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tesseract/ccmain/tesseractclass.h
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///////////////////////////////////////////////////////////////////////
// File: tesseractclass.h
// Description: The Tesseract class. It holds/owns everything needed
// to run Tesseract on a single language, and also a set of
// sub-Tesseracts to run sub-languages. For thread safety, *every*
// global variable goes in here, directly, or indirectly.
// This makes it safe to run multiple Tesseracts in different
// threads in parallel, and keeps the different language
// instances separate.
// Author: Ray Smith
// Created: Fri Mar 07 08:17:01 PST 2008
//
// (C) Copyright 2008, Google Inc.
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
///////////////////////////////////////////////////////////////////////
#ifndef TESSERACT_CCMAIN_TESSERACTCLASS_H_
#define TESSERACT_CCMAIN_TESSERACTCLASS_H_
#include "allheaders.h"
#include "control.h"
#include "debugpixa.h"
#include "devanagari_processing.h"
#include "docqual.h"
#include "genericvector.h"
#include "ocrclass.h"
#include "params.h"
#include "textord.h"
#include "wordrec.h"
class BLOB_CHOICE_LIST_CLIST;
class BLOCK_LIST;
struct OSResults;
class PAGE_RES;
class PAGE_RES_IT;
struct Pix;
class ROW;
class SVMenuNode;
class TBOX;
class TO_BLOCK_LIST;
class WERD;
class WERD_CHOICE;
class WERD_RES;
// Top-level class for all tesseract global instance data.
// This class either holds or points to all data used by an instance
// of Tesseract, including the memory allocator. When this is
// complete, Tesseract will be thread-safe. UNTIL THEN, IT IS NOT!
//
// NOTE to developers: Do not create cyclic dependencies through this class!
// The directory dependency tree must remain a tree! The keep this clean,
// lower-level code (eg in ccutil, the bottom level) must never need to
// know about the content of a higher-level directory.
// The following scheme will grant the easiest access to lower-level
// global members without creating a cyclic dependency:
//
// Class Hierarchy (^ = inheritance):
//
// CCUtil (ccutil/ccutil.h)
// ^ Members include: UNICHARSET
// CUtil (cutil/cutil_class.h)
// ^ Members include: TBLOB*, TEXTBLOCK*
// CCStruct (ccstruct/ccstruct.h)
// ^ Members include: Image
// Classify (classify/classify.h)
// ^ Members include: Dict
// WordRec (wordrec/wordrec.h)
// ^ Members include: WERD*, DENORM*
// Tesseract (ccmain/tesseractclass.h)
// Members include: Pix*
//
// Other important classes:
//
// TessBaseAPI (api/baseapi.h)
// Members include: BLOCK_LIST*, PAGE_RES*,
// Tesseract*, ImageThresholder*
// Dict (dict/dict.h)
// Members include: Image* (private)
//
// NOTE: that each level contains members that correspond to global
// data that is defined (and used) at that level, not necessarily where
// the type is defined so for instance:
// BOOL_VAR_H(textord_show_blobs, false, "Display unsorted blobs");
// goes inside the Textord class, not the cc_util class.
namespace tesseract {
class ColumnFinder;
class DocumentData;
class EquationDetect;
class ImageData;
class LSTMRecognizer;
class Tesseract;
// A collection of various variables for statistics and debugging.
struct TesseractStats {
TesseractStats()
: adaption_word_number(0),
doc_blob_quality(0),
doc_outline_errs(0),
doc_char_quality(0),
good_char_count(0),
doc_good_char_quality(0),
word_count(0),
dict_words(0),
tilde_crunch_written(false),
last_char_was_newline(true),
last_char_was_tilde(false),
write_results_empty_block(true) {}
inT32 adaption_word_number;
inT16 doc_blob_quality;
inT16 doc_outline_errs;
inT16 doc_char_quality;
inT16 good_char_count;
inT16 doc_good_char_quality;
inT32 word_count; // count of word in the document
inT32 dict_words; // number of dicitionary words in the document
STRING dump_words_str; // accumulator used by dump_words()
// Flags used by write_results()
bool tilde_crunch_written;
bool last_char_was_newline;
bool last_char_was_tilde;
bool write_results_empty_block;
};
// Struct to hold all the pointers to relevant data for processing a word.
struct WordData {
WordData() : word(NULL), row(NULL), block(NULL), prev_word(NULL) {}
explicit WordData(const PAGE_RES_IT& page_res_it)
: word(page_res_it.word()), row(page_res_it.row()->row),
block(page_res_it.block()->block), prev_word(NULL) {}
WordData(BLOCK* block_in, ROW* row_in, WERD_RES* word_res)
: word(word_res), row(row_in), block(block_in), prev_word(NULL) {}
WERD_RES* word;
ROW* row;
BLOCK* block;
WordData* prev_word;
PointerVector<WERD_RES> lang_words;
};
// Definition of a Tesseract WordRecognizer. The WordData provides the context
// of row/block, in_word holds an initialized, possibly pre-classified word,
// that the recognizer may or may not consume (but if so it sets *in_word=NULL)
// and produces one or more output words in out_words, which may be the
// consumed in_word, or may be generated independently.
// This api allows both a conventional tesseract classifier to work, or a
// line-level classifier that generates multiple words from a merged input.
typedef void (Tesseract::*WordRecognizer)(const WordData& word_data,
WERD_RES** in_word,
PointerVector<WERD_RES>* out_words);
class Tesseract : public Wordrec {
public:
Tesseract();
~Tesseract();
// Return appropriate dictionary
Dict& getDict() override;
// Clear as much used memory as possible without resetting the adaptive
// classifier or losing any other classifier data.
void Clear();
// Clear all memory of adaption for this and all subclassifiers.
void ResetAdaptiveClassifier();
// Clear the document dictionary for this and all subclassifiers.
void ResetDocumentDictionary();
// Set the equation detector.
void SetEquationDetect(EquationDetect* detector);
// Simple accessors.
const FCOORD& reskew() const {
return reskew_;
}
// Destroy any existing pix and return a pointer to the pointer.
Pix** mutable_pix_binary() {
pixDestroy(&pix_binary_);
return &pix_binary_;
}
Pix* pix_binary() const {
return pix_binary_;
}
Pix* pix_grey() const {
return pix_grey_;
}
void set_pix_grey(Pix* grey_pix) {
pixDestroy(&pix_grey_);
pix_grey_ = grey_pix;
}
Pix* pix_original() const { return pix_original_; }
// Takes ownership of the given original_pix.
void set_pix_original(Pix* original_pix) {
pixDestroy(&pix_original_);
pix_original_ = original_pix;
// Clone to sublangs as well.
for (int i = 0; i < sub_langs_.size(); ++i)
sub_langs_[i]->set_pix_original(original_pix ? pixClone(original_pix)
: nullptr);
}
// Returns a pointer to a Pix representing the best available resolution image
// of the page, with best available bit depth as second priority. Result can
// be of any bit depth, but never color-mapped, as that has always been
// removed. Note that in grey and color, 0 is black and 255 is
// white. If the input was binary, then black is 1 and white is 0.
// To tell the difference pixGetDepth() will return 32, 8 or 1.
// In any case, the return value is a borrowed Pix, and should not be
// deleted or pixDestroyed.
Pix* BestPix() const {
if (pixGetWidth(pix_original_) == ImageWidth())
return pix_original_;
else if (pix_grey_ != NULL)
return pix_grey_;
else
return pix_binary_;
}
void set_pix_thresholds(Pix* thresholds) {
pixDestroy(&pix_thresholds_);
pix_thresholds_ = thresholds;
}
int source_resolution() const {
return source_resolution_;
}
void set_source_resolution(int ppi) {
source_resolution_ = ppi;
}
int ImageWidth() const {
return pixGetWidth(pix_binary_);
}
int ImageHeight() const {
return pixGetHeight(pix_binary_);
}
Pix* scaled_color() const {
return scaled_color_;
}
int scaled_factor() const {
return scaled_factor_;
}
void SetScaledColor(int factor, Pix* color) {
scaled_factor_ = factor;
scaled_color_ = color;
}
const Textord& textord() const {
return textord_;
}
Textord* mutable_textord() {
return &textord_;
}
bool right_to_left() const {
return right_to_left_;
}
int num_sub_langs() const {
return sub_langs_.size();
}
Tesseract* get_sub_lang(int index) const {
return sub_langs_[index];
}
// Returns true if any language uses Tesseract (as opposed to LSTM).
bool AnyTessLang() const {
if (tessedit_ocr_engine_mode != OEM_LSTM_ONLY) return true;
for (int i = 0; i < sub_langs_.size(); ++i) {
if (sub_langs_[i]->tessedit_ocr_engine_mode != OEM_LSTM_ONLY) return true;
}
return false;
}
// Returns true if any language uses the LSTM.
bool AnyLSTMLang() const {
if (tessedit_ocr_engine_mode != OEM_TESSERACT_ONLY) return true;
for (int i = 0; i < sub_langs_.size(); ++i) {
if (sub_langs_[i]->tessedit_ocr_engine_mode != OEM_TESSERACT_ONLY)
return true;
}
return false;
}
void SetBlackAndWhitelist();
// Perform steps to prepare underlying binary image/other data structures for
// page segmentation. Uses the strategy specified in the global variable
// pageseg_devanagari_split_strategy for perform splitting while preparing for
// page segmentation.
void PrepareForPageseg();
// Perform steps to prepare underlying binary image/other data structures for
// Tesseract OCR. The current segmentation is required by this method.
// Uses the strategy specified in the global variable
// ocr_devanagari_split_strategy for performing splitting while preparing for
// Tesseract ocr.
void PrepareForTessOCR(BLOCK_LIST* block_list,
Tesseract* osd_tess, OSResults* osr);
int SegmentPage(const STRING* input_file, BLOCK_LIST* blocks,
Tesseract* osd_tess, OSResults* osr);
void SetupWordScripts(BLOCK_LIST* blocks);
int AutoPageSeg(PageSegMode pageseg_mode, BLOCK_LIST* blocks,
TO_BLOCK_LIST* to_blocks, BLOBNBOX_LIST* diacritic_blobs,
Tesseract* osd_tess, OSResults* osr);
ColumnFinder* SetupPageSegAndDetectOrientation(
PageSegMode pageseg_mode, BLOCK_LIST* blocks, Tesseract* osd_tess,
OSResults* osr, TO_BLOCK_LIST* to_blocks, Pix** photo_mask_pix,
Pix** music_mask_pix);
// par_control.cpp
void PrerecAllWordsPar(const GenericVector<WordData>& words);
//// linerec.cpp
// Generates training data for training a line recognizer, eg LSTM.
// Breaks the page into lines, according to the boxes, and writes them to a
// serialized DocumentData based on output_basename.
void TrainLineRecognizer(const STRING& input_imagename,
const STRING& output_basename,
BLOCK_LIST *block_list);
// Generates training data for training a line recognizer, eg LSTM.
// Breaks the boxes into lines, normalizes them, converts to ImageData and
// appends them to the given training_data.
void TrainFromBoxes(const GenericVector<TBOX>& boxes,
const GenericVector<STRING>& texts,
BLOCK_LIST *block_list,
DocumentData* training_data);
// Returns an Imagedata containing the image of the given textline,
// and ground truth boxes/truth text if available in the input.
// The image is not normalized in any way.
ImageData* GetLineData(const TBOX& line_box,
const GenericVector<TBOX>& boxes,
const GenericVector<STRING>& texts,
int start_box, int end_box,
const BLOCK& block);
// Helper gets the image of a rectangle, using the block.re_rotation() if
// needed to get to the image, and rotating the result back to horizontal
// layout. (CJK characters will be on their left sides) The vertical text flag
// is set in the returned ImageData if the text was originally vertical, which
// can be used to invoke a different CJK recognition engine. The revised_box
// is also returned to enable calculation of output bounding boxes.
ImageData* GetRectImage(const TBOX& box, const BLOCK& block, int padding,
TBOX* revised_box) const;
// Recognizes a word or group of words, converting to WERD_RES in *words.
// Analogous to classify_word_pass1, but can handle a group of words as well.
void LSTMRecognizeWord(const BLOCK& block, ROW *row, WERD_RES *word,
PointerVector<WERD_RES>* words);
// Apply segmentation search to the given set of words, within the constraints
// of the existing ratings matrix. If there is already a best_choice on a word
// leaves it untouched and just sets the done/accepted etc flags.
void SearchWords(PointerVector<WERD_RES>* words);
//// control.h /////////////////////////////////////////////////////////
bool ProcessTargetWord(const TBOX& word_box, const TBOX& target_word_box,
const char* word_config, int pass);
// Sets up the words ready for whichever engine is to be run
void SetupAllWordsPassN(int pass_n,
const TBOX* target_word_box,
const char* word_config,
PAGE_RES* page_res,
GenericVector<WordData>* words);
// Sets up the single word ready for whichever engine is to be run.
void SetupWordPassN(int pass_n, WordData* word);
// Runs word recognition on all the words.
bool RecogAllWordsPassN(int pass_n, ETEXT_DESC* monitor,
PAGE_RES_IT* pr_it,
GenericVector<WordData>* words);
bool recog_all_words(PAGE_RES* page_res,
ETEXT_DESC* monitor,
const TBOX* target_word_box,
const char* word_config,
int dopasses);
void rejection_passes(PAGE_RES* page_res,
ETEXT_DESC* monitor,
const TBOX* target_word_box,
const char* word_config);
void bigram_correction_pass(PAGE_RES *page_res);
void blamer_pass(PAGE_RES* page_res);
// Sets script positions and detects smallcaps on all output words.
void script_pos_pass(PAGE_RES* page_res);
// Helper to recognize the word using the given (language-specific) tesseract.
// Returns positive if this recognizer found more new best words than the
// number kept from best_words.
int RetryWithLanguage(const WordData& word_data, WordRecognizer recognizer,
bool debug, WERD_RES** in_word,
PointerVector<WERD_RES>* best_words);
// Moves good-looking "noise"/diacritics from the reject list to the main
// blob list on the current word. Returns true if anything was done, and
// sets make_next_word_fuzzy if blob(s) were added to the end of the word.
bool ReassignDiacritics(int pass, PAGE_RES_IT* pr_it,
bool* make_next_word_fuzzy);
// Attempts to put noise/diacritic outlines into the blobs that they overlap.
// Input: a set of noisy outlines that probably belong to the real_word.
// Output: outlines that overlapped blobs are set to NULL and put back into
// the word, either in the blobs or in the reject list.
void AssignDiacriticsToOverlappingBlobs(
const GenericVector<C_OUTLINE*>& outlines, int pass, WERD* real_word,
PAGE_RES_IT* pr_it, GenericVector<bool>* word_wanted,
GenericVector<bool>* overlapped_any_blob,
GenericVector<C_BLOB*>* target_blobs);
// Attempts to assign non-overlapping outlines to their nearest blobs or
// make new blobs out of them.
void AssignDiacriticsToNewBlobs(const GenericVector<C_OUTLINE*>& outlines,
int pass, WERD* real_word, PAGE_RES_IT* pr_it,
GenericVector<bool>* word_wanted,
GenericVector<C_BLOB*>* target_blobs);
// Starting with ok_outlines set to indicate which outlines overlap the blob,
// chooses the optimal set (approximately) and returns true if any outlines
// are desired, in which case ok_outlines indicates which ones.
bool SelectGoodDiacriticOutlines(int pass, float certainty_threshold,
PAGE_RES_IT* pr_it, C_BLOB* blob,
const GenericVector<C_OUTLINE*>& outlines,
int num_outlines,
GenericVector<bool>* ok_outlines);
// Classifies the given blob plus the outlines flagged by ok_outlines, undoes
// the inclusion of the outlines, and returns the certainty of the raw choice.
float ClassifyBlobPlusOutlines(const GenericVector<bool>& ok_outlines,
const GenericVector<C_OUTLINE*>& outlines,
int pass_n, PAGE_RES_IT* pr_it, C_BLOB* blob,
STRING* best_str);
// Classifies the given blob (part of word_data->word->word) as an individual
// word, using languages, chopper etc, returning only the certainty of the
// best raw choice, and undoing all the work done to fake out the word.
float ClassifyBlobAsWord(int pass_n, PAGE_RES_IT* pr_it, C_BLOB* blob,
STRING* best_str, float* c2);
void classify_word_and_language(int pass_n, PAGE_RES_IT* pr_it,
WordData* word_data);
void classify_word_pass1(const WordData& word_data,
WERD_RES** in_word,
PointerVector<WERD_RES>* out_words);
void recog_pseudo_word(PAGE_RES* page_res, // blocks to check
TBOX &selection_box);
void fix_rep_char(PAGE_RES_IT* page_res_it);
ACCEPTABLE_WERD_TYPE acceptable_word_string(const UNICHARSET& char_set,
const char *s,
const char *lengths);
void match_word_pass_n(int pass_n, WERD_RES *word, ROW *row, BLOCK* block);
void classify_word_pass2(const WordData& word_data,
WERD_RES** in_word,
PointerVector<WERD_RES>* out_words);
void ReportXhtFixResult(bool accept_new_word, float new_x_ht,
WERD_RES* word, WERD_RES* new_word);
bool RunOldFixXht(WERD_RES *word, BLOCK* block, ROW *row);
bool TrainedXheightFix(WERD_RES *word, BLOCK* block, ROW *row);
// Runs recognition with the test baseline shift and x-height and returns true
// if there was an improvement in recognition result.
bool TestNewNormalization(int original_misfits, float baseline_shift,
float new_x_ht, WERD_RES *word, BLOCK* block,
ROW *row);
BOOL8 recog_interactive(PAGE_RES_IT* pr_it);
// Set fonts of this word.
void set_word_fonts(WERD_RES *word);
void font_recognition_pass(PAGE_RES* page_res);
void dictionary_correction_pass(PAGE_RES* page_res);
BOOL8 check_debug_pt(WERD_RES *word, int location);
//// superscript.cpp ////////////////////////////////////////////////////
bool SubAndSuperscriptFix(WERD_RES *word_res);
void GetSubAndSuperscriptCandidates(const WERD_RES *word,
int *num_rebuilt_leading,
ScriptPos *leading_pos,
float *leading_certainty,
int *num_rebuilt_trailing,
ScriptPos *trailing_pos,
float *trailing_certainty,
float *avg_certainty,
float *unlikely_threshold);
WERD_RES *TrySuperscriptSplits(int num_chopped_leading,
float leading_certainty,
ScriptPos leading_pos,
int num_chopped_trailing,
float trailing_certainty,
ScriptPos trailing_pos,
WERD_RES *word,
bool *is_good,
int *retry_leading,
int *retry_trailing);
bool BelievableSuperscript(bool debug,
const WERD_RES &word,
float certainty_threshold,
int *left_ok,
int *right_ok) const;
//// output.h //////////////////////////////////////////////////////////
void output_pass(PAGE_RES_IT &page_res_it, const TBOX *target_word_box);
void write_results(PAGE_RES_IT &page_res_it, // full info
char newline_type, // type of newline
BOOL8 force_eol // override tilde crunch?
);
void set_unlv_suspects(WERD_RES *word);
UNICHAR_ID get_rep_char(WERD_RES *word); // what char is repeated?
BOOL8 acceptable_number_string(const char *s,
const char *lengths);
inT16 count_alphanums(const WERD_CHOICE &word);
inT16 count_alphas(const WERD_CHOICE &word);
//// tessedit.h ////////////////////////////////////////////////////////
void read_config_file(const char *filename, SetParamConstraint constraint);
// Initialize for potentially a set of languages defined by the language
// string and recursively any additional languages required by any language
// traineddata file (via tessedit_load_sublangs in its config) that is loaded.
// See init_tesseract_internal for args.
int init_tesseract(const char* arg0, const char* textbase,
const char* language, OcrEngineMode oem, char** configs,
int configs_size, const GenericVector<STRING>* vars_vec,
const GenericVector<STRING>* vars_values,
bool set_only_init_params, TessdataManager* mgr);
int init_tesseract(const char *datapath,
const char *language,
OcrEngineMode oem) {
TessdataManager mgr;
return init_tesseract(datapath, NULL, language, oem, NULL, 0, NULL, NULL,
false, &mgr);
}
// Common initialization for a single language.
// arg0 is the datapath for the tessdata directory, which could be the
// path of the tessdata directory with no trailing /, or (if tessdata
// lives in the same directory as the executable, the path of the executable,
// hence the name arg0.
// textbase is an optional output file basename (used only for training)
// language is the language code to load.
// oem controls which engine(s) will operate on the image
// configs (argv) is an array of config filenames to load variables from.
// May be NULL.
// configs_size (argc) is the number of elements in configs.
// vars_vec is an optional vector of variables to set.
// vars_values is an optional corresponding vector of values for the variables
// in vars_vec.
// If set_only_init_params is true, then only the initialization variables
// will be set.
int init_tesseract_internal(const char* arg0, const char* textbase,
const char* language, OcrEngineMode oem,
char** configs, int configs_size,
const GenericVector<STRING>* vars_vec,
const GenericVector<STRING>* vars_values,
bool set_only_init_params, TessdataManager* mgr);
// Set the universal_id member of each font to be unique among all
// instances of the same font loaded.
void SetupUniversalFontIds();
int init_tesseract_lm(const char* arg0, const char* textbase,
const char* language, TessdataManager* mgr);
void recognize_page(STRING& image_name);
void end_tesseract();
bool init_tesseract_lang_data(const char* arg0, const char* textbase,
const char* language, OcrEngineMode oem,
char** configs, int configs_size,
const GenericVector<STRING>* vars_vec,
const GenericVector<STRING>* vars_values,
bool set_only_init_params,
TessdataManager* mgr);
void ParseLanguageString(const char* lang_str,
GenericVector<STRING>* to_load,
GenericVector<STRING>* not_to_load);
//// pgedit.h //////////////////////////////////////////////////////////
SVMenuNode *build_menu_new();
#ifndef GRAPHICS_DISABLED
void pgeditor_main(int width, int height, PAGE_RES* page_res);
#endif // GRAPHICS_DISABLED
void process_image_event( // action in image win
const SVEvent &event);
BOOL8 process_cmd_win_event( // UI command semantics
inT32 cmd_event, // which menu item?
char *new_value // any prompt data
);
void debug_word(PAGE_RES* page_res, const TBOX &selection_box);
void do_re_display(
BOOL8 (tesseract::Tesseract::*word_painter)(PAGE_RES_IT* pr_it));
BOOL8 word_display(PAGE_RES_IT* pr_it);
BOOL8 word_bln_display(PAGE_RES_IT* pr_it);
BOOL8 word_blank_and_set_display(PAGE_RES_IT* pr_its);
BOOL8 word_set_display(PAGE_RES_IT* pr_it);
// #ifndef GRAPHICS_DISABLED
BOOL8 word_dumper(PAGE_RES_IT* pr_it);
// #endif // GRAPHICS_DISABLED
void blob_feature_display(PAGE_RES* page_res, const TBOX& selection_box);
//// reject.h //////////////////////////////////////////////////////////
// make rej map for word
void make_reject_map(WERD_RES *word, ROW *row, inT16 pass);
BOOL8 one_ell_conflict(WERD_RES *word_res, BOOL8 update_map);
inT16 first_alphanum_index(const char *word,
const char *word_lengths);
inT16 first_alphanum_offset(const char *word,
const char *word_lengths);
inT16 alpha_count(const char *word,
const char *word_lengths);
BOOL8 word_contains_non_1_digit(const char *word,
const char *word_lengths);
void dont_allow_1Il(WERD_RES *word);
inT16 count_alphanums( //how many alphanums
WERD_RES *word);
void flip_0O(WERD_RES *word);
BOOL8 non_0_digit(const UNICHARSET& ch_set, UNICHAR_ID unichar_id);
BOOL8 non_O_upper(const UNICHARSET& ch_set, UNICHAR_ID unichar_id);
BOOL8 repeated_nonalphanum_wd(WERD_RES *word, ROW *row);
void nn_match_word( //Match a word
WERD_RES *word,
ROW *row);
void nn_recover_rejects(WERD_RES *word, ROW *row);
void set_done( //set done flag
WERD_RES *word,
inT16 pass);
inT16 safe_dict_word(const WERD_RES *werd_res); // is best_choice in dict?
void flip_hyphens(WERD_RES *word);
void reject_I_1_L(WERD_RES *word);
void reject_edge_blobs(WERD_RES *word);
void reject_mostly_rejects(WERD_RES *word);
//// adaptions.h ///////////////////////////////////////////////////////
BOOL8 word_adaptable( //should we adapt?
WERD_RES *word,
uinT16 mode);
//// tfacepp.cpp ///////////////////////////////////////////////////////
void recog_word_recursive(WERD_RES* word);
void recog_word(WERD_RES *word);
void split_and_recog_word(WERD_RES* word);
void split_word(WERD_RES *word,
int split_pt,
WERD_RES **right_piece,
BlamerBundle **orig_blamer_bundle) const;
void join_words(WERD_RES *word,
WERD_RES *word2,
BlamerBundle *orig_bb) const;
//// fixspace.cpp ///////////////////////////////////////////////////////
BOOL8 digit_or_numeric_punct(WERD_RES *word, int char_position);
inT16 eval_word_spacing(WERD_RES_LIST &word_res_list);
void match_current_words(WERD_RES_LIST &words, ROW *row, BLOCK* block);
inT16 fp_eval_word_spacing(WERD_RES_LIST &word_res_list);
void fix_noisy_space_list(WERD_RES_LIST &best_perm, ROW *row, BLOCK* block);
void fix_fuzzy_space_list(WERD_RES_LIST &best_perm, ROW *row, BLOCK* block);
void fix_sp_fp_word(WERD_RES_IT &word_res_it, ROW *row, BLOCK* block);
void fix_fuzzy_spaces( //find fuzzy words
ETEXT_DESC *monitor, //progress monitor
inT32 word_count, //count of words in doc
PAGE_RES *page_res);
void dump_words(WERD_RES_LIST &perm, inT16 score,
inT16 mode, BOOL8 improved);
BOOL8 fixspace_thinks_word_done(WERD_RES *word);
inT16 worst_noise_blob(WERD_RES *word_res, float *worst_noise_score);
float blob_noise_score(TBLOB *blob);
void break_noisiest_blob_word(WERD_RES_LIST &words);
//// docqual.cpp ////////////////////////////////////////////////////////
GARBAGE_LEVEL garbage_word(WERD_RES *word, BOOL8 ok_dict_word);
BOOL8 potential_word_crunch(WERD_RES *word,
GARBAGE_LEVEL garbage_level,
BOOL8 ok_dict_word);
void tilde_crunch(PAGE_RES_IT &page_res_it);
void unrej_good_quality_words( //unreject potential
PAGE_RES_IT &page_res_it);
void doc_and_block_rejection( //reject big chunks
PAGE_RES_IT &page_res_it,
BOOL8 good_quality_doc);
void quality_based_rejection(PAGE_RES_IT &page_res_it,
BOOL8 good_quality_doc);
void convert_bad_unlv_chs(WERD_RES *word_res);
void tilde_delete(PAGE_RES_IT &page_res_it);
inT16 word_blob_quality(WERD_RES *word, ROW *row);
void word_char_quality(WERD_RES *word, ROW *row, inT16 *match_count,
inT16 *accepted_match_count);
void unrej_good_chs(WERD_RES *word, ROW *row);
inT16 count_outline_errs(char c, inT16 outline_count);
inT16 word_outline_errs(WERD_RES *word);
BOOL8 terrible_word_crunch(WERD_RES *word, GARBAGE_LEVEL garbage_level);
CRUNCH_MODE word_deletable(WERD_RES *word, inT16 &delete_mode);
inT16 failure_count(WERD_RES *word);
BOOL8 noise_outlines(TWERD *word);
//// pagewalk.cpp ///////////////////////////////////////////////////////
void
process_selected_words (
PAGE_RES* page_res, // blocks to check
//function to call
TBOX & selection_box,
BOOL8 (tesseract::Tesseract::*word_processor)(PAGE_RES_IT* pr_it));
//// tessbox.cpp ///////////////////////////////////////////////////////
void tess_add_doc_word( //test acceptability
WERD_CHOICE *word_choice //after context
);
void tess_segment_pass_n(int pass_n, WERD_RES *word);
bool tess_acceptable_word(WERD_RES *word);
//// applybox.cpp //////////////////////////////////////////////////////
// Applies the box file based on the image name fname, and resegments
// the words in the block_list (page), with:
// blob-mode: one blob per line in the box file, words as input.
// word/line-mode: one blob per space-delimited unit after the #, and one word
// per line in the box file. (See comment above for box file format.)
// If find_segmentation is true, (word/line mode) then the classifier is used
// to re-segment words/lines to match the space-delimited truth string for
// each box. In this case, the input box may be for a word or even a whole
// text line, and the output words will contain multiple blobs corresponding
// to the space-delimited input string.
// With find_segmentation false, no classifier is needed, but the chopper
// can still be used to correctly segment touching characters with the help
// of the input boxes.
// In the returned PAGE_RES, the WERD_RES are setup as they would be returned
// from normal classification, ie. with a word, chopped_word, rebuild_word,
// seam_array, denorm, box_word, and best_state, but NO best_choice or
// raw_choice, as they would require a UNICHARSET, which we aim to avoid.
// Instead, the correct_text member of WERD_RES is set, and this may be later
// converted to a best_choice using CorrectClassifyWords. CorrectClassifyWords
// is not required before calling ApplyBoxTraining.
PAGE_RES* ApplyBoxes(const STRING& fname, bool find_segmentation,
BLOCK_LIST *block_list);
// Any row xheight that is significantly different from the median is set
// to the median.
void PreenXHeights(BLOCK_LIST *block_list);
// Builds a PAGE_RES from the block_list in the way required for ApplyBoxes:
// All fuzzy spaces are removed, and all the words are maximally chopped.
PAGE_RES* SetupApplyBoxes(const GenericVector<TBOX>& boxes,
BLOCK_LIST *block_list);
// Tests the chopper by exhaustively running chop_one_blob.
// The word_res will contain filled chopped_word, seam_array, denorm,
// box_word and best_state for the maximally chopped word.
void MaximallyChopWord(const GenericVector<TBOX>& boxes,
BLOCK* block, ROW* row, WERD_RES* word_res);
// Gather consecutive blobs that match the given box into the best_state
// and corresponding correct_text.
// Fights over which box owns which blobs are settled by pre-chopping and
// applying the blobs to box or next_box with the least non-overlap.
// Returns false if the box was in error, which can only be caused by
// failing to find an appropriate blob for a box.
// This means that occasionally, blobs may be incorrectly segmented if the
// chopper fails to find a suitable chop point.
bool ResegmentCharBox(PAGE_RES* page_res, const TBOX *prev_box,
const TBOX& box, const TBOX& next_box,
const char* correct_text);
// Consume all source blobs that strongly overlap the given box,
// putting them into a new word, with the correct_text label.
// Fights over which box owns which blobs are settled by
// applying the blobs to box or next_box with the least non-overlap.
// Returns false if the box was in error, which can only be caused by
// failing to find an overlapping blob for a box.
bool ResegmentWordBox(BLOCK_LIST *block_list,
const TBOX& box, const TBOX& next_box,
const char* correct_text);
// Resegments the words by running the classifier in an attempt to find the
// correct segmentation that produces the required string.
void ReSegmentByClassification(PAGE_RES* page_res);
// Converts the space-delimited string of utf8 text to a vector of UNICHAR_ID.
// Returns false if an invalid UNICHAR_ID is encountered.
bool ConvertStringToUnichars(const char* utf8,
GenericVector<UNICHAR_ID>* class_ids);
// Resegments the word to achieve the target_text from the classifier.
// Returns false if the re-segmentation fails.
// Uses brute-force combination of up to kMaxGroupSize adjacent blobs, and
// applies a full search on the classifier results to find the best classified
// segmentation. As a compromise to obtain better recall, 1-1 ambigiguity
// substitutions ARE used.
bool FindSegmentation(const GenericVector<UNICHAR_ID>& target_text,
WERD_RES* word_res);
// Recursive helper to find a match to the target_text (from text_index
// position) in the choices (from choices_pos position).
// Choices is an array of GenericVectors, of length choices_length, with each
// element representing a starting position in the word, and the
// GenericVector holding classification results for a sequence of consecutive
// blobs, with index 0 being a single blob, index 1 being 2 blobs etc.
void SearchForText(const GenericVector<BLOB_CHOICE_LIST*>* choices,
int choices_pos, int choices_length,
const GenericVector<UNICHAR_ID>& target_text,
int text_index,
float rating, GenericVector<int>* segmentation,
float* best_rating, GenericVector<int>* best_segmentation);
// Counts up the labelled words and the blobs within.
// Deletes all unused or emptied words, counting the unused ones.
// Resets W_BOL and W_EOL flags correctly.
// Builds the rebuild_word and rebuilds the box_word.
void TidyUp(PAGE_RES* page_res);
// Logs a bad box by line in the box file and box coords.
void ReportFailedBox(int boxfile_lineno, TBOX box, const char *box_ch,
const char *err_msg);
// Creates a fake best_choice entry in each WERD_RES with the correct text.
void CorrectClassifyWords(PAGE_RES* page_res);
// Call LearnWord to extract features for labelled blobs within each word.
// Features are stored in an internal buffer.
void ApplyBoxTraining(const STRING& fontname, PAGE_RES* page_res);
//// fixxht.cpp ///////////////////////////////////////////////////////
// Returns the number of misfit blob tops in this word.
int CountMisfitTops(WERD_RES *word_res);
// Returns a new x-height in pixels (original image coords) that is
// maximally compatible with the result in word_res.
// Returns 0.0f if no x-height is found that is better than the current
// estimate.
float ComputeCompatibleXheight(WERD_RES *word_res, float* baseline_shift);
//// Data members ///////////////////////////////////////////////////////
// TODO(ocr-team): Find and remove obsolete parameters.
BOOL_VAR_H(tessedit_resegment_from_boxes, false,
"Take segmentation and labeling from box file");
BOOL_VAR_H(tessedit_resegment_from_line_boxes, false,
"Conversion of word/line box file to char box file");
BOOL_VAR_H(tessedit_train_from_boxes, false,
"Generate training data from boxed chars");
BOOL_VAR_H(tessedit_make_boxes_from_boxes, false,
"Generate more boxes from boxed chars");
BOOL_VAR_H(tessedit_train_line_recognizer, false,
"Break input into lines and remap boxes if present");
BOOL_VAR_H(tessedit_dump_pageseg_images, false,
"Dump intermediate images made during page segmentation");
INT_VAR_H(tessedit_pageseg_mode, PSM_SINGLE_BLOCK,
"Page seg mode: 0=osd only, 1=auto+osd, 2=auto, 3=col, 4=block,"
" 5=line, 6=word, 7=char"
" (Values from PageSegMode enum in publictypes.h)");
INT_VAR_H(tessedit_ocr_engine_mode, tesseract::OEM_DEFAULT,
"Which OCR engine(s) to run (Tesseract, LSTM, both). Defaults"
" to loading and running the most accurate available.");
STRING_VAR_H(tessedit_char_blacklist, "",
"Blacklist of chars not to recognize");
STRING_VAR_H(tessedit_char_whitelist, "",
"Whitelist of chars to recognize");
STRING_VAR_H(tessedit_char_unblacklist, "",
"List of chars to override tessedit_char_blacklist");
BOOL_VAR_H(tessedit_ambigs_training, false,
"Perform training for ambiguities");
INT_VAR_H(pageseg_devanagari_split_strategy,
tesseract::ShiroRekhaSplitter::NO_SPLIT,
"Whether to use the top-line splitting process for Devanagari "
"documents while performing page-segmentation.");
INT_VAR_H(ocr_devanagari_split_strategy,
tesseract::ShiroRekhaSplitter::NO_SPLIT,
"Whether to use the top-line splitting process for Devanagari "
"documents while performing ocr.");
STRING_VAR_H(tessedit_write_params_to_file, "",
"Write all parameters to the given file.");
BOOL_VAR_H(tessedit_adaption_debug, false,
"Generate and print debug information for adaption");
INT_VAR_H(bidi_debug, 0, "Debug level for BiDi");
INT_VAR_H(applybox_debug, 1, "Debug level");
INT_VAR_H(applybox_page, 0, "Page number to apply boxes from");
STRING_VAR_H(applybox_exposure_pattern, ".exp",
"Exposure value follows this pattern in the image"
" filename. The name of the image files are expected"
" to be in the form [lang].[fontname].exp[num].tif");
BOOL_VAR_H(applybox_learn_chars_and_char_frags_mode, false,
"Learn both character fragments (as is done in the"
" special low exposure mode) as well as unfragmented"
" characters.");
BOOL_VAR_H(applybox_learn_ngrams_mode, false,
"Each bounding box is assumed to contain ngrams. Only"
" learn the ngrams whose outlines overlap horizontally.");
BOOL_VAR_H(tessedit_display_outwords, false, "Draw output words");
BOOL_VAR_H(tessedit_dump_choices, false, "Dump char choices");
BOOL_VAR_H(tessedit_timing_debug, false, "Print timing stats");
BOOL_VAR_H(tessedit_fix_fuzzy_spaces, true,
"Try to improve fuzzy spaces");
BOOL_VAR_H(tessedit_unrej_any_wd, false,
"Don't bother with word plausibility");
BOOL_VAR_H(tessedit_fix_hyphens, true, "Crunch double hyphens?");
BOOL_VAR_H(tessedit_redo_xheight, true, "Check/Correct x-height");
BOOL_VAR_H(tessedit_enable_doc_dict, true,
"Add words to the document dictionary");
BOOL_VAR_H(tessedit_debug_fonts, false, "Output font info per char");
BOOL_VAR_H(tessedit_debug_block_rejection, false, "Block and Row stats");
BOOL_VAR_H(tessedit_enable_bigram_correction, true,
"Enable correction based on the word bigram dictionary.");
BOOL_VAR_H(tessedit_enable_dict_correction, false,
"Enable single word correction based on the dictionary.");
INT_VAR_H(tessedit_bigram_debug, 0, "Amount of debug output for bigram "
"correction.");
BOOL_VAR_H(enable_noise_removal, true,
"Remove and conditionally reassign small outlines when they"
" confuse layout analysis, determining diacritics vs noise");
INT_VAR_H(debug_noise_removal, 0, "Debug reassignment of small outlines");
// Worst (min) certainty, for which a diacritic is allowed to make the base
// character worse and still be included.
double_VAR_H(noise_cert_basechar, -8.0, "Hingepoint for base char certainty");
// Worst (min) certainty, for which a non-overlapping diacritic is allowed to
// make the base character worse and still be included.
double_VAR_H(noise_cert_disjoint, -2.5, "Hingepoint for disjoint certainty");
// Worst (min) certainty, for which a diacritic is allowed to make a new
// stand-alone blob.
double_VAR_H(noise_cert_punc, -2.5, "Threshold for new punc char certainty");
// Factor of certainty margin for adding diacritics to not count as worse.
double_VAR_H(noise_cert_factor, 0.375,
"Scaling on certainty diff from Hingepoint");
INT_VAR_H(noise_maxperblob, 8, "Max diacritics to apply to a blob");
INT_VAR_H(noise_maxperword, 16, "Max diacritics to apply to a word");
INT_VAR_H(debug_x_ht_level, 0, "Reestimate debug");
BOOL_VAR_H(debug_acceptable_wds, false, "Dump word pass/fail chk");
STRING_VAR_H(chs_leading_punct, "('`\"", "Leading punctuation");
STRING_VAR_H(chs_trailing_punct1, ").,;:?!", "1st Trailing punctuation");
STRING_VAR_H(chs_trailing_punct2, ")'`\"", "2nd Trailing punctuation");
double_VAR_H(quality_rej_pc, 0.08, "good_quality_doc lte rejection limit");
double_VAR_H(quality_blob_pc, 0.0, "good_quality_doc gte good blobs limit");
double_VAR_H(quality_outline_pc, 1.0,
"good_quality_doc lte outline error limit");
double_VAR_H(quality_char_pc, 0.95, "good_quality_doc gte good char limit");
INT_VAR_H(quality_min_initial_alphas_reqd, 2, "alphas in a good word");
INT_VAR_H(tessedit_tess_adaption_mode, 0x27,
"Adaptation decision algorithm for tess");
BOOL_VAR_H(tessedit_minimal_rej_pass1, false,
"Do minimal rejection on pass 1 output");
BOOL_VAR_H(tessedit_test_adaption, false, "Test adaption criteria");
BOOL_VAR_H(tessedit_matcher_log, false, "Log matcher activity");
INT_VAR_H(tessedit_test_adaption_mode, 3,
"Adaptation decision algorithm for tess");
BOOL_VAR_H(test_pt, false, "Test for point");
double_VAR_H(test_pt_x, 99999.99, "xcoord");
double_VAR_H(test_pt_y, 99999.99, "ycoord");
INT_VAR_H(multilang_debug_level, 0, "Print multilang debug info.");
INT_VAR_H(paragraph_debug_level, 0, "Print paragraph debug info.");
BOOL_VAR_H(paragraph_text_based, true,
"Run paragraph detection on the post-text-recognition "
"(more accurate)");
BOOL_VAR_H(lstm_use_matrix, 1, "Use ratings matrix/beam searct with lstm");
STRING_VAR_H(outlines_odd, "%| ", "Non standard number of outlines");
STRING_VAR_H(outlines_2, "ij!?%\":;", "Non standard number of outlines");
BOOL_VAR_H(docqual_excuse_outline_errs, false,
"Allow outline errs in unrejection?");
BOOL_VAR_H(tessedit_good_quality_unrej, true,
"Reduce rejection on good docs");
BOOL_VAR_H(tessedit_use_reject_spaces, true, "Reject spaces?");
double_VAR_H(tessedit_reject_doc_percent, 65.00,
"%rej allowed before rej whole doc");
double_VAR_H(tessedit_reject_block_percent, 45.00,
"%rej allowed before rej whole block");
double_VAR_H(tessedit_reject_row_percent, 40.00,
"%rej allowed before rej whole row");
double_VAR_H(tessedit_whole_wd_rej_row_percent, 70.00,
"Number of row rejects in whole word rejects"
"which prevents whole row rejection");
BOOL_VAR_H(tessedit_preserve_blk_rej_perfect_wds, true,
"Only rej partially rejected words in block rejection");
BOOL_VAR_H(tessedit_preserve_row_rej_perfect_wds, true,
"Only rej partially rejected words in row rejection");
BOOL_VAR_H(tessedit_dont_blkrej_good_wds, false,
"Use word segmentation quality metric");
BOOL_VAR_H(tessedit_dont_rowrej_good_wds, false,
"Use word segmentation quality metric");
INT_VAR_H(tessedit_preserve_min_wd_len, 2,
"Only preserve wds longer than this");
BOOL_VAR_H(tessedit_row_rej_good_docs, true,
"Apply row rejection to good docs");
double_VAR_H(tessedit_good_doc_still_rowrej_wd, 1.1,
"rej good doc wd if more than this fraction rejected");
BOOL_VAR_H(tessedit_reject_bad_qual_wds, true,
"Reject all bad quality wds");
BOOL_VAR_H(tessedit_debug_doc_rejection, false, "Page stats");
BOOL_VAR_H(tessedit_debug_quality_metrics, false,
"Output data to debug file");
BOOL_VAR_H(bland_unrej, false, "unrej potential with no checks");
double_VAR_H(quality_rowrej_pc, 1.1,
"good_quality_doc gte good char limit");
BOOL_VAR_H(unlv_tilde_crunching, true,
"Mark v.bad words for tilde crunch");
BOOL_VAR_H(hocr_font_info, false,
"Add font info to hocr output");
BOOL_VAR_H(crunch_early_merge_tess_fails, true, "Before word crunch?");
BOOL_VAR_H(crunch_early_convert_bad_unlv_chs, false, "Take out ~^ early?");
double_VAR_H(crunch_terrible_rating, 80.0, "crunch rating lt this");
BOOL_VAR_H(crunch_terrible_garbage, true, "As it says");
double_VAR_H(crunch_poor_garbage_cert, -9.0,
"crunch garbage cert lt this");
double_VAR_H(crunch_poor_garbage_rate, 60, "crunch garbage rating lt this");
double_VAR_H(crunch_pot_poor_rate, 40, "POTENTIAL crunch rating lt this");
double_VAR_H(crunch_pot_poor_cert, -8.0, "POTENTIAL crunch cert lt this");
BOOL_VAR_H(crunch_pot_garbage, true, "POTENTIAL crunch garbage");
double_VAR_H(crunch_del_rating, 60, "POTENTIAL crunch rating lt this");
double_VAR_H(crunch_del_cert, -10.0, "POTENTIAL crunch cert lt this");
double_VAR_H(crunch_del_min_ht, 0.7, "Del if word ht lt xht x this");
double_VAR_H(crunch_del_max_ht, 3.0, "Del if word ht gt xht x this");
double_VAR_H(crunch_del_min_width, 3.0, "Del if word width lt xht x this");
double_VAR_H(crunch_del_high_word, 1.5,
"Del if word gt xht x this above bl");
double_VAR_H(crunch_del_low_word, 0.5, "Del if word gt xht x this below bl");
double_VAR_H(crunch_small_outlines_size, 0.6, "Small if lt xht x this");
INT_VAR_H(crunch_rating_max, 10, "For adj length in rating per ch");
INT_VAR_H(crunch_pot_indicators, 1, "How many potential indicators needed");
BOOL_VAR_H(crunch_leave_ok_strings, true, "Don't touch sensible strings");
BOOL_VAR_H(crunch_accept_ok, true, "Use acceptability in okstring");
BOOL_VAR_H(crunch_leave_accept_strings, false,
"Don't pot crunch sensible strings");
BOOL_VAR_H(crunch_include_numerals, false, "Fiddle alpha figures");
INT_VAR_H(crunch_leave_lc_strings, 4,
"Don't crunch words with long lower case strings");
INT_VAR_H(crunch_leave_uc_strings, 4,
"Don't crunch words with long lower case strings");
INT_VAR_H(crunch_long_repetitions, 3, "Crunch words with long repetitions");
INT_VAR_H(crunch_debug, 0, "As it says");
INT_VAR_H(fixsp_non_noise_limit, 1,
"How many non-noise blbs either side?");
double_VAR_H(fixsp_small_outlines_size, 0.28, "Small if lt xht x this");
BOOL_VAR_H(tessedit_prefer_joined_punct, false, "Reward punctation joins");
INT_VAR_H(fixsp_done_mode, 1, "What constitues done for spacing");
INT_VAR_H(debug_fix_space_level, 0, "Contextual fixspace debug");
STRING_VAR_H(numeric_punctuation, ".,",
"Punct. chs expected WITHIN numbers");
INT_VAR_H(x_ht_acceptance_tolerance, 8,
"Max allowed deviation of blob top outside of font data");
INT_VAR_H(x_ht_min_change, 8, "Min change in xht before actually trying it");
INT_VAR_H(superscript_debug, 0, "Debug level for sub & superscript fixer");
double_VAR_H(superscript_worse_certainty, 2.0, "How many times worse "
"certainty does a superscript position glyph need to be for us "
"to try classifying it as a char with a different baseline?");
double_VAR_H(superscript_bettered_certainty, 0.97, "What reduction in "
"badness do we think sufficient to choose a superscript over "
"what we'd thought. For example, a value of 0.6 means we want "
"to reduce badness of certainty by 40%");
double_VAR_H(superscript_scaledown_ratio, 0.4,
"A superscript scaled down more than this is unbelievably "
"small. For example, 0.3 means we expect the font size to "
"be no smaller than 30% of the text line font size.");
double_VAR_H(subscript_max_y_top, 0.5,
"Maximum top of a character measured as a multiple of x-height "
"above the baseline for us to reconsider whether it's a "
"subscript.");
double_VAR_H(superscript_min_y_bottom, 0.3,
"Minimum bottom of a character measured as a multiple of "
"x-height above the baseline for us to reconsider whether it's "
"a superscript.");
BOOL_VAR_H(tessedit_write_block_separators, false,
"Write block separators in output");
BOOL_VAR_H(tessedit_write_rep_codes, false,
"Write repetition char code");
BOOL_VAR_H(tessedit_write_unlv, false, "Write .unlv output file");
BOOL_VAR_H(tessedit_create_txt, false, "Write .txt output file");
BOOL_VAR_H(tessedit_create_hocr, false, "Write .html hOCR output file");
BOOL_VAR_H(tessedit_create_tsv, false, "Write .tsv output file");
BOOL_VAR_H(tessedit_create_pdf, false, "Write .pdf output file");
BOOL_VAR_H(textonly_pdf, false,
"Create PDF with only one invisible text layer");
INT_VAR_H(jpg_quality, 85, "Set JPEG quality level");
STRING_VAR_H(unrecognised_char, "|",
"Output char for unidentified blobs");
INT_VAR_H(suspect_level, 99, "Suspect marker level");
INT_VAR_H(suspect_space_level, 100,
"Min suspect level for rejecting spaces");
INT_VAR_H(suspect_short_words, 2, "Don't Suspect dict wds longer than this");
BOOL_VAR_H(suspect_constrain_1Il, false, "UNLV keep 1Il chars rejected");
double_VAR_H(suspect_rating_per_ch, 999.9, "Don't touch bad rating limit");
double_VAR_H(suspect_accept_rating, -999.9, "Accept good rating limit");
BOOL_VAR_H(tessedit_minimal_rejection, false, "Only reject tess failures");
BOOL_VAR_H(tessedit_zero_rejection, false, "Don't reject ANYTHING");
BOOL_VAR_H(tessedit_word_for_word, false,
"Make output have exactly one word per WERD");
BOOL_VAR_H(tessedit_zero_kelvin_rejection, false,
"Don't reject ANYTHING AT ALL");
BOOL_VAR_H(tessedit_consistent_reps, true, "Force all rep chars the same");
INT_VAR_H(tessedit_reject_mode, 0, "Rejection algorithm");
BOOL_VAR_H(tessedit_rejection_debug, false, "Adaption debug");
BOOL_VAR_H(tessedit_flip_0O, true, "Contextual 0O O0 flips");
double_VAR_H(tessedit_lower_flip_hyphen, 1.5,
"Aspect ratio dot/hyphen test");
double_VAR_H(tessedit_upper_flip_hyphen, 1.8,
"Aspect ratio dot/hyphen test");
BOOL_VAR_H(rej_trust_doc_dawg, false, "Use DOC dawg in 11l conf. detector");
BOOL_VAR_H(rej_1Il_use_dict_word, false, "Use dictword test");
BOOL_VAR_H(rej_1Il_trust_permuter_type, true, "Don't double check");
BOOL_VAR_H(rej_use_tess_accepted, true, "Individual rejection control");
BOOL_VAR_H(rej_use_tess_blanks, true, "Individual rejection control");
BOOL_VAR_H(rej_use_good_perm, true, "Individual rejection control");
BOOL_VAR_H(rej_use_sensible_wd, false, "Extend permuter check");
BOOL_VAR_H(rej_alphas_in_number_perm, false, "Extend permuter check");
double_VAR_H(rej_whole_of_mostly_reject_word_fract, 0.85, "if >this fract");
INT_VAR_H(tessedit_image_border, 2, "Rej blbs near image edge limit");
STRING_VAR_H(ok_repeated_ch_non_alphanum_wds, "-?*\075",
"Allow NN to unrej");
STRING_VAR_H(conflict_set_I_l_1, "Il1[]", "Il1 conflict set");
INT_VAR_H(min_sane_x_ht_pixels, 8, "Reject any x-ht lt or eq than this");
BOOL_VAR_H(tessedit_create_boxfile, false, "Output text with boxes");
INT_VAR_H(tessedit_page_number, -1,
"-1 -> All pages, else specific page to process");
BOOL_VAR_H(tessedit_write_images, false, "Capture the image from the IPE");
BOOL_VAR_H(interactive_display_mode, false, "Run interactively?");
STRING_VAR_H(file_type, ".tif", "Filename extension");
BOOL_VAR_H(tessedit_override_permuter, true, "According to dict_word");
STRING_VAR_H(tessedit_load_sublangs, "",
"List of languages to load with this one");
BOOL_VAR_H(tessedit_use_primary_params_model, false,
"In multilingual mode use params model of the primary language");
// Min acceptable orientation margin (difference in scores between top and 2nd
// choice in OSResults::orientations) to believe the page orientation.
double_VAR_H(min_orientation_margin, 7.0,
"Min acceptable orientation margin");
BOOL_VAR_H(textord_tabfind_show_vlines, false, "Debug line finding");
BOOL_VAR_H(textord_use_cjk_fp_model, FALSE, "Use CJK fixed pitch model");
BOOL_VAR_H(poly_allow_detailed_fx, false,
"Allow feature extractors to see the original outline");
BOOL_VAR_H(tessedit_init_config_only, false,
"Only initialize with the config file. Useful if the instance is "
"not going to be used for OCR but say only for layout analysis.");
BOOL_VAR_H(textord_equation_detect, false, "Turn on equation detector");
BOOL_VAR_H(textord_tabfind_vertical_text, true, "Enable vertical detection");
BOOL_VAR_H(textord_tabfind_force_vertical_text, false,
"Force using vertical text page mode");
double_VAR_H(textord_tabfind_vertical_text_ratio, 0.5,
"Fraction of textlines deemed vertical to use vertical page "
"mode");
double_VAR_H(textord_tabfind_aligned_gap_fraction, 0.75,
"Fraction of height used as a minimum gap for aligned blobs.");
INT_VAR_H(tessedit_parallelize, 0, "Run in parallel where possible");
BOOL_VAR_H(preserve_interword_spaces, false,
"Preserve multiple interword spaces");
STRING_VAR_H(page_separator, "\f",
"Page separator (default is form feed control character)");
// The following parameters were deprecated and removed from their original
// locations. The parameters are temporarily kept here to give Tesseract
// users a chance to updated their [lang].traineddata and config files
// without introducing failures during Tesseract initialization.
// TODO(ocr-team): remove these parameters from the code once we are
// reasonably sure that Tesseract users have updated their data files.
//
// BEGIN DEPRECATED PARAMETERS
BOOL_VAR_H(textord_tabfind_vertical_horizontal_mix, true,
"find horizontal lines such as headers in vertical page mode");
INT_VAR_H(tessedit_ok_mode, 5, "Acceptance decision algorithm");
BOOL_VAR_H(load_fixed_length_dawgs, true, "Load fixed length"
" dawgs (e.g. for non-space delimited languages)");
INT_VAR_H(segment_debug, 0, "Debug the whole segmentation process");
BOOL_VAR_H(permute_debug, 0, "char permutation debug");
double_VAR_H(bestrate_pruning_factor, 2.0, "Multiplying factor of"
" current best rate to prune other hypotheses");
BOOL_VAR_H(permute_script_word, 0,
"Turn on word script consistency permuter");
BOOL_VAR_H(segment_segcost_rating, 0,
"incorporate segmentation cost in word rating?");
double_VAR_H(segment_reward_script, 0.95,
"Score multipler for script consistency within a word. "
"Being a 'reward' factor, it should be <= 1. "
"Smaller value implies bigger reward.");
BOOL_VAR_H(permute_fixed_length_dawg, 0,
"Turn on fixed-length phrasebook search permuter");
BOOL_VAR_H(permute_chartype_word, 0,
"Turn on character type (property) consistency permuter");
double_VAR_H(segment_reward_chartype, 0.97,
"Score multipler for char type consistency within a word. ");
double_VAR_H(segment_reward_ngram_best_choice, 0.99,
"Score multipler for ngram permuter's best choice"
" (only used in the Han script path).");
BOOL_VAR_H(ngram_permuter_activated, false,
"Activate character-level n-gram-based permuter");
BOOL_VAR_H(permute_only_top, false, "Run only the top choice permuter");
INT_VAR_H(language_model_fixed_length_choices_depth, 3,
"Depth of blob choice lists to explore"
" when fixed length dawgs are on");
BOOL_VAR_H(use_new_state_cost, FALSE,
"use new state cost heuristics for segmentation state evaluation");
double_VAR_H(heuristic_segcost_rating_base, 1.25,
"base factor for adding segmentation cost into word rating."
"It's a multiplying factor, the larger the value above 1, "
"the bigger the effect of segmentation cost.");
double_VAR_H(heuristic_weight_rating, 1,
"weight associated with char rating in combined cost of state");
double_VAR_H(heuristic_weight_width, 1000.0,
"weight associated with width evidence in combined cost of"
" state");
double_VAR_H(heuristic_weight_seamcut, 0,
"weight associated with seam cut in combined cost of state");
double_VAR_H(heuristic_max_char_wh_ratio, 2.0,
"max char width-to-height ratio allowed in segmentation");
BOOL_VAR_H(enable_new_segsearch, false,
"Enable new segmentation search path.");
double_VAR_H(segsearch_max_fixed_pitch_char_wh_ratio, 2.0,
"Maximum character width-to-height ratio for"
"fixed pitch fonts");
// END DEPRECATED PARAMETERS
//// ambigsrecog.cpp /////////////////////////////////////////////////////////
FILE *init_recog_training(const STRING &fname);
void recog_training_segmented(const STRING &fname,
PAGE_RES *page_res,
volatile ETEXT_DESC *monitor,
FILE *output_file);
void ambigs_classify_and_output(const char *label,
PAGE_RES_IT* pr_it,
FILE *output_file);
private:
// The filename of a backup config file. If not null, then we currently
// have a temporary debug config file loaded, and backup_config_file_
// will be loaded, and set to null when debug is complete.
const char* backup_config_file_;
// The filename of a config file to read when processing a debug word.
STRING word_config_;
// Image used for input to layout analysis and tesseract recognition.
// May be modified by the ShiroRekhaSplitter to eliminate the top-line.
Pix* pix_binary_;
// Grey-level input image if the input was not binary, otherwise NULL.
Pix* pix_grey_;
// Original input image. Color if the input was color.
Pix* pix_original_;
// Thresholds that were used to generate the thresholded image from grey.
Pix* pix_thresholds_;
// Debug images. If non-empty, will be written on destruction.
DebugPixa pixa_debug_;
// Input image resolution after any scaling. The resolution is not well
// transmitted by operations on Pix, so we keep an independent record here.
int source_resolution_;
// The shiro-rekha splitter object which is used to split top-lines in
// Devanagari words to provide a better word and grapheme segmentation.
ShiroRekhaSplitter splitter_;
// Page segmentation/layout
Textord textord_;
// True if the primary language uses right_to_left reading order.
bool right_to_left_;
Pix* scaled_color_;
int scaled_factor_;
FCOORD deskew_;
FCOORD reskew_;
TesseractStats stats_;
// Sub-languages to be tried in addition to this.
GenericVector<Tesseract*> sub_langs_;
// Most recently used Tesseract out of this and sub_langs_. The default
// language for the next word.
Tesseract* most_recently_used_;
// The size of the font table, ie max possible font id + 1.
int font_table_size_;
// Equation detector. Note: this pointer is NOT owned by the class.
EquationDetect* equ_detect_;
// LSTM recognizer, if available.
LSTMRecognizer* lstm_recognizer_;
// Output "page" number (actually line number) using TrainLineRecognizer.
int train_line_page_num_;
};
} // namespace tesseract
#endif // TESSERACT_CCMAIN_TESSERACTCLASS_H_
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