that the point being returned is not right next to the split point. Return the edge point in *best_point as a result, and any points that were newly created are also saved on the new_points list.

void vertical_projection_point (EDGEPT *split_point, EDGEPT *target_point, EDGEPT **best_point, EDGEPT_CLIST *new_points)

improve_one_blob

Finds the best place to chop, based on the worst blob, fixpt, or next to a fragment, according to the input. Returns the SEAM corresponding to the chop point, if any is found, and the index in the ratings_matrix of the chopped blob. Note that blob_choices is just a copy of the pointers in the leading diagonal of the ratings MATRIX. Although the blob is chopped, the returned SEAM is yet to be inserted into word->seam_array and the resulting blobs are unclassified, so this function can be used by ApplyBox as well as during recognition.

SEAM * improve_one_blob (const GenericVector< BLOB_CHOICE *> &blob_choices, DANGERR *fixpt, bool split_next_to_fragment, bool italic_blob, WERD_RES *word, int *blob_number)

chop_one_blob

Start with the current one-blob word and its classification. Find the worst blobs and try to divide it up to improve the ratings. Used for testing chopper.

SEAM * chop_one_blob (const GenericVector< TBOX > &boxes, const GenericVector< BLOB_CHOICE *> &blob_choices, WERD_RES *word_res, int *blob_number)

chop_word_main

Classify the blobs in this word and permute the results. Find the worst blob in the word and chop it up. Continue this process until a good answer has been found or all the blobs have been chopped up enough. The results are returned in the WERD_RES.

void chop_word_main (WERD_RES *word)

improve_by_chopping

Repeatedly chops the worst blob, classifying the new blobs fixing up all the data, and incrementally runs the segmentation search until a good word is found, or no more chops can be found.

void improve_by_chopping (float rating_cert_scale, WERD_RES *word, BestChoiceBundle *best_choice_bundle, BlamerBundle *blamer_bundle, LMPainPoints *pain_points, GenericVector< SegSearchPending > *pending)

int select_blob_to_split (const GenericVector< BLOB_CHOICE *> &blob_choices, float rating_ceiling, bool split_next_to_fragment)

int select_blob_to_split_from_fixpt (DANGERR *fixpt)

Public Member Functions inherited from tesseract::Classify

Classify ()

virtual ~Classify ()

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 ConfigId)

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, CLASS_CUTOFF_ARRAY Cutoffs)

void PrintAdaptedTemplates (FILE *File, ADAPT_TEMPLATES Templates)

void WriteAdaptedTemplates (FILE *File, ADAPT_TEMPLATES Templates)

ADAPT_TEMPLATES ReadAdaptedTemplates (TFile *File)

FLOAT32 ComputeNormMatch (CLASS_ID ClassId, const FEATURE_STRUCT &feature, BOOL8 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 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)

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)

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)

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 (FLOAT32 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, 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)

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

bool AdaptiveClassifierIsEmpty () const

bool LooksLikeGarbage (TBLOB *blob)

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

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)

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, FLOAT32 *XScale, FLOAT32 *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)

Public Member Functions inherited from tesseract::CCStruct

CCStruct ()

~CCStruct ()

Public Member Functions inherited from tesseract::CUtil

CUtil ()

~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 ()

Public Attributes
bool	merge_fragments_in_matrix = TRUE

bool	wordrec_no_block = FALSE

bool	wordrec_enable_assoc = TRUE

bool	force_word_assoc = FALSE

double	wordrec_worst_state = 1

bool	fragments_guide_chopper = FALSE

int	repair_unchopped_blobs = 1

double	tessedit_certainty_threshold = -2.25

int	chop_debug = 0

bool	chop_enable = 1

bool	chop_vertical_creep = 0

int	chop_split_length = 10000

int	chop_same_distance = 2

int	chop_min_outline_points = 6

int	chop_seam_pile_size = 150

bool	chop_new_seam_pile = 1

int	chop_inside_angle = -50

int	chop_min_outline_area = 2000

double	chop_split_dist_knob = 0.5

double	chop_overlap_knob = 0.9

double	chop_center_knob = 0.15

int	chop_centered_maxwidth = 90

double	chop_sharpness_knob = 0.06

double	chop_width_change_knob = 5.0

double	chop_ok_split = 100.0

double	chop_good_split = 50.0

int	chop_x_y_weight = 3

int	segment_adjust_debug = 0

bool	assume_fixed_pitch_char_segment = FALSE

int	wordrec_debug_level = 0

int	wordrec_max_join_chunks = 4

bool	wordrec_skip_no_truth_words = false

bool	wordrec_debug_blamer = false

bool	wordrec_run_blamer = false

int	segsearch_debug_level = 0

int	segsearch_max_pain_points = 2000

int	segsearch_max_futile_classifications = 10

double	segsearch_max_char_wh_ratio = 2.0

bool	save_alt_choices = true

LanguageModel *	language_model_

PRIORITY	pass2_ok_split

WERD_CHOICE *	prev_word_best_choice_

GenericVector< int >	blame_reasons_

void(Wordrec::*	fill_lattice_ )(const MATRIX &ratings, const WERD_CHOICE_LIST &best_choices, const UNICHARSET &unicharset, BlamerBundle *blamer_bundle)

Public Attributes inherited from tesseract::Classify
bool	allow_blob_division = true

bool	prioritize_division = FALSE

int	tessedit_single_match = FALSE

bool	classify_enable_learning = true

int	classify_debug_level = 0

int	classify_norm_method = character

double	classify_char_norm_range = 0.2

double	classify_min_norm_scale_x = 0.0

double	classify_max_norm_scale_x = 0.325

double	classify_min_norm_scale_y = 0.0

double	classify_max_norm_scale_y = 0.325

double	classify_max_rating_ratio = 1.5

double	classify_max_certainty_margin = 5.5

bool	tess_cn_matching = 0

bool	tess_bn_matching = 0

bool	classify_enable_adaptive_matcher = 1

bool	classify_use_pre_adapted_templates = 0

bool	classify_save_adapted_templates = 0

bool	classify_enable_adaptive_debugger = 0

bool	classify_nonlinear_norm = 0

int	matcher_debug_level = 0

int	matcher_debug_flags = 0

int	classify_learning_debug_level = 0

double	matcher_good_threshold = 0.125

double	matcher_reliable_adaptive_result = 0.0

double	matcher_perfect_threshold = 0.02

double	matcher_bad_match_pad = 0.15

double	matcher_rating_margin = 0.1

double	matcher_avg_noise_size = 12.0

int	matcher_permanent_classes_min = 1

int	matcher_min_examples_for_prototyping = 3

int	matcher_sufficient_examples_for_prototyping = 5

double	matcher_clustering_max_angle_delta = 0.015

double	classify_misfit_junk_penalty = 0.0

double	rating_scale = 1.5

double	certainty_scale = 20.0

double	tessedit_class_miss_scale = 0.00390625

double	classify_adapted_pruning_factor = 2.5

double	classify_adapted_pruning_threshold = -1.0

int	classify_adapt_proto_threshold = 230

int	classify_adapt_feature_threshold = 230

bool	disable_character_fragments = TRUE

double	classify_character_fragments_garbage_certainty_threshold = -3.0

bool	classify_debug_character_fragments = FALSE

bool	matcher_debug_separate_windows = FALSE

char *	classify_learn_debug_str = ""

int	classify_class_pruner_threshold = 229

int	classify_class_pruner_multiplier = 15

int	classify_cp_cutoff_strength = 7

int	classify_integer_matcher_multiplier = 10

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_

int	il1_adaption_test = 0

bool	classify_bln_numeric_mode = 0

double	speckle_large_max_size = 0.30

double	speckle_rating_penalty = 10.0

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

char *	m_data_sub_dir = "tessdata/"

int	ambigs_debug_level = 0

bool	use_definite_ambigs_for_classifier = 0

bool	use_ambigs_for_adaption = 0

Protected Member Functions
bool	SegSearchDone (int num_futile_classifications)

void	UpdateSegSearchNodes (float rating_cert_scale, int starting_col, GenericVector< SegSearchPending > pending, WERD_RES word_res, LMPainPoints pain_points, BestChoiceBundle best_choice_bundle, BlamerBundle *blamer_bundle)

void	ProcessSegSearchPainPoint (float pain_point_priority, const MATRIX_COORD &pain_point, const char pain_point_type, GenericVector< SegSearchPending > pending, WERD_RES word_res, LMPainPoints pain_points, BlamerBundle *blamer_bundle)

void	ResetNGramSearch (WERD_RES word_res, BestChoiceBundle best_choice_bundle, GenericVector< SegSearchPending > *pending)

void	InitBlamerForSegSearch (WERD_RES word_res, LMPainPoints pain_points, BlamerBundle blamer_bundle, STRING blamer_debug)

Additional Inherited Members
Static Public Member Functions inherited from tesseract::Classify
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)

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

Protected Attributes inherited from tesseract::Classify
IntegerMatcher	im_

FEATURE_DEFS_STRUCT	feature_defs_

ShapeTable *	shape_table_

Detailed Description

Definition at line 123 of file wordrec.h.

Constructor & Destructor Documentation

◆ Wordrec()

tesseract::Wordrec::Wordrec ( )

Definition at line 26 of file wordrec.cpp.

                  :
   // control parameters
   BOOL_MEMBER(merge_fragments_in_matrix, TRUE,
               "Merge the fragments in the ratings matrix and delete them"
               " after merging", params()),
   BOOL_MEMBER(wordrec_no_block, FALSE, "Don't output block information",
               params()),
   BOOL_MEMBER(wordrec_enable_assoc, TRUE, "Associator Enable",
               params()),
   BOOL_MEMBER(force_word_assoc, FALSE,
               "force associator to run regardless of what enable_assoc is."
               "This is used for CJK where component grouping is necessary.",
               CCUtil::params()),
   double_MEMBER(wordrec_worst_state, 1.0, "Worst segmentation state",
                 params()),
   BOOL_MEMBER(fragments_guide_chopper, FALSE,
               "Use information from fragments to guide chopping process",
               params()),
   INT_MEMBER(repair_unchopped_blobs, 1, "Fix blobs that aren't chopped",
              params()),
   double_MEMBER(tessedit_certainty_threshold, -2.25, "Good blob limit",
                 params()),
   INT_MEMBER(chop_debug, 0, "Chop debug",
              params()),
   BOOL_MEMBER(chop_enable, 1, "Chop enable",
               params()),
   BOOL_MEMBER(chop_vertical_creep, 0, "Vertical creep",
             params()),
   INT_MEMBER(chop_split_length, 10000, "Split Length",
              params()),
   INT_MEMBER(chop_same_distance, 2, "Same distance",
              params()),
   INT_MEMBER(chop_min_outline_points, 6, "Min Number of Points on Outline",
              params()),
   INT_MEMBER(chop_seam_pile_size, 150, "Max number of seams in seam_pile",
              params()),
   BOOL_MEMBER(chop_new_seam_pile, 1, "Use new seam_pile", params()),
   INT_MEMBER(chop_inside_angle, -50, "Min Inside Angle Bend",
              params()),
   INT_MEMBER(chop_min_outline_area, 2000, "Min Outline Area",
              params()),
   double_MEMBER(chop_split_dist_knob, 0.5, "Split length adjustment",
                 params()),
   double_MEMBER(chop_overlap_knob, 0.9, "Split overlap adjustment",
                 params()),
   double_MEMBER(chop_center_knob, 0.15, "Split center adjustment",
                 params()),
   INT_MEMBER(chop_centered_maxwidth, 90, "Width of (smaller) chopped blobs "
              "above which we don't care that a chop is not near the center.",
              params()),
   double_MEMBER(chop_sharpness_knob, 0.06, "Split sharpness adjustment",
                 params()),
   double_MEMBER(chop_width_change_knob, 5.0, "Width change adjustment",
                 params()),
   double_MEMBER(chop_ok_split, 100.0, "OK split limit",
                 params()),
   double_MEMBER(chop_good_split, 50.0, "Good split limit",
                 params()),
   INT_MEMBER(chop_x_y_weight, 3, "X / Y  length weight",
              params()),
   INT_MEMBER(segment_adjust_debug, 0, "Segmentation adjustment debug",
              params()),
   BOOL_MEMBER(assume_fixed_pitch_char_segment, FALSE,
               "include fixed-pitch heuristics in char segmentation",
               params()),
   INT_MEMBER(wordrec_debug_level, 0,
              "Debug level for wordrec", params()),
   INT_MEMBER(wordrec_max_join_chunks, 4,
              "Max number of broken pieces to associate", params()),
   BOOL_MEMBER(wordrec_skip_no_truth_words, false,
               "Only run OCR for words that had truth recorded in BlamerBundle",
               params()),
   BOOL_MEMBER(wordrec_debug_blamer, false,
               "Print blamer debug messages", params()),
   BOOL_MEMBER(wordrec_run_blamer, false,
               "Try to set the blame for errors", params()),
   INT_MEMBER(segsearch_debug_level, 0,
              "SegSearch debug level", params()),
   INT_MEMBER(segsearch_max_pain_points, 2000,
              "Maximum number of pain points stored in the queue",
              params()),
   INT_MEMBER(segsearch_max_futile_classifications, 20,
              "Maximum number of pain point classifications per chunk that"
              "did not result in finding a better word choice.",
              params()),
   double_MEMBER(segsearch_max_char_wh_ratio, 2.0,
                 "Maximum character width-to-height ratio", params()),
   BOOL_MEMBER(save_alt_choices, true,
               "Save alternative paths found during chopping"
               " and segmentation search",
               params()) {
   prev_word_best_choice_ = NULL;
   language_model_ = new LanguageModel(&get_fontinfo_table(),
                                       &(getDict()));
   fill_lattice_ = NULL;
 }

◆ ~Wordrec()

tesseract::Wordrec::~Wordrec ( )

virtual

Definition at line 123 of file wordrec.cpp.

                   {
   delete language_model_;
 }

Member Function Documentation

◆ add_point_to_list()

void tesseract::Wordrec::add_point_to_list	(	PointHeap *	point_heap,
		EDGEPT *	point
	)

Definition at line 64 of file chop.cpp.

                                                                     {
   if (point_heap->size() < MAX_NUM_POINTS - 2) {
     PointPair pair(point_priority(point), point);
     point_heap->Push(&pair);
   }
 
 #ifndef GRAPHICS_DISABLED
   if (chop_debug > 2)
     mark_outline(point);
 #endif
 }

◆ add_seam_to_queue()

void tesseract::Wordrec::add_seam_to_queue	(	float	new_priority,
		SEAM *	new_seam,
		SeamQueue *	seams
	)

Definition at line 63 of file findseam.cpp.

                                                   {
   if (new_seam == NULL) return;
   if (chop_debug) {
     tprintf("Pushing new seam with priority %g :", new_priority);
     new_seam->Print("seam: ");
   }
   if (seams->size() >= MAX_NUM_SEAMS) {
     SeamPair old_pair(0, NULL);
     if (seams->PopWorst(&old_pair) && old_pair.key() <= new_priority) {
       if (chop_debug) {
         tprintf("Old seam staying with priority %g\n", old_pair.key());
       }
       delete new_seam;
       seams->Push(&old_pair);
       return;
     } else if (chop_debug) {
       tprintf("New seam with priority %g beats old worst seam with %g\n",
               new_priority, old_pair.key());
     }
   }
   SeamPair new_pair(new_priority, new_seam);
   seams->Push(&new_pair);
 }

◆ angle_change()

int tesseract::Wordrec::angle_change	(	EDGEPT *	point1,
		EDGEPT *	point2,
		EDGEPT *	point3
	)

Definition at line 88 of file chop.cpp.

                                                                         {
   VECTOR vector1;
   VECTOR vector2;
 
   int angle;
   float length;
 
   /* Compute angle */
   vector1.x = point2->pos.x - point1->pos.x;
   vector1.y = point2->pos.y - point1->pos.y;
   vector2.x = point3->pos.x - point2->pos.x;
   vector2.y = point3->pos.y - point2->pos.y;
   /* Use cross product */
   length = (float)sqrt((float)LENGTH(vector1) * LENGTH(vector2));
   if ((int) length == 0)
     return (0);
   angle = static_cast<int>(floor(asin(CROSS (vector1, vector2) /
                                       length) / PI * 180.0 + 0.5));
 
   /* Use dot product */
   if (SCALAR (vector1, vector2) < 0)
     angle = 180 - angle;
   /* Adjust angle */
   if (angle > 180)
     angle -= 360;
   if (angle <= -180)
     angle += 360;
   return (angle);
 }

◆ attempt_blob_chop()

SEAM * tesseract::Wordrec::attempt_blob_chop	(	TWERD *	word,
		TBLOB *	blob,
		inT32	blob_number,
		bool	italic_blob,
		const GenericVector< SEAM *> &	seams
	)

Definition at line 169 of file chopper.cpp.

                                                                     {
   if (repair_unchopped_blobs)
     preserve_outline_tree (blob->outlines);
   TBLOB *other_blob = TBLOB::ShallowCopy(*blob);       /* Make new blob */
   // Insert it into the word.
   word->blobs.insert(other_blob, blob_number + 1);
 
   SEAM *seam = NULL;
   if (prioritize_division) {
     TPOINT location;
     if (divisible_blob(blob, italic_blob, &location)) {
       seam = new SEAM(0.0f, location);
     }
   }
   if (seam == NULL)
     seam = pick_good_seam(blob);
   if (chop_debug) {
     if (seam != NULL)
       seam->Print("Good seam picked=");
     else
       tprintf("\n** no seam picked *** \n");
   }
   if (seam) {
     seam->ApplySeam(italic_blob, blob, other_blob);
   }
 
   seam = CheckSeam(chop_debug, blob_number, word, blob, other_blob,
                    seams, seam);
   if (seam == NULL) {
     if (repair_unchopped_blobs)
       restore_outline_tree(blob->outlines);
     if (allow_blob_division && !prioritize_division) {
       // If the blob can simply be divided into outlines, then do that.
       TPOINT location;
       if (divisible_blob(blob, italic_blob, &location)) {
         other_blob = TBLOB::ShallowCopy(*blob);       /* Make new blob */
         word->blobs.insert(other_blob, blob_number + 1);
         seam = new SEAM(0.0f, location);
         seam->ApplySeam(italic_blob, blob, other_blob);
         seam = CheckSeam(chop_debug, blob_number, word, blob, other_blob,
                          seams, seam);
       }
     }
   }
   if (seam != NULL) {
     // Make sure this seam doesn't get chopped again.
     seam->Finalize();
   }
   return seam;
 }

◆ call_matcher()

BLOB_CHOICE_LIST * tesseract::Wordrec::call_matcher ( TBLOB * blob )

Definition at line 138 of file tface.cpp.

                                                        {
   // Rotate the blob for classification if necessary.
   TBLOB* rotated_blob = tessblob->ClassifyNormalizeIfNeeded();
   if (rotated_blob == NULL) {
     rotated_blob = tessblob;
   }
   BLOB_CHOICE_LIST *ratings = new BLOB_CHOICE_LIST();  // matcher result
   AdaptiveClassifier(rotated_blob, ratings);
   if (rotated_blob != tessblob) {
     delete rotated_blob;
   }
   return ratings;
 }

◆ CallFillLattice()

void tesseract::Wordrec::CallFillLattice	(	const MATRIX &	ratings,
		const WERD_CHOICE_LIST &	best_choices,
		const UNICHARSET &	unicharset,
		BlamerBundle *	blamer_bundle
	)

inline

Definition at line 195 of file wordrec.h.

                                                     {
     (this->*fill_lattice_)(ratings, best_choices, unicharset, blamer_bundle);
   }

◆ cc_recog()

void tesseract::Wordrec::cc_recog ( WERD_RES * word )

Definition at line 113 of file tface.cpp.

                                      {
   getDict().reset_hyphen_vars(word->word->flag(W_EOL));
   chop_word_main(word);
   word->DebugWordChoices(getDict().stopper_debug_level >= 1,
                          getDict().word_to_debug.string());
   ASSERT_HOST(word->StatesAllValid());
 }

◆ choose_best_seam()

void tesseract::Wordrec::choose_best_seam	(	SeamQueue *	seam_queue,
		const SPLIT *	split,
		PRIORITY	priority,
		SEAM **	seam_result,
		TBLOB *	blob,
		SeamPile *	seam_pile
	)

Definition at line 102 of file findseam.cpp.

                                                                  {
   SEAM *seam;
   char str[80];
   float my_priority;
   /* Add seam of split */
   my_priority = priority;
   if (split != NULL) {
     TPOINT split_point = split->point1->pos;
     split_point += split->point2->pos;
     split_point /= 2;
     seam = new SEAM(my_priority, split_point, *split);
     if (chop_debug > 1) seam->Print("Partial priority    ");
     add_seam_to_queue(my_priority, seam, seam_queue);
 
     if (my_priority > chop_good_split)
       return;
   }
 
   TBOX bbox = blob->bounding_box();
   /* Queue loop */
   while (!seam_queue->empty()) {
     SeamPair seam_pair;
     seam_queue->Pop(&seam_pair);
     seam = seam_pair.extract_data();
     /* Set full priority */
     my_priority = seam->FullPriority(bbox.left(), bbox.right(),
                                      chop_overlap_knob, chop_centered_maxwidth,
                                      chop_center_knob, chop_width_change_knob);
     if (chop_debug) {
       sprintf (str, "Full my_priority %0.0f,  ", my_priority);
       seam->Print(str);
     }
 
     if ((*seam_result == NULL || (*seam_result)->priority() > my_priority) &&
         my_priority < chop_ok_split) {
       /* No crossing */
       if (seam->IsHealthy(*blob, chop_min_outline_points,
                           chop_min_outline_area)) {
         delete *seam_result;
         *seam_result = new SEAM(*seam);
         (*seam_result)->set_priority(my_priority);
       } else {
         delete seam;
         seam = NULL;
         my_priority = BAD_PRIORITY;
       }
     }
 
     if (my_priority < chop_good_split) {
       if (seam)
         delete seam;
       return;                    /* Made good answer */
     }
 
     if (seam) {
       /* Combine with others */
       if (seam_pile->size() < chop_seam_pile_size) {
         combine_seam(*seam_pile, seam, seam_queue);
         SeamDecPair pair(seam_pair.key(), seam);
         seam_pile->Push(&pair);
       } else if (chop_new_seam_pile &&
                  seam_pile->size() == chop_seam_pile_size &&
                  seam_pile->PeekTop().key() > seam_pair.key()) {
         combine_seam(*seam_pile, seam, seam_queue);
         SeamDecPair pair;
         seam_pile->Pop(&pair);  // pop the worst.
         // Replace the seam in pair (deleting the old one) with
         // the new seam and score, then push back into the heap.
         pair.set_key(seam_pair.key());
         pair.set_data(seam);
         seam_pile->Push(&pair);
       } else {
         delete seam;
       }
     }
 
     my_priority = seam_queue->empty() ? NO_FULL_PRIORITY
                                       : seam_queue->PeekTop().key();
     if ((my_priority > chop_ok_split) ||
       (my_priority > chop_good_split && split))
       return;
   }
 }

◆ chop_numbered_blob()

SEAM * tesseract::Wordrec::chop_numbered_blob	(	TWERD *	word,
		inT32	blob_number,
		bool	italic_blob,
		const GenericVector< SEAM *> &	seams
	)

Definition at line 223 of file chopper.cpp.

                                                                      {
   return attempt_blob_chop(word, word->blobs[blob_number], blob_number,
                            italic_blob, seams);
 }

◆ chop_one_blob()

SEAM * tesseract::Wordrec::chop_one_blob	(	const GenericVector< TBOX > &	boxes,
		const GenericVector< BLOB_CHOICE *> &	blob_choices,
		WERD_RES *	word_res,
		int *	blob_number
	)

Definition at line 373 of file chopper.cpp.

                                                {
   if (prioritize_division) {
     return chop_overlapping_blob(boxes, true, word_res, blob_number);
   } else {
     return improve_one_blob(blob_choices, NULL, false, true, word_res,
                             blob_number);
   }
 }

◆ chop_overlapping_blob()

SEAM * tesseract::Wordrec::chop_overlapping_blob	(	const GenericVector< TBOX > &	boxes,
		bool	italic_blob,
		WERD_RES *	word_res,
		int *	blob_number
	)

Definition at line 231 of file chopper.cpp.

                                                        {
   TWERD *word = word_res->chopped_word;
   for (*blob_number = 0; *blob_number < word->NumBlobs(); ++*blob_number) {
     TBLOB *blob = word->blobs[*blob_number];
     TPOINT topleft, botright;
     topleft.x = blob->bounding_box().left();
     topleft.y = blob->bounding_box().top();
     botright.x = blob->bounding_box().right();
     botright.y = blob->bounding_box().bottom();
 
     TPOINT original_topleft, original_botright;
     word_res->denorm.DenormTransform(NULL, topleft, &original_topleft);
     word_res->denorm.DenormTransform(NULL, botright, &original_botright);
 
     TBOX original_box = TBOX(original_topleft.x, original_botright.y,
                              original_botright.x, original_topleft.y);
 
     bool almost_equal_box = false;
     int num_overlap = 0;
     for (int i = 0; i < boxes.size(); i++) {
       if (original_box.overlap_fraction(boxes[i]) > 0.125)
         num_overlap++;
       if (original_box.almost_equal(boxes[i], 3))
         almost_equal_box = true;
     }
 
     TPOINT location;
     if (divisible_blob(blob, italic_blob, &location) ||
         (!almost_equal_box && num_overlap > 1)) {
       SEAM *seam = attempt_blob_chop(word, blob, *blob_number,
                                      italic_blob, word_res->seam_array);
       if (seam != NULL)
         return seam;
     }
   }
 
   *blob_number = -1;
   return NULL;
 }

◆ chop_word_main()

void tesseract::Wordrec::chop_word_main ( WERD_RES * word )

Definition at line 393 of file chopper.cpp.

                                            {
   int num_blobs = word->chopped_word->NumBlobs();
   if (word->ratings == NULL) {
     word->ratings = new MATRIX(num_blobs, wordrec_max_join_chunks);
   }
   if (word->ratings->get(0, 0) == NULL) {
     // Run initial classification.
     for (int b = 0; b < num_blobs; ++b) {
       BLOB_CHOICE_LIST* choices = classify_piece(word->seam_array, b, b,
                                                  "Initial:", word->chopped_word,
                                                  word->blamer_bundle);
       word->ratings->put(b, b, choices);
     }
   } else {
     // Blobs have been pre-classified. Set matrix cell for all blob choices
     for (int col = 0; col < word->ratings->dimension(); ++col) {
       for (int row = col; row < word->ratings->dimension() &&
            row < col + word->ratings->bandwidth(); ++row) {
         BLOB_CHOICE_LIST* choices = word->ratings->get(col, row);
         if (choices != NULL) {
           BLOB_CHOICE_IT bc_it(choices);
           for (bc_it.mark_cycle_pt(); !bc_it.cycled_list(); bc_it.forward()) {
             bc_it.data()->set_matrix_cell(col, row);
           }
         }
       }
     }
   }
 
   // Run Segmentation Search.
   BestChoiceBundle best_choice_bundle(word->ratings->dimension());
   SegSearch(word, &best_choice_bundle, word->blamer_bundle);
 
   if (word->best_choice == NULL) {
     // SegSearch found no valid paths, so just use the leading diagonal.
     word->FakeWordFromRatings(TOP_CHOICE_PERM);
   }
   word->RebuildBestState();
   // If we finished without a hyphen at the end of the word, let the next word
   // be found in the dictionary.
   if (word->word->flag(W_EOL) &&
       !getDict().has_hyphen_end(*word->best_choice)) {
     getDict().reset_hyphen_vars(true);
   }
 
   if (word->blamer_bundle != NULL && this->fill_lattice_ != NULL) {
     CallFillLattice(*word->ratings, word->best_choices,
                     *word->uch_set, word->blamer_bundle);
   }
   if (wordrec_debug_level > 0) {
     tprintf("Final Ratings Matrix:\n");
     word->ratings->print(getDict().getUnicharset());
   }
   word->FilterWordChoices(getDict().stopper_debug_level);
 }

◆ classify_blob()

BLOB_CHOICE_LIST * tesseract::Wordrec::classify_blob	(	TBLOB *	blob,
		const char *	string,
		C_COL	color,
		BlamerBundle *	blamer_bundle
	)

Definition at line 56 of file wordclass.cpp.

                                                                       {
 #ifndef GRAPHICS_DISABLED
   if (wordrec_display_all_blobs)
     display_blob(blob, color);
 #endif
   // TODO(rays) collapse with call_matcher and move all to wordrec.cpp.
   BLOB_CHOICE_LIST* choices = call_matcher(blob);
   // If a blob with the same bounding box as one of the truth character
   // bounding boxes is not classified as the corresponding truth character
   // blame character classifier for incorrect answer.
   if (blamer_bundle != NULL) {
     blamer_bundle->BlameClassifier(getDict().getUnicharset(),
                                    blob->bounding_box(),
                                    *choices,
                                    wordrec_debug_blamer);
   }
   #ifndef GRAPHICS_DISABLED
   if (classify_debug_level && string)
     print_ratings_list(string, choices, getDict().getUnicharset());
 
   if (wordrec_blob_pause)
     window_wait(blob_window);
 #endif
 
   return choices;
 }

◆ classify_piece()

BLOB_CHOICE_LIST * tesseract::Wordrec::classify_piece	(	const GenericVector< SEAM *> &	seams,
		inT16	start,
		inT16	end,
		const char *	description,
		TWERD *	word,
		BlamerBundle *	blamer_bundle
	)

virtual

Definition at line 56 of file pieces.cpp.

                                                                        {
   if (end > start) SEAM::JoinPieces(seams, word->blobs, start, end);
   BLOB_CHOICE_LIST *choices = classify_blob(word->blobs[start], description,
                                             White, blamer_bundle);
   // Set the matrix_cell_ entries in all the BLOB_CHOICES.
   BLOB_CHOICE_IT bc_it(choices);
   for (bc_it.mark_cycle_pt(); !bc_it.cycled_list(); bc_it.forward()) {
     bc_it.data()->set_matrix_cell(start, end);
   }
 
   if (end > start) SEAM::BreakPieces(seams, word->blobs, start, end);
 
   return (choices);
 }

◆ combine_seam()

void tesseract::Wordrec::combine_seam	(	const SeamPile &	seam_pile,
		const SEAM *	seam,
		SeamQueue *	seam_queue
	)

Definition at line 196 of file findseam.cpp.

                                                                     {
   for (int x = 0; x < seam_pile.size(); ++x) {
     const SEAM *this_one = seam_pile.get(x).data();
     if (seam->CombineableWith(*this_one, SPLIT_CLOSENESS, chop_ok_split)) {
       SEAM *new_one = new SEAM(*seam);
       new_one->CombineWith(*this_one);
       if (chop_debug > 1) new_one->Print("Combo priority       ");
       add_seam_to_queue(new_one->priority(), new_one, seam_queue);
     }
   }
 }

◆ dict_word()

int tesseract::Wordrec::dict_word ( const WERD_CHOICE & word )

Definition at line 128 of file tface.cpp.

                                               {
   return getDict().valid_word(word);
 }

◆ DoSegSearch()

void tesseract::Wordrec::DoSegSearch ( WERD_RES * word_res )

Definition at line 31 of file segsearch.cpp.

                                             {
   BestChoiceBundle best_choice_bundle(word_res->ratings->dimension());
   // Run Segmentation Search.
   SegSearch(word_res, &best_choice_bundle, NULL);
 }

◆ end_recog()

int tesseract::Wordrec::end_recog ( )

Definition at line 65 of file tface.cpp.

                        {
   program_editdown (0);
 
   return (0);
 }

◆ fill_filtered_fragment_list()

void tesseract::Wordrec::fill_filtered_fragment_list	(	BLOB_CHOICE_LIST *	choices,
		int	fragment_pos,
		int	num_frag_parts,
		BLOB_CHOICE_LIST *	filtered_choices
	)

Definition at line 105 of file pieces.cpp.

                                                                               {
   BLOB_CHOICE_IT filtered_choices_it(filtered_choices);
   BLOB_CHOICE_IT choices_it(choices);
 
   for (choices_it.mark_cycle_pt(); !choices_it.cycled_list();
        choices_it.forward()) {
     UNICHAR_ID choice_unichar_id = choices_it.data()->unichar_id();
     const CHAR_FRAGMENT *frag = unicharset.get_fragment(choice_unichar_id);
 
     if (frag != NULL && frag->get_pos() == fragment_pos &&
         frag->get_total() == num_frag_parts) {
       // Recover the unichar_id of the unichar that this fragment is
       // a part of
       BLOB_CHOICE *b = new BLOB_CHOICE(*choices_it.data());
       int original_unichar = unicharset.unichar_to_id(frag->get_unichar());
       b->set_unichar_id(original_unichar);
       filtered_choices_it.add_to_end(b);
     }
   }
 
   filtered_choices->sort(SortByUnicharID<BLOB_CHOICE>);
 }

◆ FillLattice()

void tesseract::Wordrec::FillLattice	(	const MATRIX &	ratings,
		const WERD_CHOICE_LIST &	best_choices,
		const UNICHARSET &	unicharset,
		BlamerBundle *	blamer_bundle
	)

◆ get_fragment_lists()

void tesseract::Wordrec::get_fragment_lists	(	inT16	current_frag,
		inT16	current_row,
		inT16	start,
		inT16	num_frag_parts,
		inT16	num_blobs,
		MATRIX *	ratings,
		BLOB_CHOICE_LIST *	choice_lists
	)

Definition at line 281 of file pieces.cpp.

                                                                  {
   if (current_frag == num_frag_parts) {
     merge_and_put_fragment_lists(start, current_row - 1, num_frag_parts,
                                  choice_lists, ratings);
     return;
   }
 
   for (inT16 x = current_row; x < num_blobs; x++) {
     BLOB_CHOICE_LIST *choices = ratings->get(current_row, x);
     if (choices == NULL)
       continue;
 
     fill_filtered_fragment_list(choices, current_frag, num_frag_parts,
                                 &choice_lists[current_frag]);
     if (!choice_lists[current_frag].empty()) {
       get_fragment_lists(current_frag + 1, x + 1, start, num_frag_parts,
                          num_blobs, ratings, choice_lists);
       choice_lists[current_frag].clear();
     }
   }
 }

◆ grade_sharpness()

PRIORITY tesseract::Wordrec::grade_sharpness ( register SPLIT * split )

Definition at line 74 of file gradechop.cpp.

                                                        {
   PRIORITY grade;
 
   grade = point_priority (split->point1) + point_priority (split->point2);
 
   if (grade < -360.0)
     grade = 0;
   else
     grade += 360.0;
 
   grade *= chop_sharpness_knob;       /* Values 0 to -360 */
 
   return (grade);
 }

◆ grade_split_length()

PRIORITY tesseract::Wordrec::grade_split_length ( register SPLIT * split )

Definition at line 51 of file gradechop.cpp.

                                                           {
   PRIORITY grade;
   float split_length;
 
   split_length =
       split->point1->WeightedDistance(*split->point2, chop_x_y_weight);
 
   if (split_length <= 0)
     grade = 0;
   else
     grade = sqrt (split_length) * chop_split_dist_knob;
 
   return (MAX (0.0, grade));
 }

◆ improve_by_chopping()

void tesseract::Wordrec::improve_by_chopping	(	float	rating_cert_scale,
		WERD_RES *	word,
		BestChoiceBundle *	best_choice_bundle,
		BlamerBundle *	blamer_bundle,
		LMPainPoints *	pain_points,
		GenericVector< SegSearchPending > *	pending
	)

Definition at line 456 of file chopper.cpp.

                                                                             {
   int blob_number;
   do {  // improvement loop.
     // Make a simple vector of BLOB_CHOICEs to make it easy to pick which
     // one to chop.
     GenericVector<BLOB_CHOICE*> blob_choices;
     int num_blobs = word->ratings->dimension();
     for (int i = 0; i < num_blobs; ++i) {
       BLOB_CHOICE_LIST* choices = word->ratings->get(i, i);
       if (choices == NULL || choices->empty()) {
         blob_choices.push_back(NULL);
       } else {
         BLOB_CHOICE_IT bc_it(choices);
         blob_choices.push_back(bc_it.data());
       }
     }
     SEAM* seam = improve_one_blob(blob_choices, &best_choice_bundle->fixpt,
                                   false, false, word, &blob_number);
     if (seam == NULL) break;
     // A chop has been made. We have to correct all the data structures to
     // take into account the extra bottom-level blob.
     // Put the seam into the seam_array and correct everything else on the
     // word: ratings matrix (including matrix location in the BLOB_CHOICES),
     // states in WERD_CHOICEs, and blob widths.
     word->InsertSeam(blob_number, seam);
     // Insert a new entry in the beam array.
     best_choice_bundle->beam.insert(new LanguageModelState, blob_number);
     // Fixpts are outdated, but will get recalculated.
     best_choice_bundle->fixpt.clear();
     // Remap existing pain points.
     pain_points->RemapForSplit(blob_number);
     // Insert a new pending at the chop point.
     pending->insert(SegSearchPending(), blob_number);
 
     // Classify the two newly created blobs using ProcessSegSearchPainPoint,
     // as that updates the pending correctly and adds new pain points.
     MATRIX_COORD pain_point(blob_number, blob_number);
     ProcessSegSearchPainPoint(0.0f, pain_point, "Chop1", pending, word,
                               pain_points, blamer_bundle);
     pain_point.col = blob_number + 1;
     pain_point.row = blob_number + 1;
     ProcessSegSearchPainPoint(0.0f, pain_point, "Chop2", pending, word,
                               pain_points, blamer_bundle);
     if (language_model_->language_model_ngram_on) {
       // N-gram evaluation depends on the number of blobs in a chunk, so we
       // have to re-evaluate everything in the word.
       ResetNGramSearch(word, best_choice_bundle, pending);
       blob_number = 0;
     }
     // Run language model incrementally. (Except with the n-gram model on.)
     UpdateSegSearchNodes(rating_cert_scale, blob_number, pending,
                          word, pain_points, best_choice_bundle, blamer_bundle);
   } while (!language_model_->AcceptableChoiceFound() &&
            word->ratings->dimension() < kMaxNumChunks);
 
   // If after running only the chopper best_choice is incorrect and no blame
   // has been yet set, blame the classifier if best_choice is classifier's
   // top choice and is a dictionary word (i.e. language model could not have
   // helped). Otherwise blame the tradeoff between the classifier and
   // the old language model (permuters).
   if (word->blamer_bundle != NULL &&
       word->blamer_bundle->incorrect_result_reason() == IRR_CORRECT &&
       !word->blamer_bundle->ChoiceIsCorrect(word->best_choice)) {
     bool valid_permuter = word->best_choice != NULL &&
         Dict::valid_word_permuter(word->best_choice->permuter(), false);
     word->blamer_bundle->BlameClassifierOrLangModel(word,
                                                     getDict().getUnicharset(),
                                                     valid_permuter,
                                                     wordrec_debug_blamer);
   }
 }

◆ improve_one_blob()

SEAM * tesseract::Wordrec::improve_one_blob	(	const GenericVector< BLOB_CHOICE *> &	blob_choices,
		DANGERR *	fixpt,
		bool	split_next_to_fragment,
		bool	italic_blob,
		WERD_RES *	word,
		int *	blob_number
	)

Definition at line 329 of file chopper.cpp.

                                                   {
   float rating_ceiling = MAX_FLOAT32;
   SEAM *seam = NULL;
   do {
     *blob_number = select_blob_to_split_from_fixpt(fixpt);
     if (chop_debug) tprintf("blob_number from fixpt = %d\n", *blob_number);
     bool split_point_from_dict = (*blob_number != -1);
     if (split_point_from_dict) {
       fixpt->clear();
     } else {
       *blob_number = select_blob_to_split(blob_choices, rating_ceiling,
                                           split_next_to_fragment);
     }
     if (chop_debug) tprintf("blob_number = %d\n", *blob_number);
     if (*blob_number == -1)
       return NULL;
 
     // TODO(rays) it may eventually help to allow italic_blob to be true,
     seam = chop_numbered_blob(word->chopped_word, *blob_number, italic_blob,
                               word->seam_array);
     if (seam != NULL)
       return seam;  // Success!
     if (blob_choices[*blob_number] == NULL)
       return NULL;
     if (!split_point_from_dict) {
       // We chopped the worst rated blob, try something else next time.
       rating_ceiling = blob_choices[*blob_number]->rating();
     }
   } while (true);
   return seam;
 }

◆ InitBlamerForSegSearch()

void tesseract::Wordrec::InitBlamerForSegSearch	(	WERD_RES *	word_res,
		LMPainPoints *	pain_points,
		BlamerBundle *	blamer_bundle,
		STRING *	blamer_debug
	)

protected

Definition at line 342 of file segsearch.cpp.

                                                            {
   pain_points->Clear();  // Clear pain points heap.
   TessResultCallback2<bool, int, int>* pp_cb = NewPermanentTessCallback(
       pain_points, &LMPainPoints::GenerateForBlamer,
       static_cast<double>(segsearch_max_char_wh_ratio), word_res);
   blamer_bundle->InitForSegSearch(word_res->best_choice, word_res->ratings,
                                   getDict().WildcardID(), wordrec_debug_blamer,
                                   blamer_debug, pp_cb);
   delete pp_cb;
 }

◆ InitialSegSearch()

void tesseract::Wordrec::InitialSegSearch	(	WERD_RES *	word_res,
		LMPainPoints *	pain_points,
		GenericVector< SegSearchPending > *	pending,
		BestChoiceBundle *	best_choice_bundle,
		BlamerBundle *	blamer_bundle
	)

Definition at line 150 of file segsearch.cpp.

                                                             {
   if (segsearch_debug_level > 0) {
     tprintf("Starting SegSearch on ratings matrix%s:\n",
             wordrec_enable_assoc ? " (with assoc)" : "");
     word_res->ratings->print(getDict().getUnicharset());
   }
 
   pain_points->GenerateInitial(word_res);
 
   // Compute scaling factor that will help us recover blob outline length
   // from classifier rating and certainty for the blob.
   float rating_cert_scale = -1.0 * getDict().certainty_scale / rating_scale;
 
   language_model_->InitForWord(prev_word_best_choice_,
                                assume_fixed_pitch_char_segment,
                                segsearch_max_char_wh_ratio, rating_cert_scale);
 
   // Initialize blamer-related information: map character boxes recorded in
   // blamer_bundle->norm_truth_word to the corresponding i,j indices in the
   // ratings matrix. We expect this step to succeed, since when running the
   // chopper we checked that the correct chops are present.
   if (blamer_bundle != NULL) {
     blamer_bundle->SetupCorrectSegmentation(word_res->chopped_word,
                                             wordrec_debug_blamer);
   }
 
   // pending[col] tells whether there is update work to do to combine
   // best_choice_bundle->beam[col - 1] with some BLOB_CHOICEs in matrix[col, *].
   // As the language model state is updated, pending entries are modified to
   // minimize duplication of work. It is important that during the update the
   // children are considered in the non-decreasing order of their column, since
   // this guarantees that all the parents would be up to date before an update
   // of a child is done.
   pending->init_to_size(word_res->ratings->dimension(), SegSearchPending());
 
   // Search the ratings matrix for the initial best path.
   (*pending)[0].SetColumnClassified();
   UpdateSegSearchNodes(rating_cert_scale, 0, pending, word_res,
                        pain_points, best_choice_bundle, blamer_bundle);
 }

◆ is_inside_angle()

bool tesseract::Wordrec::is_inside_angle ( EDGEPT * pt )

Definition at line 78 of file chop.cpp.

                                         {
   return angle_change(pt->prev, pt, pt->next) < chop_inside_angle;
 }

◆ merge_and_put_fragment_lists()

void tesseract::Wordrec::merge_and_put_fragment_lists	(	inT16	row,
		inT16	column,
		inT16	num_frag_parts,
		BLOB_CHOICE_LIST *	choice_lists,
		MATRIX *	ratings
	)

Definition at line 138 of file pieces.cpp.

                                                             {
   BLOB_CHOICE_IT *choice_lists_it = new BLOB_CHOICE_IT[num_frag_parts];
 
   for (int i = 0; i < num_frag_parts; i++) {
     choice_lists_it[i].set_to_list(&choice_lists[i]);
     choice_lists_it[i].mark_cycle_pt();
   }
 
   BLOB_CHOICE_LIST *merged_choice = ratings->get(row, column);
   if (merged_choice == NULL)
     merged_choice = new BLOB_CHOICE_LIST;
 
   bool end_of_list = false;
   BLOB_CHOICE_IT merged_choice_it(merged_choice);
   while (!end_of_list) {
     // Find the maximum unichar_id of the current entry the iterators
     // are pointing at
     UNICHAR_ID max_unichar_id = choice_lists_it[0].data()->unichar_id();
     for (int i = 0; i < num_frag_parts; i++) {
       UNICHAR_ID unichar_id = choice_lists_it[i].data()->unichar_id();
       if (max_unichar_id < unichar_id) {
         max_unichar_id = unichar_id;
       }
     }
 
     // Move the each iterators until it gets to an entry that has a
     // value greater than or equal to max_unichar_id
     for (int i = 0; i < num_frag_parts; i++) {
       UNICHAR_ID unichar_id = choice_lists_it[i].data()->unichar_id();
       while (!choice_lists_it[i].cycled_list() &&
              unichar_id < max_unichar_id) {
         choice_lists_it[i].forward();
         unichar_id = choice_lists_it[i].data()->unichar_id();
       }
       if (choice_lists_it[i].cycled_list()) {
         end_of_list = true;
         break;
       }
     }
 
     if (end_of_list)
       break;
 
     // Checks if the fragments are parts of the same character
     UNICHAR_ID first_unichar_id = choice_lists_it[0].data()->unichar_id();
     bool same_unichar = true;
     for (int i = 1; i < num_frag_parts; i++) {
       UNICHAR_ID unichar_id = choice_lists_it[i].data()->unichar_id();
       if (unichar_id != first_unichar_id) {
         same_unichar = false;
         break;
       }
     }
 
     if (same_unichar) {
       // Add the merged character to the result
       UNICHAR_ID merged_unichar_id = first_unichar_id;
       GenericVector<ScoredFont> merged_fonts =
           choice_lists_it[0].data()->fonts();
       float merged_min_xheight = choice_lists_it[0].data()->min_xheight();
       float merged_max_xheight = choice_lists_it[0].data()->max_xheight();
       float positive_yshift = 0, negative_yshift = 0;
       int merged_script_id = choice_lists_it[0].data()->script_id();
       BlobChoiceClassifier classifier = choice_lists_it[0].data()->classifier();
 
       float merged_rating = 0, merged_certainty = 0;
       for (int i = 0; i < num_frag_parts; i++) {
         float rating = choice_lists_it[i].data()->rating();
         float certainty = choice_lists_it[i].data()->certainty();
 
         if (i == 0 || certainty < merged_certainty)
           merged_certainty = certainty;
         merged_rating += rating;
 
         choice_lists_it[i].forward();
         if (choice_lists_it[i].cycled_list())
           end_of_list = true;
         IntersectRange(choice_lists_it[i].data()->min_xheight(),
                        choice_lists_it[i].data()->max_xheight(),
                        &merged_min_xheight, &merged_max_xheight);
         float yshift = choice_lists_it[i].data()->yshift();
         if (yshift > positive_yshift) positive_yshift = yshift;
         if (yshift < negative_yshift) negative_yshift = yshift;
         // Use the min font rating over the parts.
         // TODO(rays) font lists are unsorted. Need to be faster?
         const GenericVector<ScoredFont>& frag_fonts =
             choice_lists_it[i].data()->fonts();
         for (int f = 0; f < frag_fonts.size(); ++f) {
           int merged_f = 0;
           for (merged_f = 0; merged_f < merged_fonts.size() &&
                merged_fonts[merged_f].fontinfo_id != frag_fonts[f].fontinfo_id;
                ++merged_f) {}
           if (merged_f == merged_fonts.size()) {
             merged_fonts.push_back(frag_fonts[f]);
           } else if (merged_fonts[merged_f].score > frag_fonts[f].score) {
             merged_fonts[merged_f].score = frag_fonts[f].score;
           }
         }
       }
 
       float merged_yshift = positive_yshift != 0
           ? (negative_yshift != 0 ? 0 : positive_yshift)
           : negative_yshift;
       BLOB_CHOICE* choice = new BLOB_CHOICE(merged_unichar_id,
                                             merged_rating,
                                             merged_certainty,
                                             merged_script_id,
                                             merged_min_xheight,
                                             merged_max_xheight,
                                             merged_yshift,
                                             classifier);
       choice->set_fonts(merged_fonts);
       merged_choice_it.add_to_end(choice);
     }
   }
 
   if (classify_debug_level)
     print_ratings_list("Merged Fragments", merged_choice,
                        unicharset);
 
   if (merged_choice->empty())
     delete merged_choice;
   else
     ratings->put(row, column, merged_choice);
 
   delete [] choice_lists_it;
 }

◆ merge_fragments()

void tesseract::Wordrec::merge_fragments	(	MATRIX *	ratings,
		inT16	num_blobs
	)

Definition at line 313 of file pieces.cpp.

                                                               {
   BLOB_CHOICE_LIST choice_lists[CHAR_FRAGMENT::kMaxChunks];
   for (inT16 start = 0; start < num_blobs; start++) {
     for (int frag_parts = 2; frag_parts <= CHAR_FRAGMENT::kMaxChunks;
          frag_parts++) {
       get_fragment_lists(0, start, start, frag_parts, num_blobs,
                          ratings, choice_lists);
     }
   }
 
   // Delete fragments from the rating matrix
   for (inT16 x = 0; x < num_blobs; x++) {
     for (inT16 y = x; y < num_blobs; y++) {
       BLOB_CHOICE_LIST *choices = ratings->get(x, y);
       if (choices != NULL) {
         BLOB_CHOICE_IT choices_it(choices);
         for (choices_it.mark_cycle_pt(); !choices_it.cycled_list();
              choices_it.forward()) {
           UNICHAR_ID choice_unichar_id = choices_it.data()->unichar_id();
           const CHAR_FRAGMENT *frag =
               unicharset.get_fragment(choice_unichar_id);
           if (frag != NULL)
             delete choices_it.extract();
         }
       }
     }
   }
 }

◆ near_point()

bool tesseract::Wordrec::near_point	(	EDGEPT *	point,
		EDGEPT *	line_pt_0,
		EDGEPT *	line_pt_1,
		EDGEPT **	near_pt
	)

Definition at line 49 of file outlines.cpp.

                                            {
   TPOINT p;
 
   float slope;
   float intercept;
 
   float x0 = line_pt_0->pos.x;
   float x1 = line_pt_1->pos.x;
   float y0 = line_pt_0->pos.y;
   float y1 = line_pt_1->pos.y;
 
   if (x0 == x1) {
                                  /* Handle vertical line */
     p.x = (inT16) x0;
     p.y = point->pos.y;
   }
   else {
     /* Slope and intercept */
     slope = (y0 - y1) / (x0 - x1);
     intercept = y1 - x1 * slope;
 
     /* Find perpendicular */
     p.x = (inT16) ((point->pos.x + (point->pos.y - intercept) * slope) /
       (slope * slope + 1));
     p.y = (inT16) (slope * p.x + intercept);
   }
 
   if (is_on_line (p, line_pt_0->pos, line_pt_1->pos) &&
     (!same_point (p, line_pt_0->pos)) && (!same_point (p, line_pt_1->pos))) {
     /* Intersection on line */
     *near_pt = make_edgept(p.x, p.y, line_pt_1, line_pt_0);
     return true;
   } else {                           /* Intersection not on line */
     *near_pt = closest(point, line_pt_0, line_pt_1);
     return false;
   }
 }

◆ new_max_point()

void tesseract::Wordrec::new_max_point	(	EDGEPT *	local_max,
		PointHeap *	points
	)

Definition at line 245 of file chop.cpp.

                                                                 {
   inT16 dir;
 
   dir = direction (local_max);
 
   if (dir > 0) {
     add_point_to_list(points, local_max);
     return;
   }
 
   if (dir == 0 && point_priority (local_max) < 0) {
     add_point_to_list(points, local_max);
     return;
   }
 }

◆ new_min_point()

void tesseract::Wordrec::new_min_point	(	EDGEPT *	local_min,
		PointHeap *	points
	)

Definition at line 221 of file chop.cpp.

                                                                 {
   inT16 dir;
 
   dir = direction (local_min);
 
   if (dir < 0) {
     add_point_to_list(points, local_min);
     return;
   }
 
   if (dir == 0 && point_priority (local_min) < 0) {
     add_point_to_list(points, local_min);
     return;
   }
 }

◆ pick_close_point()

EDGEPT * tesseract::Wordrec::pick_close_point	(	EDGEPT *	critical_point,
		EDGEPT *	vertical_point,
		int *	best_dist
	)

Definition at line 124 of file chop.cpp.

                                                   {
   EDGEPT *best_point = NULL;
   int this_distance;
   int found_better;
 
   do {
     found_better = FALSE;
 
     this_distance = edgept_dist (critical_point, vertical_point);
     if (this_distance <= *best_dist) {
 
       if (!(same_point (critical_point->pos, vertical_point->pos) ||
         same_point (critical_point->pos, vertical_point->next->pos) ||
         (best_point && same_point (best_point->pos, vertical_point->pos)) ||
       is_exterior_point (critical_point, vertical_point))) {
         *best_dist = this_distance;
         best_point = vertical_point;
         if (chop_vertical_creep)
           found_better = TRUE;
       }
     }
     vertical_point = vertical_point->next;
   }
   while (found_better == TRUE);
 
   return (best_point);
 }

◆ pick_good_seam()

SEAM * tesseract::Wordrec::pick_good_seam ( TBLOB * blob )

Definition at line 215 of file findseam.cpp.

                                          {
   SeamPile seam_pile(chop_seam_pile_size);
   EDGEPT *points[MAX_NUM_POINTS];
   EDGEPT_CLIST new_points;
   SEAM *seam = NULL;
   TESSLINE *outline;
   inT16 num_points = 0;
 
 #ifndef GRAPHICS_DISABLED
   if (chop_debug > 2)
     wordrec_display_splits.set_value(true);
 
   draw_blob_edges(blob);
 #endif
 
   PointHeap point_heap(MAX_NUM_POINTS);
   for (outline = blob->outlines; outline; outline = outline->next)
     prioritize_points(outline, &point_heap);
 
   while (!point_heap.empty() && num_points < MAX_NUM_POINTS) {
     points[num_points++] = point_heap.PeekTop().data;
     point_heap.Pop(NULL);
   }
 
   /* Initialize queue */
   SeamQueue seam_queue(MAX_NUM_SEAMS);
 
   try_point_pairs(points, num_points, &seam_queue, &seam_pile, &seam, blob);
   try_vertical_splits(points, num_points, &new_points,
                       &seam_queue, &seam_pile, &seam, blob);
 
   if (seam == NULL) {
     choose_best_seam(&seam_queue, NULL, BAD_PRIORITY, &seam, blob, &seam_pile);
   } else if (seam->priority() > chop_good_split) {
     choose_best_seam(&seam_queue, NULL, seam->priority(), &seam, blob,
                      &seam_pile);
   }
 
   EDGEPT_C_IT it(&new_points);
   for (it.mark_cycle_pt(); !it.cycled_list(); it.forward()) {
     EDGEPT *inserted_point = it.data();
     if (seam == NULL || !seam->UsesPoint(inserted_point)) {
       for (outline = blob->outlines; outline; outline = outline->next) {
         if (outline->loop == inserted_point) {
           outline->loop = outline->loop->next;
         }
       }
       remove_edgept(inserted_point);
     }
   }
 
   if (seam) {
     if (seam->priority() > chop_ok_split) {
       delete seam;
       seam = NULL;
     }
 #ifndef GRAPHICS_DISABLED
     else if (wordrec_display_splits) {
       seam->Mark(edge_window);
       if (chop_debug > 2) {
         update_edge_window();
         edge_window_wait();
       }
     }
 #endif
   }
 
   if (chop_debug)
     wordrec_display_splits.set_value(false);
 
   return (seam);
 }

◆ point_priority()

PRIORITY tesseract::Wordrec::point_priority ( EDGEPT * point )

Definition at line 54 of file chop.cpp.

                                               {
   return (PRIORITY)angle_change(point->prev, point, point->next);
 }

◆ prioritize_points()

void tesseract::Wordrec::prioritize_points	(	TESSLINE *	outline,
		PointHeap *	points
	)

Definition at line 162 of file chop.cpp.

                                                                     {
   EDGEPT *this_point;
   EDGEPT *local_min = NULL;
   EDGEPT *local_max = NULL;
 
   this_point = outline->loop;
   local_min = this_point;
   local_max = this_point;
   do {
     if (this_point->vec.y < 0) {
                                  /* Look for minima */
       if (local_max != NULL)
         new_max_point(local_max, points);
       else if (is_inside_angle (this_point))
         add_point_to_list(points, this_point);
       local_max = NULL;
       local_min = this_point->next;
     }
     else if (this_point->vec.y > 0) {
                                  /* Look for maxima */
       if (local_min != NULL)
         new_min_point(local_min, points);
       else if (is_inside_angle (this_point))
         add_point_to_list(points, this_point);
       local_min = NULL;
       local_max = this_point->next;
     }
     else {
       /* Flat area */
       if (local_max != NULL) {
         if (local_max->prev->vec.y != 0) {
           new_max_point(local_max, points);
         }
         local_max = this_point->next;
         local_min = NULL;
       }
       else {
         if (local_min->prev->vec.y != 0) {
           new_min_point(local_min, points);
         }
         local_min = this_point->next;
         local_max = NULL;
       }
     }
 
                                  /* Next point */
     this_point = this_point->next;
   }
   while (this_point != outline->loop);
 }

◆ ProcessSegSearchPainPoint()

void tesseract::Wordrec::ProcessSegSearchPainPoint	(	float	pain_point_priority,
		const MATRIX_COORD &	pain_point,
		const char *	pain_point_type,
		GenericVector< SegSearchPending > *	pending,
		WERD_RES *	word_res,
		LMPainPoints *	pain_points,
		BlamerBundle *	blamer_bundle
	)

protected

Definition at line 262 of file segsearch.cpp.

                                                             {
   if (segsearch_debug_level > 0) {
     tprintf("Classifying pain point %s priority=%.4f, col=%d, row=%d\n",
             pain_point_type, pain_point_priority,
             pain_point.col, pain_point.row);
   }
   ASSERT_HOST(pain_points != NULL);
   MATRIX *ratings = word_res->ratings;
   // Classify blob [pain_point.col pain_point.row]
   if (!pain_point.Valid(*ratings)) {
     ratings->IncreaseBandSize(pain_point.row + 1 - pain_point.col);
   }
   ASSERT_HOST(pain_point.Valid(*ratings));
   BLOB_CHOICE_LIST *classified = classify_piece(word_res->seam_array,
                                                 pain_point.col, pain_point.row,
                                                 pain_point_type,
                                                 word_res->chopped_word,
                                                 blamer_bundle);
   BLOB_CHOICE_LIST *lst = ratings->get(pain_point.col, pain_point.row);
   if (lst == NULL) {
     ratings->put(pain_point.col, pain_point.row, classified);
   } else {
     // We can not delete old BLOB_CHOICEs, since they might contain
     // ViterbiStateEntries that are parents of other "active" entries.
     // Thus if the matrix cell already contains classifications we add
     // the new ones to the beginning of the list.
     BLOB_CHOICE_IT it(lst);
     it.add_list_before(classified);
     delete classified;  // safe to delete, since empty after add_list_before()
     classified = NULL;
   }
 
   if (segsearch_debug_level > 0) {
     print_ratings_list("Updated ratings matrix with a new entry:",
                        ratings->get(pain_point.col, pain_point.row),
                        getDict().getUnicharset());
     ratings->print(getDict().getUnicharset());
   }
 
   // Insert initial "pain points" to join the newly classified blob
   // with its left and right neighbors.
   if (classified != NULL && !classified->empty()) {
     if (pain_point.col > 0) {
       pain_points->GeneratePainPoint(
           pain_point.col - 1, pain_point.row, LM_PPTYPE_SHAPE, 0.0,
           true, segsearch_max_char_wh_ratio, word_res);
     }
     if (pain_point.row + 1 < ratings->dimension()) {
       pain_points->GeneratePainPoint(
           pain_point.col, pain_point.row + 1, LM_PPTYPE_SHAPE, 0.0,
           true, segsearch_max_char_wh_ratio, word_res);
     }
   }
   (*pending)[pain_point.col].SetBlobClassified(pain_point.row);
 }

◆ program_editdown()

void tesseract::Wordrec::program_editdown ( inT32 elasped_time )

Definition at line 78 of file tface.cpp.

                                                  {
   EndAdaptiveClassifier();
   getDict().End();
 }

◆ program_editup()

void tesseract::Wordrec::program_editup	(	const char *	textbase,
		TessdataManager *	init_classifier,
		TessdataManager *	init_dict
	)

Definition at line 46 of file tface.cpp.

                                                          {
   if (textbase != NULL) imagefile = textbase;
   InitFeatureDefs(&feature_defs_);
   InitAdaptiveClassifier(init_classifier);
   if (init_dict) {
     getDict().SetupForLoad(Dict::GlobalDawgCache());
     getDict().Load(lang, init_dict);
     getDict().FinishLoad();
   }
   pass2_ok_split = chop_ok_split;
 }

◆ ResetNGramSearch()

void tesseract::Wordrec::ResetNGramSearch	(	WERD_RES *	word_res,
		BestChoiceBundle *	best_choice_bundle,
		GenericVector< SegSearchPending > *	pending
	)

protected

Definition at line 325 of file segsearch.cpp.

                                                                          {
   // TODO(rays) More refactoring required here.
   // Delete existing viterbi states.
   for (int col = 0; col < best_choice_bundle->beam.size(); ++col) {
     best_choice_bundle->beam[col]->Clear();
   }
   // Reset best_choice_bundle.
   word_res->ClearWordChoices();
   best_choice_bundle->best_vse = NULL;
   // Clear out all existing pendings and add a new one for the first column.
   (*pending)[0].SetColumnClassified();
   for (int i = 1; i < pending->size(); ++i)
     (*pending)[i].Clear();
 }

◆ SaveAltChoices()

void tesseract::Wordrec::SaveAltChoices	(	const LIST &	best_choices,
		WERD_RES *	word
	)

◆ SegSearch()

void tesseract::Wordrec::SegSearch	(	WERD_RES *	word_res,
		BestChoiceBundle *	best_choice_bundle,
		BlamerBundle *	blamer_bundle
	)

Definition at line 37 of file segsearch.cpp.

                                                      {
   LMPainPoints pain_points(segsearch_max_pain_points,
                            segsearch_max_char_wh_ratio,
                            assume_fixed_pitch_char_segment,
                            &getDict(), segsearch_debug_level);
   // Compute scaling factor that will help us recover blob outline length
   // from classifier rating and certainty for the blob.
   float rating_cert_scale = -1.0 * getDict().certainty_scale / rating_scale;
   GenericVector<SegSearchPending> pending;
   InitialSegSearch(word_res, &pain_points, &pending, best_choice_bundle,
                    blamer_bundle);
 
   if (!SegSearchDone(0)) {  // find a better choice
     if (chop_enable && word_res->chopped_word != NULL) {
       improve_by_chopping(rating_cert_scale, word_res, best_choice_bundle,
                           blamer_bundle, &pain_points, &pending);
     }
     if (chop_debug) SEAM::PrintSeams("Final seam list:", word_res->seam_array);
 
     if (blamer_bundle != NULL &&
         !blamer_bundle->ChoiceIsCorrect(word_res->best_choice)) {
       blamer_bundle->SetChopperBlame(word_res, wordrec_debug_blamer);
     }
   }
   // Keep trying to find a better path by fixing the "pain points".
 
   MATRIX_COORD pain_point;
   float pain_point_priority;
   int num_futile_classifications = 0;
   STRING blamer_debug;
   while (wordrec_enable_assoc &&
       (!SegSearchDone(num_futile_classifications) ||
           (blamer_bundle != NULL &&
               blamer_bundle->GuidedSegsearchStillGoing()))) {
     // Get the next valid "pain point".
     bool found_nothing = true;
     LMPainPointsType pp_type;
     while ((pp_type = pain_points.Deque(&pain_point, &pain_point_priority)) !=
         LM_PPTYPE_NUM) {
       if (!pain_point.Valid(*word_res->ratings)) {
         word_res->ratings->IncreaseBandSize(
             pain_point.row - pain_point.col + 1);
       }
       if (pain_point.Valid(*word_res->ratings) &&
           !word_res->ratings->Classified(pain_point.col, pain_point.row,
                                          getDict().WildcardID())) {
         found_nothing = false;
         break;
       }
     }
     if (found_nothing) {
       if (segsearch_debug_level > 0) tprintf("Pain points queue is empty\n");
       break;
     }
     ProcessSegSearchPainPoint(pain_point_priority, pain_point,
                               LMPainPoints::PainPointDescription(pp_type),
                               &pending, word_res, &pain_points, blamer_bundle);
 
     UpdateSegSearchNodes(rating_cert_scale, pain_point.col, &pending,
                          word_res, &pain_points, best_choice_bundle,
                          blamer_bundle);
     if (!best_choice_bundle->updated) ++num_futile_classifications;
 
     if (segsearch_debug_level > 0) {
       tprintf("num_futile_classifications %d\n", num_futile_classifications);
     }
 
     best_choice_bundle->updated = false;  // reset updated
 
     // See if it's time to terminate SegSearch or time for starting a guided
     // search for the true path to find the blame for the incorrect best_choice.
     if (SegSearchDone(num_futile_classifications) &&
         blamer_bundle != NULL &&
         blamer_bundle->GuidedSegsearchNeeded(word_res->best_choice)) {
       InitBlamerForSegSearch(word_res, &pain_points, blamer_bundle,
                              &blamer_debug);
     }
   }  // end while loop exploring alternative paths
   if (blamer_bundle != NULL) {
     blamer_bundle->FinishSegSearch(word_res->best_choice,
                                    wordrec_debug_blamer, &blamer_debug);
   }
 
   if (segsearch_debug_level > 0) {
     tprintf("Done with SegSearch (AcceptableChoiceFound: %d)\n",
             language_model_->AcceptableChoiceFound());
   }
 }

◆ SegSearchDone()

bool tesseract::Wordrec::SegSearchDone ( int num_futile_classifications )

inlineprotected

Definition at line 425 of file wordrec.h.

                                                             {
     return (language_model_->AcceptableChoiceFound() ||
             num_futile_classifications >=
             segsearch_max_futile_classifications);
   }

◆ select_blob_to_split()

int tesseract::Wordrec::select_blob_to_split	(	const GenericVector< BLOB_CHOICE *> &	blob_choices,
		float	rating_ceiling,
		bool	split_next_to_fragment
	)

Definition at line 540 of file chopper.cpp.

                                                        {
   BLOB_CHOICE *blob_choice;
   int x;
   float worst = -MAX_FLOAT32;
   int worst_index = -1;
   float worst_near_fragment = -MAX_FLOAT32;
   int worst_index_near_fragment = -1;
   const CHAR_FRAGMENT **fragments = NULL;
 
   if (chop_debug) {
     if (rating_ceiling < MAX_FLOAT32)
       tprintf("rating_ceiling = %8.4f\n", rating_ceiling);
     else
       tprintf("rating_ceiling = No Limit\n");
   }
 
   if (split_next_to_fragment && blob_choices.size() > 0) {
     fragments = new const CHAR_FRAGMENT *[blob_choices.length()];
     if (blob_choices[0] != NULL) {
       fragments[0] = getDict().getUnicharset().get_fragment(
           blob_choices[0]->unichar_id());
     } else {
       fragments[0] = NULL;
     }
   }
 
   for (x = 0; x < blob_choices.size(); ++x) {
     if (blob_choices[x] == NULL) {
       delete[] fragments;
       return x;
     } else {
       blob_choice = blob_choices[x];
       // Populate fragments for the following position.
       if (split_next_to_fragment && x+1 < blob_choices.size()) {
         if (blob_choices[x + 1] != NULL) {
           fragments[x + 1] = getDict().getUnicharset().get_fragment(
               blob_choices[x + 1]->unichar_id());
         } else {
           fragments[x + 1] = NULL;
         }
       }
       if (blob_choice->rating() < rating_ceiling &&
           blob_choice->certainty() < tessedit_certainty_threshold) {
         // Update worst and worst_index.
         if (blob_choice->rating() > worst) {
           worst_index = x;
           worst = blob_choice->rating();
         }
         if (split_next_to_fragment) {
           // Update worst_near_fragment and worst_index_near_fragment.
           bool expand_following_fragment =
             (x + 1 < blob_choices.size() &&
              fragments[x+1] != NULL && !fragments[x+1]->is_beginning());
           bool expand_preceding_fragment =
             (x > 0 && fragments[x-1] != NULL && !fragments[x-1]->is_ending());
           if ((expand_following_fragment || expand_preceding_fragment) &&
               blob_choice->rating() > worst_near_fragment) {
             worst_index_near_fragment = x;
             worst_near_fragment = blob_choice->rating();
             if (chop_debug) {
               tprintf("worst_index_near_fragment=%d"
                       " expand_following_fragment=%d"
                       " expand_preceding_fragment=%d\n",
                       worst_index_near_fragment,
                       expand_following_fragment,
                       expand_preceding_fragment);
             }
           }
         }
       }
     }
   }
   delete[] fragments;
   // TODO(daria): maybe a threshold of badness for
   // worst_near_fragment would be useful.
   return worst_index_near_fragment != -1 ?
     worst_index_near_fragment : worst_index;
 }

◆ select_blob_to_split_from_fixpt()

int tesseract::Wordrec::select_blob_to_split_from_fixpt ( DANGERR * fixpt )

Definition at line 628 of file chopper.cpp.

                                                            {
   if (!fixpt)
     return -1;
   for (int i = 0; i < fixpt->size(); i++) {
     if ((*fixpt)[i].begin + 1 == (*fixpt)[i].end &&
         (*fixpt)[i].dangerous &&
         (*fixpt)[i].correct_is_ngram) {
       return (*fixpt)[i].begin;
     }
   }
   return -1;
 }

◆ set_pass1()

void tesseract::Wordrec::set_pass1 ( )

Definition at line 89 of file tface.cpp.

                         {
   chop_ok_split.set_value(70.0);
   language_model_->getParamsModel().SetPass(ParamsModel::PTRAIN_PASS1);
   SettupPass1();
 }

◆ set_pass2()

void tesseract::Wordrec::set_pass2 ( )

Definition at line 101 of file tface.cpp.

                         {
   chop_ok_split.set_value(pass2_ok_split);
   language_model_->getParamsModel().SetPass(ParamsModel::PTRAIN_PASS2);
   SettupPass2();
 }

◆ try_point_pairs()

void tesseract::Wordrec::try_point_pairs	(	EDGEPT *	points[MAX_NUM_POINTS],
		inT16	num_points,
		SeamQueue *	seam_queue,
		SeamPile *	seam_pile,
		SEAM **	seam,
		TBLOB *	blob
	)

Definition at line 296 of file findseam.cpp.

                                             {
   inT16 x;
   inT16 y;
   PRIORITY priority;
 
   for (x = 0; x < num_points; x++) {
     for (y = x + 1; y < num_points; y++) {
       if (points[y] &&
           points[x]->WeightedDistance(*points[y], chop_x_y_weight) <
               chop_split_length &&
           points[x] != points[y]->next && points[y] != points[x]->next &&
           !is_exterior_point(points[x], points[y]) &&
           !is_exterior_point(points[y], points[x])) {
         SPLIT split(points[x], points[y]);
         priority = partial_split_priority(&split);
 
         choose_best_seam(seam_queue, &split, priority, seam, blob, seam_pile);
       }
     }
   }
 }

◆ try_vertical_splits()

void tesseract::Wordrec::try_vertical_splits	(	EDGEPT *	points[MAX_NUM_POINTS],
		inT16	num_points,
		EDGEPT_CLIST *	new_points,
		SeamQueue *	seam_queue,
		SeamPile *	seam_pile,
		SEAM **	seam,
		TBLOB *	blob
	)

Definition at line 334 of file findseam.cpp.

                                                 {
   EDGEPT *vertical_point = NULL;
   inT16 x;
   PRIORITY priority;
   TESSLINE *outline;
 
   for (x = 0; x < num_points; x++) {
     vertical_point = NULL;
     for (outline = blob->outlines; outline; outline = outline->next) {
       vertical_projection_point(points[x], outline->loop,
                                 &vertical_point, new_points);
     }
 
     if (vertical_point && points[x] != vertical_point->next &&
         vertical_point != points[x]->next &&
         points[x]->WeightedDistance(*vertical_point, chop_x_y_weight) <
             chop_split_length) {
       SPLIT split(points[x], vertical_point);
       priority = partial_split_priority(&split);
       choose_best_seam(seam_queue, &split, priority, seam, blob, seam_pile);
     }
   }
 }

◆ UpdateSegSearchNodes()

void tesseract::Wordrec::UpdateSegSearchNodes	(	float	rating_cert_scale,
		int	starting_col,
		GenericVector< SegSearchPending > *	pending,
		WERD_RES *	word_res,
		LMPainPoints *	pain_points,
		BestChoiceBundle *	best_choice_bundle,
		BlamerBundle *	blamer_bundle
	)

protected

Definition at line 194 of file segsearch.cpp.

                                  {
   MATRIX *ratings = word_res->ratings;
   ASSERT_HOST(ratings->dimension() == pending->size());
   ASSERT_HOST(ratings->dimension() == best_choice_bundle->beam.size());
   for (int col = starting_col; col < ratings->dimension(); ++col) {
     if (!(*pending)[col].WorkToDo()) continue;
     int first_row = col;
     int last_row = MIN(ratings->dimension() - 1,
                        col + ratings->bandwidth() - 1);
     if ((*pending)[col].SingleRow() >= 0) {
       first_row = last_row = (*pending)[col].SingleRow();
     }
     if (segsearch_debug_level > 0) {
       tprintf("\n\nUpdateSegSearchNodes: col=%d, rows=[%d,%d], alljust=%d\n",
               col, first_row, last_row,
               (*pending)[col].IsRowJustClassified(MAX_INT32));
     }
     // Iterate over the pending list for this column.
     for (int row = first_row; row <= last_row; ++row) {
       // Update language model state of this child+parent pair.
       BLOB_CHOICE_LIST *current_node = ratings->get(col, row);
       LanguageModelState *parent_node =
           col == 0 ? NULL : best_choice_bundle->beam[col - 1];
       if (current_node != NULL &&
           language_model_->UpdateState((*pending)[col].IsRowJustClassified(row),
                                        col, row, current_node, parent_node,
                                        pain_points, word_res,
                                        best_choice_bundle, blamer_bundle) &&
           row + 1 < ratings->dimension()) {
         // Since the language model state of this entry changed, process all
         // the child column.
         (*pending)[row + 1].RevisitWholeColumn();
         if (segsearch_debug_level > 0) {
           tprintf("Added child col=%d to pending\n", row + 1);
         }
       }  // end if UpdateState.
     }  // end for row.
   }  // end for col.
   if (best_choice_bundle->best_vse != NULL) {
     ASSERT_HOST(word_res->StatesAllValid());
     if (best_choice_bundle->best_vse->updated) {
       pain_points->GenerateFromPath(rating_cert_scale,
                                     best_choice_bundle->best_vse, word_res);
       if (!best_choice_bundle->fixpt.empty()) {
         pain_points->GenerateFromAmbigs(best_choice_bundle->fixpt,
                                         best_choice_bundle->best_vse, word_res);
       }
     }
   }
   // The segsearch is completed. Reset all updated flags on all VSEs and reset
   // all pendings.
   for (int col = 0; col < pending->size(); ++col) {
     (*pending)[col].Clear();
     ViterbiStateEntry_IT
         vse_it(&best_choice_bundle->beam[col]->viterbi_state_entries);
     for (vse_it.mark_cycle_pt(); !vse_it.cycled_list(); vse_it.forward()) {
       vse_it.data()->updated = false;
     }
   }
 }

◆ vertical_projection_point()

void tesseract::Wordrec::vertical_projection_point	(	EDGEPT *	split_point,
		EDGEPT *	target_point,
		EDGEPT **	best_point,
		EDGEPT_CLIST *	new_points
	)

Definition at line 274 of file chop.cpp.

                                                                   {
   EDGEPT *p;                     /* Iterator */
   EDGEPT *this_edgept;           /* Iterator */
   EDGEPT_C_IT new_point_it(new_points);
   int x = split_point->pos.x;    /* X value of vertical */
   int best_dist = LARGE_DISTANCE;/* Best point found */
 
   if (*best_point != NULL)
     best_dist = edgept_dist(split_point, *best_point);
 
   p = target_point;
   /* Look at each edge point */
   do {
     if (((p->pos.x <= x && x <= p->next->pos.x) ||
          (p->next->pos.x <= x && x <= p->pos.x)) &&
         !same_point(split_point->pos, p->pos) &&
         !same_point(split_point->pos, p->next->pos) &&
         !p->IsChopPt() &&
         (*best_point == NULL || !same_point((*best_point)->pos, p->pos))) {
 
       if (near_point(split_point, p, p->next, &this_edgept)) {
         new_point_it.add_before_then_move(this_edgept);
       }
 
       if (*best_point == NULL)
         best_dist = edgept_dist (split_point, this_edgept);
 
       this_edgept =
         pick_close_point(split_point, this_edgept, &best_dist);
       if (this_edgept)
         *best_point = this_edgept;
     }
 
     p = p->next;
   }
   while (p != target_point);
 }

◆ WordSearch()

void tesseract::Wordrec::WordSearch ( WERD_RES * word_res )

Definition at line 130 of file segsearch.cpp.

                                            {
   LMPainPoints pain_points(segsearch_max_pain_points,
                            segsearch_max_char_wh_ratio,
                            assume_fixed_pitch_char_segment,
                            &getDict(), segsearch_debug_level);
   GenericVector<SegSearchPending> pending;
   BestChoiceBundle best_choice_bundle(word_res->ratings->dimension());
   // Run Segmentation Search.
   InitialSegSearch(word_res, &pain_points, &pending, &best_choice_bundle, NULL);
   if (segsearch_debug_level > 0) {
     tprintf("Ending ratings matrix%s:\n",
             wordrec_enable_assoc ? " (with assoc)" : "");
     word_res->ratings->print(getDict().getUnicharset());
   }
 }

Member Data Documentation

◆ assume_fixed_pitch_char_segment

bool tesseract::Wordrec::assume_fixed_pitch_char_segment = FALSE

"include fixed-pitch heuristics in char segmentation"

Definition at line 161 of file wordrec.h.

◆ blame_reasons_

GenericVector<int> tesseract::Wordrec::blame_reasons_

Definition at line 417 of file wordrec.h.

◆ chop_center_knob

double tesseract::Wordrec::chop_center_knob = 0.15

"Split center adjustment"

Definition at line 151 of file wordrec.h.

◆ chop_centered_maxwidth

int tesseract::Wordrec::chop_centered_maxwidth = 90

"Width of (smaller) chopped blobs " "above which we don't care that a chop is not near the center."

Definition at line 153 of file wordrec.h.

◆ chop_debug

int tesseract::Wordrec::chop_debug = 0

"Chop debug"

Definition at line 139 of file wordrec.h.

◆ chop_enable

bool tesseract::Wordrec::chop_enable = 1

"Chop enable"

Definition at line 140 of file wordrec.h.

◆ chop_good_split

double tesseract::Wordrec::chop_good_split = 50.0

"Good split limit"

Definition at line 157 of file wordrec.h.

◆ chop_inside_angle

int tesseract::Wordrec::chop_inside_angle = -50

"Min Inside Angle Bend"

Definition at line 147 of file wordrec.h.

◆ chop_min_outline_area

int tesseract::Wordrec::chop_min_outline_area = 2000

"Min Outline Area"

Definition at line 148 of file wordrec.h.

◆ chop_min_outline_points

int tesseract::Wordrec::chop_min_outline_points = 6

"Min Number of Points on Outline"

Definition at line 144 of file wordrec.h.

◆ chop_new_seam_pile

bool tesseract::Wordrec::chop_new_seam_pile = 1

"Use new seam_pile"

Definition at line 146 of file wordrec.h.

◆ chop_ok_split

double tesseract::Wordrec::chop_ok_split = 100.0

"OK split limit"

Definition at line 156 of file wordrec.h.

◆ chop_overlap_knob

double tesseract::Wordrec::chop_overlap_knob = 0.9

"Split overlap adjustment"

Definition at line 150 of file wordrec.h.

◆ chop_same_distance

int tesseract::Wordrec::chop_same_distance = 2

"Same distance"

Definition at line 143 of file wordrec.h.

◆ chop_seam_pile_size

int tesseract::Wordrec::chop_seam_pile_size = 150

"Max number of seams in seam_pile"

Definition at line 145 of file wordrec.h.

◆ chop_sharpness_knob

double tesseract::Wordrec::chop_sharpness_knob = 0.06

"Split sharpness adjustment"

Definition at line 154 of file wordrec.h.

◆ chop_split_dist_knob

double tesseract::Wordrec::chop_split_dist_knob = 0.5

"Split length adjustment"

Definition at line 149 of file wordrec.h.

◆ chop_split_length

int tesseract::Wordrec::chop_split_length = 10000

"Split Length"

Definition at line 142 of file wordrec.h.

◆ chop_vertical_creep

bool tesseract::Wordrec::chop_vertical_creep = 0

"Vertical creep"

Definition at line 141 of file wordrec.h.

◆ chop_width_change_knob

double tesseract::Wordrec::chop_width_change_knob = 5.0

"Width change adjustment"

Definition at line 155 of file wordrec.h.

◆ chop_x_y_weight

int tesseract::Wordrec::chop_x_y_weight = 3

"X / Y length weight"

Definition at line 158 of file wordrec.h.

◆ fill_lattice_

void(Wordrec::* tesseract::Wordrec::fill_lattice_) (const MATRIX &ratings, const WERD_CHOICE_LIST &best_choices, const UNICHARSET &unicharset, BlamerBundle *blamer_bundle)

Definition at line 419 of file wordrec.h.

◆ force_word_assoc

bool tesseract::Wordrec::force_word_assoc = FALSE

"force associator to run regardless of what enable_assoc is." "This is used for CJK where component grouping is necessary."

Definition at line 133 of file wordrec.h.

◆ fragments_guide_chopper

bool tesseract::Wordrec::fragments_guide_chopper = FALSE

"Use information from fragments to guide chopping process"

Definition at line 136 of file wordrec.h.

◆ language_model_

LanguageModel* tesseract::Wordrec::language_model_

Definition at line 410 of file wordrec.h.

◆ merge_fragments_in_matrix

bool tesseract::Wordrec::merge_fragments_in_matrix = TRUE

"Merge the fragments in the ratings matrix and delete them " "after merging"

Definition at line 128 of file wordrec.h.

◆ pass2_ok_split

PRIORITY tesseract::Wordrec::pass2_ok_split

Definition at line 411 of file wordrec.h.

◆ prev_word_best_choice_

WERD_CHOICE* tesseract::Wordrec::prev_word_best_choice_

Definition at line 415 of file wordrec.h.

◆ repair_unchopped_blobs

int tesseract::Wordrec::repair_unchopped_blobs = 1

"Fix blobs that aren't chopped"

Definition at line 137 of file wordrec.h.

◆ save_alt_choices

bool tesseract::Wordrec::save_alt_choices = true

"Save alternative paths found during chopping " "and segmentation search"

Definition at line 178 of file wordrec.h.

◆ segment_adjust_debug

int tesseract::Wordrec::segment_adjust_debug = 0

"Segmentation adjustment debug"

Definition at line 159 of file wordrec.h.

◆ segsearch_debug_level

int tesseract::Wordrec::segsearch_debug_level = 0

"SegSearch debug level"

Definition at line 169 of file wordrec.h.

◆ segsearch_max_char_wh_ratio

double tesseract::Wordrec::segsearch_max_char_wh_ratio = 2.0

"Maximum character width-to-height ratio"

Definition at line 175 of file wordrec.h.

◆ segsearch_max_futile_classifications

int tesseract::Wordrec::segsearch_max_futile_classifications = 10

"Maximum number of pain point classifications per word."

Definition at line 173 of file wordrec.h.

◆ segsearch_max_pain_points

int tesseract::Wordrec::segsearch_max_pain_points = 2000

"Maximum number of pain points stored in the queue"

Definition at line 171 of file wordrec.h.

◆ tessedit_certainty_threshold

double tesseract::Wordrec::tessedit_certainty_threshold = -2.25

"Good blob limit"

Definition at line 138 of file wordrec.h.

◆ wordrec_debug_blamer

bool tesseract::Wordrec::wordrec_debug_blamer = false

"Print blamer debug messages"

Definition at line 167 of file wordrec.h.

◆ wordrec_debug_level

int tesseract::Wordrec::wordrec_debug_level = 0

"Debug level for wordrec"

Definition at line 162 of file wordrec.h.

◆ wordrec_enable_assoc

bool tesseract::Wordrec::wordrec_enable_assoc = TRUE

"Associator Enable"

Definition at line 130 of file wordrec.h.

◆ wordrec_max_join_chunks

int tesseract::Wordrec::wordrec_max_join_chunks = 4

"Max number of broken pieces to associate"

Definition at line 164 of file wordrec.h.

◆ wordrec_no_block

bool tesseract::Wordrec::wordrec_no_block = FALSE

"Don't output block information"

Definition at line 129 of file wordrec.h.

◆ wordrec_run_blamer

bool tesseract::Wordrec::wordrec_run_blamer = false

"Try to set the blame for errors"

Definition at line 168 of file wordrec.h.

◆ wordrec_skip_no_truth_words

bool tesseract::Wordrec::wordrec_skip_no_truth_words = false

"Only run OCR for words that had truth recorded in BlamerBundle"

Definition at line 166 of file wordrec.h.

◆ wordrec_worst_state

double tesseract::Wordrec::wordrec_worst_state = 1

"Worst segmentation state"

Definition at line 134 of file wordrec.h.

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

/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/wordrec.h
/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/chop.cpp
/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/chopper.cpp
/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/findseam.cpp
/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/gradechop.cpp
/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/outlines.cpp
/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/pieces.cpp
/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/segsearch.cpp
/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/tface.cpp
/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/wordclass.cpp
/home/stefan/src/github/tesseract-ocr/tesseract/wordrec/wordrec.cpp

Public Member Functions

Public Attributes

Protected Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Wordrec()

◆ ~Wordrec()

Member Function Documentation

◆ add_point_to_list()

◆ add_seam_to_queue()

◆ angle_change()

◆ attempt_blob_chop()

◆ call_matcher()

◆ CallFillLattice()

◆ cc_recog()

◆ choose_best_seam()

◆ chop_numbered_blob()

◆ chop_one_blob()

◆ chop_overlapping_blob()

◆ chop_word_main()

◆ classify_blob()

◆ classify_piece()

◆ combine_seam()

◆ dict_word()

◆ DoSegSearch()

◆ end_recog()

◆ fill_filtered_fragment_list()

◆ FillLattice()

◆ get_fragment_lists()

◆ grade_sharpness()

◆ grade_split_length()

◆ improve_by_chopping()

◆ improve_one_blob()

◆ InitBlamerForSegSearch()

◆ InitialSegSearch()

◆ is_inside_angle()

◆ merge_and_put_fragment_lists()

◆ merge_fragments()

◆ near_point()

◆ new_max_point()

◆ new_min_point()

◆ pick_close_point()

◆ pick_good_seam()

◆ point_priority()

◆ prioritize_points()

◆ ProcessSegSearchPainPoint()

◆ program_editdown()

◆ program_editup()

◆ ResetNGramSearch()

◆ SaveAltChoices()

◆ SegSearch()

◆ SegSearchDone()

◆ select_blob_to_split()

◆ select_blob_to_split_from_fixpt()

◆ set_pass1()

◆ set_pass2()

◆ try_point_pairs()

◆ try_vertical_splits()

◆ UpdateSegSearchNodes()

◆ vertical_projection_point()

◆ WordSearch()

Member Data Documentation

◆ assume_fixed_pitch_char_segment

◆ blame_reasons_

◆ chop_center_knob

◆ chop_centered_maxwidth

◆ chop_debug

◆ chop_enable

◆ chop_good_split

◆ chop_inside_angle

◆ chop_min_outline_area

◆ chop_min_outline_points

◆ chop_new_seam_pile

◆ chop_ok_split

◆ chop_overlap_knob

◆ chop_same_distance

◆ chop_seam_pile_size

◆ chop_sharpness_knob

◆ chop_split_dist_knob