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tesseract/dict/stopper.cpp
david.eger@gmail.com 0aadbd0169 Save BLOB_CHOICE s for alternate choices saved during segmentation 
search so we have them when trying to replace words with alternates in
the bigram correction pass.


git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@739 d0cd1f9f-072b-0410-8dd7-cf729c803f20
2012-09-01 00:33:46 +00:00

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/******************************************************************************
** Filename: stopper.c
** Purpose: Stopping criteria for word classifier.
** Author: Dan Johnson
** History: Mon Apr 29 14:56:49 1991, DSJ, Created.
**
** (c) Copyright Hewlett-Packard Company, 1988.
** 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.
******************************************************************************/
#include "stopper.h"
#include "matchdefs.h"
#include "callcpp.h"
#include "permute.h"
#include "danerror.h"
#include "const.h"
#include "efio.h"
#include "scanutils.h"
#include "unichar.h"
#include "params.h"
#include "dict.h"
#include "image.h"
#include "ccutil.h"
#include "ratngs.h"
#include "ambigs.h"
#include <stdio.h>
#include <string.h>
#include <ctype.h>
#include <math.h>
#ifdef __UNIX__
#include <assert.h>
#endif
#ifdef _MSC_VER
#pragma warning(disable:4244) // Conversion warnings
#pragma warning(disable:4800) // int/bool warnings
#endif
/* these are kludges - add appropriate .h file later */
/* from adaptmatch.cpp */
#define MAX_WERD_SIZE 100
typedef struct
{
VIABLE_CHOICE Choice;
float ChunkCertainty[MAX_NUM_CHUNKS];
UNICHAR_ID ChunkClass[MAX_NUM_CHUNKS];
} EXPANDED_CHOICE;
void DeleteViableChoiceStruct(void *vcs) {
delete (static_cast<VIABLE_CHOICE_STRUCT *>(vcs));
}
#define BestCertainty(Choices) \
(((VIABLE_CHOICE) first_node (Choices))->Certainty)
#define BestRating(Choices) (((VIABLE_CHOICE) first_node (Choices))->Rating)
#define BestFactor(Choices) \
(((VIABLE_CHOICE) first_node (Choices))->AdjustFactor)
/**----------------------------------------------------------------------------
Private Code
----------------------------------------------------------------------------**/
// Returns -1 if the rating for Choice1 is less than the rating for Choice2,
// otherwise returns 1.
static int CmpChoiceRatings(void *arg1, // VIABLE_CHOICE Choice1
void *arg2) { // VIABLE_CHOICE Choice2
float R1, R2;
VIABLE_CHOICE Choice1 = (VIABLE_CHOICE) arg1;
VIABLE_CHOICE Choice2 = (VIABLE_CHOICE) arg2;
R1 = Choice1->Rating;
R2 = Choice2->Rating;
return (R1 < R2) ? -1 : 1;
}
// Expands Choice and places the results in ExpandedChoice. The primary
// function of expansion is to create an two arrays, one which holds the
// corresponding certainty for each chunk in Choice, and one which holds
// the class for each chunk.
static void ExpandChoice(VIABLE_CHOICE Choice,
EXPANDED_CHOICE *ExpandedChoice) {
int i, j, Chunk;
ExpandedChoice->Choice = Choice;
for (i = 0, Chunk = 0; i < Choice->Length; i++)
for (j = 0; j < Choice->Blob[i].NumChunks; j++, Chunk++) {
ExpandedChoice->ChunkCertainty[Chunk] = Choice->Blob[i].Certainty;
ExpandedChoice->ChunkClass[Chunk] = Choice->Blob[i].Class;
}
}
VIABLE_CHOICE_STRUCT::VIABLE_CHOICE_STRUCT(int length)
: Length(length) {
Blob = new CHAR_CHOICE[length];
blob_choices = NULL;
}
VIABLE_CHOICE_STRUCT::VIABLE_CHOICE_STRUCT() : Length(0) {
Blob = NULL;
blob_choices = NULL;
}
VIABLE_CHOICE_STRUCT::~VIABLE_CHOICE_STRUCT() {
delete []Blob;
if (blob_choices) {
blob_choices->deep_clear();
delete blob_choices;
}
}
void VIABLE_CHOICE_STRUCT::Init(
const WERD_CHOICE &word_choice,
const PIECES_STATE &pieces_state,
const float certainties[],
FLOAT32 adjust_factor) {
this->Rating = word_choice.rating();
this->Certainty = word_choice.certainty();
this->AdjustFactor = adjust_factor;
this->ComposedFromCharFragments = false;
ASSERT_HOST(this->Length == word_choice.length());
for (int i = 0, bw_idx = 0; i < word_choice.length(); i++, bw_idx++) {
int blob_width = pieces_state[bw_idx];
CHAR_CHOICE *blob_choice = &this->Blob[i];
blob_choice->Class = word_choice.unichar_id(i);
blob_choice->NumChunks = blob_width;
blob_choice->Certainty = certainties[i];
for (int f = 1; f < word_choice.fragment_length(i); ++f) {
blob_width = pieces_state[++bw_idx];
assert(blob_width > 0);
blob_choice->NumChunks += blob_width;
this->ComposedFromCharFragments = true;
}
}
}
void VIABLE_CHOICE_STRUCT::SetBlobChoices(
const BLOB_CHOICE_LIST_VECTOR &src_choices) {
if (blob_choices != NULL) {
blob_choices->deep_clear();
} else {
blob_choices = new BLOB_CHOICE_LIST_CLIST();
}
BLOB_CHOICE_LIST_C_IT list_it(blob_choices);
for (int i = 0; i < src_choices.size(); ++i) {
BLOB_CHOICE_LIST *cc_list = new BLOB_CHOICE_LIST();
cc_list->deep_copy(src_choices[i], &BLOB_CHOICE::deep_copy);
list_it.add_after_then_move(cc_list);
}
}
namespace tesseract {
// If the certainty of any chunk in Choice (item1) is not ambiguous with the
// corresponding chunk in the best choice (item2), frees Choice and
// returns true.
int Dict::FreeBadChoice(
void *item1, // VIABLE_CHOICE Choice,
void *item2) { // EXPANDED_CHOICE *BestChoice
int i, j, Chunk;
FLOAT32 Threshold;
VIABLE_CHOICE Choice = reinterpret_cast<VIABLE_CHOICE>(item1);
EXPANDED_CHOICE *BestChoice = reinterpret_cast<EXPANDED_CHOICE *>(item2);
Threshold = StopperAmbigThreshold(BestChoice->Choice->AdjustFactor,
Choice->AdjustFactor);
for (i = 0, Chunk = 0; i < Choice->Length; i++) {
for (j = 0; j < Choice->Blob[i].NumChunks; j++, Chunk++) {
if (Choice->Blob[i].Class != BestChoice->ChunkClass[Chunk] &&
Choice->Blob[i].Certainty - BestChoice->ChunkCertainty[Chunk] <
Threshold) {
if (stopper_debug_level >= 2)
PrintViableChoice(stderr, "\nDiscarding bad choice: ", Choice);
delete Choice;
return true;
}
}
}
return false;
}
bool Dict::AcceptableChoice(BLOB_CHOICE_LIST_VECTOR *Choices,
WERD_CHOICE *BestChoice,
DANGERR *fixpt,
ACCEPTABLE_CHOICE_CALLER caller,
bool *modified_blobs) {
float CertaintyThreshold = stopper_nondict_certainty_base;
int WordSize;
if (modified_blobs != NULL) *modified_blobs = false;
if (stopper_no_acceptable_choices) return false;
if (fixpt != NULL) fixpt->clear();
if (BestChoice->length() == 0)
return false;
if (caller == CHOPPER_CALLER && BestChoice->fragment_mark()) {
if (stopper_debug_level >= 1) {
cprintf("AcceptableChoice(): a choice with fragments beats BestChoice");
}
return false;
}
bool no_dang_ambigs = (GetMaxFixedLengthDawgIndex() >= 0 ||
NoDangerousAmbig(BestChoice, fixpt, true,
Choices, modified_blobs));
bool is_valid_word = valid_word_permuter(BestChoice->permuter(), false);
bool is_case_ok = case_ok(*BestChoice, getUnicharset());
if (stopper_debug_level >= 1)
tprintf("\nStopper: %s (word=%c, case=%c)\n",
BestChoice->debug_string().string(),
(is_valid_word ? 'y' : 'n'),
(is_case_ok ? 'y' : 'n'));
// Do not accept invalid words in PASS1.
if (reject_offset_ <= 0.0f && !is_valid_word) return false;
if (is_valid_word && is_case_ok) {
WordSize = LengthOfShortestAlphaRun(*BestChoice);
WordSize -= stopper_smallword_size;
if (WordSize < 0)
WordSize = 0;
CertaintyThreshold += WordSize * stopper_certainty_per_char;
}
if (stopper_debug_level >= 1)
tprintf("Stopper: Certainty = %4.1f, Threshold = %4.1f\n",
BestChoice->certainty(), CertaintyThreshold);
if (no_dang_ambigs &&
BestChoice->certainty() > CertaintyThreshold &&
UniformCertainties(*Choices, *BestChoice)) {
return true;
} else {
if (stopper_debug_level >= 2) {
tprintf("AcceptableChoice() returned false"
" (no_dang_ambig:%d cert:%g thresh:%g uniform:%d)\n",
no_dang_ambigs, BestChoice->certainty(),
CertaintyThreshold,
UniformCertainties(*Choices, *BestChoice));
}
return false;
}
}
bool Dict::AcceptableResult(const WERD_CHOICE &BestChoice) {
float CertaintyThreshold = stopper_nondict_certainty_base - reject_offset_;
int WordSize;
if (stopper_debug_level >= 1) {
tprintf("\nRejecter: %s (word=%c, case=%c, unambig=%c)\n",
BestChoice.debug_string().string(),
(valid_word(BestChoice) ? 'y' : 'n'),
(case_ok(BestChoice, getUnicharset()) ? 'y' : 'n'),
((list_rest (best_choices_) != NIL_LIST) ? 'n' : 'y'));
}
if (BestChoice.length() == 0 || CurrentWordAmbig())
return false;
if (BestChoice.fragment_mark()) {
if (stopper_debug_level >= 1) {
cprintf("AcceptableResult(): a choice with fragments beats BestChoice\n");
}
return false;
}
if (valid_word(BestChoice) && case_ok(BestChoice, getUnicharset())) {
WordSize = LengthOfShortestAlphaRun(BestChoice);
WordSize -= stopper_smallword_size;
if (WordSize < 0)
WordSize = 0;
CertaintyThreshold += WordSize * stopper_certainty_per_char;
}
if (stopper_debug_level >= 1)
cprintf ("Rejecter: Certainty = %4.1f, Threshold = %4.1f ",
BestChoice.certainty(), CertaintyThreshold);
if (BestChoice.certainty() > CertaintyThreshold &&
!stopper_no_acceptable_choices) {
if (stopper_debug_level >= 1)
cprintf("ACCEPTED\n");
return true;
}
else {
if (stopper_debug_level >= 1)
cprintf("REJECTED\n");
return false;
}
}
bool Dict::AlternativeChoicesWorseThan(FLOAT32 Threshold) {
LIST Alternatives;
VIABLE_CHOICE Choice;
Alternatives = list_rest (best_choices_);
iterate(Alternatives) {
Choice = (VIABLE_CHOICE) first_node (Alternatives);
if (Choice->AdjustFactor <= Threshold)
return false;
}
return true;
}
bool Dict::CurrentBestChoiceIs(const WERD_CHOICE &WordChoice) {
return (best_choices_ != NIL_LIST &&
StringSameAs(WordChoice, (VIABLE_CHOICE)first_node(best_choices_)));
}
FLOAT32 Dict::CurrentBestChoiceAdjustFactor() {
VIABLE_CHOICE BestChoice;
if (best_choices_ == NIL_LIST)
return (MAX_FLOAT32);
BestChoice = (VIABLE_CHOICE) first_node (best_choices_);
return (BestChoice->AdjustFactor);
}
bool Dict::CurrentWordAmbig() {
return (list_rest (best_choices_) != NIL_LIST);
}
void Dict::DebugWordChoices() {
LIST Choices;
int i;
char LabelString[80];
VIABLE_CHOICE VChoice = (VIABLE_CHOICE)first_node(best_choices_);
bool force_debug =
fragments_debug && VChoice != NULL && VChoice->ComposedFromCharFragments;
if (stopper_debug_level >= 1 || force_debug ||
(((STRING)word_to_debug).length() > 0 && best_choices_ &&
StringSameAs(word_to_debug.string(), word_to_debug_lengths.string(),
(VIABLE_CHOICE)first_node(best_choices_)))) {
if (best_raw_choice_)
PrintViableChoice(stderr, "\nBest Raw Choice: ", best_raw_choice_);
i = 1;
Choices = best_choices_;
if (Choices)
cprintf("\nBest Cooked Choices:\n");
iterate(Choices) {
sprintf(LabelString, "Cooked Choice #%d: ", i);
PrintViableChoice(stderr, LabelString,
(VIABLE_CHOICE)first_node(Choices));
i++;
}
}
}
void Dict::PrintAmbigAlternatives(FILE *file, const char *label,
int label_num_unichars) {
iterate(raw_choices_) {
VIABLE_CHOICE Choice = (VIABLE_CHOICE)first_node(raw_choices_);
if (Choice->Length > 0 &&
(label_num_unichars > 1 || Choice->Length > 1)) {
for (int i = 0; i < Choice->Length; i++) {
fprintf(file, "%s",
getUnicharset().id_to_unichar(Choice->Blob[i].Class));
}
fflush(file);
fprintf(file, "\t%s\t%.4f\t%.4f\n", label,
Choice->Rating, Choice->Certainty);
}
}
}
void Dict::FilterWordChoices() {
EXPANDED_CHOICE BestChoice;
if (best_choices_ == NIL_LIST || second_node (best_choices_) == NIL_LIST)
return;
// Compute certainties and class for each chunk in best choice.
VIABLE_CHOICE_STRUCT *best_choice =
(VIABLE_CHOICE_STRUCT *)first_node(best_choices_);
ExpandChoice(best_choice, &BestChoice);
if (stopper_debug_level >= 2)
PrintViableChoice(stderr, "\nFiltering against best choice: ", best_choice);
TessResultCallback2<int, void*, void*>* is_bad =
NewPermanentTessCallback(this, &Dict::FreeBadChoice);
set_rest(best_choices_, delete_d(list_rest(best_choices_),
&BestChoice, is_bad));
delete is_bad;
}
void Dict::FindClassifierErrors(FLOAT32 MinRating,
FLOAT32 MaxRating,
FLOAT32 RatingMargin,
FLOAT32 Thresholds[]) {
EXPANDED_CHOICE BestRaw;
VIABLE_CHOICE Choice;
int i, j, Chunk;
FLOAT32 AvgRating;
int NumErrorChunks;
assert (best_choices_ != NIL_LIST);
assert (best_raw_choice_ != NULL);
ExpandChoice(best_raw_choice_, &BestRaw);
Choice = (VIABLE_CHOICE) first_node (best_choices_);
for (i = 0, Chunk = 0; i < Choice->Length; i++, Thresholds++) {
AvgRating = 0.0;
NumErrorChunks = 0;
for (j = 0; j < Choice->Blob[i].NumChunks; j++, Chunk++) {
if (Choice->Blob[i].Class != BestRaw.ChunkClass[Chunk]) {
AvgRating += BestRaw.ChunkCertainty[Chunk];
NumErrorChunks++;
}
}
if (NumErrorChunks > 0) {
AvgRating /= NumErrorChunks;
*Thresholds = (AvgRating / -certainty_scale) * (1.0 - RatingMargin);
}
else
*Thresholds = MaxRating;
if (*Thresholds > MaxRating)
*Thresholds = MaxRating;
if (*Thresholds < MinRating)
*Thresholds = MinRating;
}
}
void Dict::InitChoiceAccum() {
BLOB_WIDTH *BlobWidth, *End;
if (best_raw_choice_)
delete best_raw_choice_;
best_raw_choice_ = NULL;
if (best_choices_)
destroy_nodes(best_choices_, DeleteViableChoiceStruct);
best_choices_ = NIL_LIST;
if (raw_choices_)
destroy_nodes(raw_choices_, DeleteViableChoiceStruct);
raw_choices_ = NIL_LIST;
EnableChoiceAccum();
for (BlobWidth = current_segmentation_,
End = current_segmentation_ + MAX_NUM_CHUNKS;
BlobWidth < End; *BlobWidth++ = 1);
}
void Dict::ClearBestChoiceAccum() {
if (best_choices_) destroy_nodes(best_choices_, DeleteViableChoiceStruct);
best_choices_ = NIL_LIST;
}
void Dict::LogNewSegmentation(PIECES_STATE BlobWidth) {
BLOB_WIDTH *Segmentation;
for (Segmentation = current_segmentation_; *BlobWidth != 0;
BlobWidth++, Segmentation++)
*Segmentation = *BlobWidth;
*Segmentation = 0;
}
void Dict::LogNewSplit(int Blob) {
LIST Choices;
if (best_raw_choice_) AddNewChunk(best_raw_choice_, Blob);
Choices = best_choices_;
iterate(Choices) {
AddNewChunk ((VIABLE_CHOICE) first_node (Choices), Blob);
}
Choices = raw_choices_;
iterate(Choices) {
AddNewChunk ((VIABLE_CHOICE) first_node (Choices), Blob);
}
}
void Dict::LogNewChoice(FLOAT32 AdjustFactor,
const float Certainties[],
bool raw_choice,
WERD_CHOICE *WordChoice,
const BLOB_CHOICE_LIST_VECTOR &blob_choices) {
LIST ChoicesList;
LIST Choices;
FLOAT32 Threshold;
if (!keep_word_choices_)
return;
if (raw_choice) {
if (!best_raw_choice_) {
best_raw_choice_ =
NewViableChoice(*WordChoice, AdjustFactor, Certainties);
} else if (WordChoice->rating() < best_raw_choice_->Rating) {
if (ChoiceSameAs(*WordChoice, best_raw_choice_)) {
FillViableChoice(*WordChoice, AdjustFactor, Certainties,
best_raw_choice_);
} else {
delete best_raw_choice_;
best_raw_choice_ =
NewViableChoice(*WordChoice, AdjustFactor, Certainties);
}
}
if (!save_raw_choices) return;
ChoicesList = raw_choices_;
} else {
ChoicesList = best_choices_;
}
// Throw out obviously bad choices to save some work.
if (ChoicesList != NIL_LIST) {
Threshold = StopperAmbigThreshold(BestFactor(ChoicesList), AdjustFactor);
if (Threshold > -stopper_ambiguity_threshold_offset)
Threshold = -stopper_ambiguity_threshold_offset;
if (WordChoice->certainty() - BestCertainty (ChoicesList) < Threshold) {
// Set the rating of the word to be terrible, so that it does not
// get chosen as the best choice.
if (stopper_debug_level >= 2) {
STRING bad_string;
WordChoice->string_and_lengths(&bad_string, NULL);
tprintf("Discarding choice \"%s\" with an overly low certainty"
" %.4f vs best choice certainty %.4f (Threshold: %.4f)\n",
bad_string.string(), WordChoice->certainty(),
BestCertainty(ChoicesList),
Threshold + BestCertainty(ChoicesList));
}
WordChoice->set_rating(WERD_CHOICE::kBadRating);
return;
}
}
// See if a choice with the same text string has already been found.
VIABLE_CHOICE NewChoice = NULL;
Choices = ChoicesList;
iterate(Choices) {
if (ChoiceSameAs (*WordChoice, (VIABLE_CHOICE) first_node (Choices))) {
if (WordChoice->rating() < BestRating (Choices)) {
NewChoice = (VIABLE_CHOICE) first_node (Choices);
} else {
return;
}
}
}
if (NewChoice) {
FillViableChoice(*WordChoice, AdjustFactor, Certainties, NewChoice);
ChoicesList = delete_d(ChoicesList, NewChoice, is_same_node);
} else {
NewChoice = NewViableChoice(*WordChoice, AdjustFactor, Certainties);
}
// Now we know we're gonna save it, so add the expensive copy.
NewChoice->SetBlobChoices(blob_choices);
ChoicesList = s_adjoin (ChoicesList, NewChoice, CmpChoiceRatings);
if (stopper_debug_level >= 2)
raw_choice ? PrintViableChoice (stderr, "New Raw Choice: ", NewChoice) :
PrintViableChoice (stderr, "New Word Choice: ", NewChoice);
if (count (ChoicesList) > tessedit_truncate_wordchoice_log) {
Choices =
(LIST) nth_cell (ChoicesList, tessedit_truncate_wordchoice_log);
destroy_nodes(list_rest (Choices), DeleteViableChoiceStruct);
set_rest(Choices, NIL_LIST);
}
// Update raw_choices_/best_choices_ pointer.
if (raw_choice) {
raw_choices_ = ChoicesList;
} else {
best_choices_ = ChoicesList;
}
}
bool Dict::NoDangerousAmbig(WERD_CHOICE *best_choice,
DANGERR *fixpt,
bool fix_replaceable,
BLOB_CHOICE_LIST_VECTOR *blob_choices,
bool *modified_blobs) {
if (stopper_debug_level > 2) {
tprintf("\nRunning NoDangerousAmbig() for %s\n",
best_choice->debug_string().string());
}
// Construct BLOB_CHOICE_LIST_VECTOR with ambiguities
// for each unichar id in BestChoice.
BLOB_CHOICE_LIST_VECTOR ambig_blob_choices;
int i;
bool modified_best_choice = false;
bool ambigs_found = false;
// For each position in best_choice:
// -- choose AMBIG_SPEC_LIST that corresponds to unichar_id at best_choice[i]
// -- initialize wrong_ngram with a single unichar_id at best_choice[i]
// -- look for ambiguities corresponding to wrong_ngram in the list while
// adding the following unichar_ids from best_choice to wrong_ngram
//
// Repeat the above procedure twice: first time look through
// ambigs to be replaced and replace all the ambiguities found;
// second time look through dangerous ambiguities and construct
// ambig_blob_choices with fake a blob choice for each ambiguity
// and pass them to dawg_permute_and_select() to search for
// ambiguous words in the dictionaries.
//
// Note that during the execution of the for loop (on the first pass)
// if replacements are made the length of best_choice might change.
for (int pass = 0; pass < (fix_replaceable ? 2 : 1); ++pass) {
bool replace = (fix_replaceable && pass == 0);
const UnicharAmbigsVector &table = replace ?
getUnicharAmbigs().replace_ambigs() : getUnicharAmbigs().dang_ambigs();
if (!replace) {
// Initialize ambig_blob_choices with lists containing a single
// unichar id for the correspoding position in best_choice.
// best_choice consisting from only the original letters will
// have a rating of 0.0.
for (i = 0; i < best_choice->length(); ++i) {
BLOB_CHOICE_LIST *lst = new BLOB_CHOICE_LIST();
BLOB_CHOICE_IT lst_it(lst);
// TODO(rays/antonova) Should these BLOB_CHOICEs use real xheights
// or are these fake ones good enough?
lst_it.add_to_end(new BLOB_CHOICE(best_choice->unichar_id(i),
0.0, 0.0, -1, -1, -1, 0, 1, false));
ambig_blob_choices.push_back(lst);
}
}
UNICHAR_ID wrong_ngram[MAX_AMBIG_SIZE + 1];
int wrong_ngram_index;
int next_index;
int blob_index = 0;
for (i = 0; i < best_choice->length(); ++i) {
if (i > 0) blob_index += best_choice->fragment_length(i-1);
UNICHAR_ID curr_unichar_id = best_choice->unichar_id(i);
if (stopper_debug_level > 2) {
tprintf("Looking for %s ngrams starting with %s:\n",
replace ? "replaceable" : "ambiguous",
getUnicharset().debug_str(curr_unichar_id).string());
}
wrong_ngram_index = 0;
wrong_ngram[wrong_ngram_index] = curr_unichar_id;
if (curr_unichar_id == INVALID_UNICHAR_ID ||
curr_unichar_id >= table.size() ||
table[curr_unichar_id] == NULL) {
continue; // there is no ambig spec for this unichar id
}
AmbigSpec_IT spec_it(table[curr_unichar_id]);
for (spec_it.mark_cycle_pt(); !spec_it.cycled_list();) {
const AmbigSpec *ambig_spec = spec_it.data();
wrong_ngram[wrong_ngram_index+1] = INVALID_UNICHAR_ID;
int compare = UnicharIdArrayUtils::compare(wrong_ngram,
ambig_spec->wrong_ngram);
if (stopper_debug_level > 2) {
tprintf("candidate ngram: ");
UnicharIdArrayUtils::print(wrong_ngram, getUnicharset());
tprintf("current ngram from spec: ");
UnicharIdArrayUtils::print(ambig_spec->wrong_ngram, getUnicharset());
tprintf("comparison result: %d\n", compare);
}
if (compare == 0) {
// Record the place where we found an ambiguity.
if (fixpt != NULL) {
fixpt->push_back(DANGERR_INFO(
blob_index, blob_index+wrong_ngram_index, replace,
getUnicharset().get_isngram(ambig_spec->correct_ngram_id)));
if (stopper_debug_level > 1) {
tprintf("fixpt+=(%d %d %d %d)\n", blob_index,
blob_index+wrong_ngram_index, false,
getUnicharset().get_isngram(
ambig_spec->correct_ngram_id));
}
}
if (replace) {
if (stopper_debug_level > 2) {
tprintf("replace ambiguity with: ");
UnicharIdArrayUtils::print(
ambig_spec->correct_fragments, getUnicharset());
}
ReplaceAmbig(i, ambig_spec->wrong_ngram_size,
ambig_spec->correct_ngram_id,
best_choice, blob_choices, modified_blobs);
modified_best_choice = true;
} else if (i > 0 || ambig_spec->type != CASE_AMBIG) {
// We found dang ambig - update ambig_blob_choices.
if (stopper_debug_level > 2) {
tprintf("found ambiguity: ");
UnicharIdArrayUtils::print(
ambig_spec->correct_fragments, getUnicharset());
}
ambigs_found = true;
for (int tmp_index = 0; tmp_index <= wrong_ngram_index;
++tmp_index) {
// Add a blob choice for the corresponding fragment of the
// ambiguity. These fake blob choices are initialized with
// negative ratings (which are not possible for real blob
// choices), so that dawg_permute_and_select() considers any
// word not consisting of only the original letters a better
// choice and stops searching for alternatives once such a
// choice is found.
BLOB_CHOICE_IT bc_it(ambig_blob_choices[i+tmp_index]);
bc_it.add_to_end(new BLOB_CHOICE(
ambig_spec->correct_fragments[tmp_index], -1.0, 0.0,
-1, -1, -1, 0, 1, false));
}
}
spec_it.forward();
} else if (compare == -1) {
if (wrong_ngram_index+1 < ambig_spec->wrong_ngram_size &&
((next_index = wrong_ngram_index+1+i) < best_choice->length())) {
// Add the next unichar id to wrong_ngram and keep looking for
// more ambigs starting with curr_unichar_id in AMBIG_SPEC_LIST.
wrong_ngram[++wrong_ngram_index] =
best_choice->unichar_id(next_index);
} else {
break; // no more matching ambigs in this AMBIG_SPEC_LIST
}
} else {
spec_it.forward();
}
} // end searching AmbigSpec_LIST
} // end searching best_choice
} // end searching replace and dangerous ambigs
// If any ambiguities were found permute the constructed ambig_blob_choices
// to see if an alternative dictionary word can be found.
if (ambigs_found) {
if (stopper_debug_level > 2) {
tprintf("\nResulting ambig_blob_choices:\n");
for (i = 0; i < ambig_blob_choices.length(); ++i) {
print_ratings_list("", ambig_blob_choices.get(i), getUnicharset());
tprintf("\n");
}
}
WERD_CHOICE *alt_word = dawg_permute_and_select(ambig_blob_choices, 0.0);
ambigs_found = (alt_word->rating() < 0.0);
if (ambigs_found) {
if (stopper_debug_level >= 1) {
tprintf ("Stopper: Possible ambiguous word = %s\n",
alt_word->debug_string().string());
}
if (fixpt != NULL) {
// Note: Currently character choices combined from fragments can only
// be generated by NoDangrousAmbigs(). This code should be updated if
// the capability to produce classifications combined from character
// fragments is added to other functions.
int orig_i = 0;
for (i = 0; i < alt_word->length(); ++i) {
bool replacement_is_ngram =
getUnicharset().get_isngram(alt_word->unichar_id(i));
int end_i = orig_i + alt_word->fragment_length(i) - 1;
if (alt_word->fragment_length(i) > 1 ||
(orig_i == end_i && replacement_is_ngram)) {
fixpt->push_back(DANGERR_INFO(orig_i, end_i, true,
replacement_is_ngram));
if (stopper_debug_level > 1) {
tprintf("fixpt->dangerous+=(%d %d %d %d)\n", orig_i, end_i,
true, replacement_is_ngram);
}
}
orig_i += alt_word->fragment_length(i);
}
}
}
delete alt_word;
}
if (output_ambig_words_file_ != NULL) {
fprintf(output_ambig_words_file_, "\n");
}
ambig_blob_choices.delete_data_pointers();
return !ambigs_found;
}
void Dict::EndDangerousAmbigs() {}
void Dict::SettupStopperPass1() {
reject_offset_ = 0.0;
}
void Dict::SettupStopperPass2() {
reject_offset_ = stopper_phase2_certainty_rejection_offset;
}
void Dict::AddNewChunk(VIABLE_CHOICE Choice, int Blob) {
int i, LastChunk;
for (i = 0, LastChunk = 0; i < Choice->Length; i++) {
LastChunk += Choice->Blob[i].NumChunks;
if (Blob < LastChunk) {
(Choice->Blob[i].NumChunks)++;
return;
}
}
cprintf ("AddNewChunk failed:Choice->Length=%d, LastChunk=%d, Blob=%d\n",
Choice->Length, LastChunk, Blob);
assert(false); // this should never get executed
}
void Dict::ReplaceAmbig(int wrong_ngram_begin_index, int wrong_ngram_size,
UNICHAR_ID correct_ngram_id, WERD_CHOICE *werd_choice,
BLOB_CHOICE_LIST_VECTOR *blob_choices,
bool *modified_blobs) {
int num_blobs_to_replace = 0;
int begin_blob_index = 0;
int i;
for (i = 0; i < wrong_ngram_begin_index + wrong_ngram_size; ++i) {
if (i >= wrong_ngram_begin_index) {
num_blobs_to_replace += werd_choice->fragment_length(i);
} else {
begin_blob_index += werd_choice->fragment_length(i);
}
}
BLOB_CHOICE_IT bit;
int temp_blob_index = begin_blob_index;
const char *temp_uch = NULL;
const char *correct_ngram_str =
getUnicharset().id_to_unichar(correct_ngram_id);
for (int replaced_count = 0; replaced_count < wrong_ngram_size;
++replaced_count) {
if (blob_choices != NULL) {
UNICHAR_ID uch_id = werd_choice->unichar_id(wrong_ngram_begin_index);
int fraglen = werd_choice->fragment_length(wrong_ngram_begin_index);
if (fraglen > 1) temp_uch = getUnicharset().id_to_unichar(uch_id);
for (i = 0; i < fraglen; ++i) {
if (fraglen > 1) {
STRING frag_str =
CHAR_FRAGMENT::to_string(temp_uch, i, fraglen, false);
getUnicharset().unichar_insert(frag_str.string());
uch_id = getUnicharset().unichar_to_id(frag_str.string());
}
bit.set_to_list(blob_choices->get(temp_blob_index));
STRING correct_frag_uch =
CHAR_FRAGMENT::to_string(correct_ngram_str,
temp_blob_index - begin_blob_index,
num_blobs_to_replace, false);
getUnicharset().unichar_insert(correct_frag_uch.string());
UNICHAR_ID correct_frag_uch_id =
getUnicharset().unichar_to_id(correct_frag_uch.string());
// Find the WERD_CHOICE corresponding to the original unichar in
// the list of blob choices, add the derived character fragment
// before it with the same rating and certainty.
for (bit.mark_cycle_pt(); !bit.cycled_list(); bit.forward()) {
if (bit.data()->unichar_id() == correct_frag_uch_id) {
break; // the unichar we want to insert is already there
}
if (bit.data()->unichar_id() == uch_id) {
bit.add_before_then_move(new BLOB_CHOICE(*(bit.data())));
bit.data()->set_unichar_id(correct_frag_uch_id);
if (modified_blobs != NULL) *modified_blobs = true;
break;
}
}
temp_blob_index++;
}
}
// Remove current unichar from werd_choice. On the last iteration
// set the correct replacement unichar instead of removing a unichar.
if (replaced_count + 1 == wrong_ngram_size) {
werd_choice->set_unichar_id(correct_ngram_id,
num_blobs_to_replace, 0.0, 0.0, wrong_ngram_begin_index);
} else {
werd_choice->remove_unichar_id(wrong_ngram_begin_index);
}
}
if (stopper_debug_level >= 1 && modified_blobs != NULL &&
*modified_blobs && blob_choices != NULL) {
werd_choice->print("ReplaceAmbig() ");
tprintf("Modified blob_choices: ");
for (int i = 0; i < blob_choices->size(); ++i) {
print_ratings_list("\n", blob_choices->get(i), getUnicharset());
}
}
}
int Dict::ChoiceSameAs(const WERD_CHOICE &WordChoice,
VIABLE_CHOICE ViableChoice) {
return (StringSameAs(WordChoice, ViableChoice));
}
int Dict::LengthOfShortestAlphaRun(const WERD_CHOICE &WordChoice) {
int shortest = MAX_INT32;
int curr_len = 0;
for (int w = 0; w < WordChoice.length(); ++w) {
if (getUnicharset().get_isalpha(WordChoice.unichar_id(w))) {
curr_len++;
} else if (curr_len > 0) {
if (curr_len < shortest) shortest = curr_len;
curr_len = 0;
}
}
if (curr_len > 0 && curr_len < shortest) {
shortest = curr_len;
} else if (shortest == MAX_INT32) {
shortest = 0;
}
return shortest;
}
VIABLE_CHOICE Dict::NewViableChoice(const WERD_CHOICE &WordChoice,
FLOAT32 AdjustFactor,
const float Certainties[]) {
int Length = WordChoice.length();
assert (Length <= MAX_NUM_CHUNKS && Length > 0);
VIABLE_CHOICE NewChoice = new VIABLE_CHOICE_STRUCT(Length);
FillViableChoice(WordChoice, AdjustFactor, Certainties, NewChoice);
return NewChoice;
}
void Dict::PrintViableChoice(FILE *File, const char *Label, VIABLE_CHOICE Choice) {
int i, j;
fprintf (File, "%s", Label);
fprintf(File, "(R=%5.1f, C=%4.1f, F=%4.2f, Frag=%d) ",
Choice->Rating, Choice->Certainty,
Choice->AdjustFactor, Choice->ComposedFromCharFragments);
for (i = 0; i < Choice->Length; i++)
fprintf(File, "%s", getUnicharset().id_to_unichar(Choice->Blob[i].Class));
fprintf(File, "\n");
for (i = 0; i < Choice->Length; i++) {
fprintf(File, " %s", getUnicharset().id_to_unichar(Choice->Blob[i].Class));
for (j = 0; j < Choice->Blob[i].NumChunks - 1; j++)
fprintf(File, " ");
}
fprintf(File, "\n");
for (i = 0; i < Choice->Length; i++) {
for (j = 0; j < Choice->Blob[i].NumChunks; j++)
fprintf(File, "%3d ", (int) (Choice->Blob[i].Certainty * -10.0));
}
fprintf(File, "\n");
for (i = 0; i < Choice->Length; i++) {
for (j = 0; j < Choice->Blob[i].NumChunks; j++)
fprintf(File, "%3d ", Choice->Blob[i].NumChunks);
}
fprintf(File, "\n");
}
void Dict::FillViableChoice(const WERD_CHOICE &WordChoice,
FLOAT32 AdjustFactor, const float Certainties[],
VIABLE_CHOICE ViableChoice) {
ViableChoice->Init(WordChoice, current_segmentation_, Certainties,
AdjustFactor);
}
bool Dict::StringSameAs(const WERD_CHOICE &WordChoice,
VIABLE_CHOICE ViableChoice) {
if (WordChoice.length() != ViableChoice->Length) {
return false;
}
int i;
CHAR_CHOICE *CharChoice;
for (i = 0, CharChoice = &(ViableChoice->Blob[0]);
i < ViableChoice->Length; CharChoice++, i++) {
if (CharChoice->Class != WordChoice.unichar_id(i)) {
return false;
}
}
return true;
}
bool Dict::StringSameAs(const char *String,
const char *String_lengths,
VIABLE_CHOICE ViableChoice) {
CHAR_CHOICE *Char;
int i;
int current_unichar_length;
for (Char = &(ViableChoice->Blob[0]), i = 0;
i < ViableChoice->Length;
String += *(String_lengths++), Char++, i++) {
current_unichar_length = strlen(getUnicharset().id_to_unichar(Char->Class));
if (current_unichar_length != *String_lengths ||
strncmp(String, getUnicharset().id_to_unichar(Char->Class),
current_unichar_length) != 0)
return false;
}
return (*String == 0) ? true : false;
}
int Dict::UniformCertainties(const BLOB_CHOICE_LIST_VECTOR &Choices,
const WERD_CHOICE &BestChoice) {
float Certainty;
float WorstCertainty = MAX_FLOAT32;
float CertaintyThreshold;
FLOAT64 TotalCertainty;
FLOAT64 TotalCertaintySquared;
FLOAT64 Variance;
FLOAT32 Mean, StdDev;
int WordLength;
WordLength = Choices.length();
if (WordLength < 3)
return true;
TotalCertainty = TotalCertaintySquared = 0.0;
BLOB_CHOICE_IT BlobChoiceIt;
for (int i = 0; i < Choices.length(); ++i) {
BlobChoiceIt.set_to_list(Choices.get(i));
Certainty = BlobChoiceIt.data()->certainty();
TotalCertainty += Certainty;
TotalCertaintySquared += Certainty * Certainty;
if (Certainty < WorstCertainty)
WorstCertainty = Certainty;
}
// Subtract off worst certainty from statistics.
WordLength--;
TotalCertainty -= WorstCertainty;
TotalCertaintySquared -= WorstCertainty * WorstCertainty;
Mean = TotalCertainty / WordLength;
Variance = ((WordLength * TotalCertaintySquared -
TotalCertainty * TotalCertainty) /
(WordLength * (WordLength - 1)));
if (Variance < 0.0)
Variance = 0.0;
StdDev = sqrt (Variance);
CertaintyThreshold = Mean - stopper_allowable_character_badness * StdDev;
if (CertaintyThreshold > stopper_nondict_certainty_base)
CertaintyThreshold = stopper_nondict_certainty_base;
if (BestChoice.certainty() < CertaintyThreshold) {
if (stopper_debug_level >= 1)
cprintf("Stopper: Non-uniform certainty = %4.1f"
" (m=%4.1f, s=%4.1f, t=%4.1f)\n",
BestChoice.certainty(), Mean, StdDev, CertaintyThreshold);
return false;
} else {
return true;
}
}
} // namespace tesseract
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