mirror/tesseract
1
0
Fork 0
mirror of https://github.com/tesseract-ocr/tesseract.git synced 2024-12-12 07:29:07 +08:00
Code Issues Actions 9 Packages Projects Releases Wiki Activity
2fcea93846
tesseract/classify/adaptmatch.cpp
theraysmith@gmail.com 2fcea93846 Fixed issues 1081-1090 
git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@1046 d0cd1f9f-072b-0410-8dd7-cf729c803f20
2014-02-04 02:23:18 +00:00

2390 lines
89 KiB
C++
Raw Blame History

/******************************************************************************
** Filename: adaptmatch.c
** Purpose: High level adaptive matcher.
** Author: Dan Johnson
** History: Mon Mar 11 10:00:10 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 Files and Type Defines
-----------------------------------------------------------------------------*/
#ifdef HAVE_CONFIG_H
#include "config_auto.h"
#endif
#include <ctype.h>
#include "ambigs.h"
#include "blobclass.h"
#include "blobs.h"
#include "helpers.h"
#include "normfeat.h"
#include "mfoutline.h"
#include "picofeat.h"
#include "float2int.h"
#include "outfeat.h"
#include "emalloc.h"
#include "intfx.h"
#include "efio.h"
#include "normmatch.h"
#include "ndminx.h"
#include "intproto.h"
#include "const.h"
#include "globals.h"
#include "werd.h"
#include "callcpp.h"
#include "pageres.h"
#include "params.h"
#include "classify.h"
#include "shapetable.h"
#include "tessclassifier.h"
#include "trainingsample.h"
#include "unicharset.h"
#include "dict.h"
#include "featdefs.h"
#include "genericvector.h"
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <math.h>
#ifdef __UNIX__
#include <assert.h>
#endif
#define ADAPT_TEMPLATE_SUFFIX ".a"
#define MAX_MATCHES 10
#define UNLIKELY_NUM_FEAT 200
#define NO_DEBUG 0
#define MAX_ADAPTABLE_WERD_SIZE 40
#define ADAPTABLE_WERD_ADJUSTMENT (0.05)
#define Y_DIM_OFFSET (Y_SHIFT - BASELINE_Y_SHIFT)
#define WORST_POSSIBLE_RATING (1.0)
struct ScoredClass {
CLASS_ID unichar_id;
int shape_id;
FLOAT32 rating;
bool adapted;
inT16 config;
inT16 fontinfo_id;
inT16 fontinfo_id2;
};
struct ADAPT_RESULTS {
inT32 BlobLength;
bool HasNonfragment;
GenericVector<ScoredClass> match;
ScoredClass best_match;
GenericVector<CP_RESULT_STRUCT> CPResults;
/// Initializes data members to the default values. Sets the initial
/// rating of each class to be the worst possible rating (1.0).
inline void Initialize() {
BlobLength = MAX_INT32;
HasNonfragment = false;
best_match.unichar_id = NO_CLASS;
best_match.shape_id = -1;
best_match.rating = WORST_POSSIBLE_RATING;
best_match.adapted = false;
best_match.config = 0;
best_match.fontinfo_id = kBlankFontinfoId;
best_match.fontinfo_id2 = kBlankFontinfoId;
}
};
struct PROTO_KEY {
ADAPT_TEMPLATES Templates;
CLASS_ID ClassId;
int ConfigId;
};
/*-----------------------------------------------------------------------------
Private Macros
-----------------------------------------------------------------------------*/
#define MarginalMatch(Rating) \
((Rating) > matcher_great_threshold)
/*-----------------------------------------------------------------------------
Private Function Prototypes
-----------------------------------------------------------------------------*/
int CompareByRating(const void *arg1, const void *arg2);
ScoredClass *FindScoredUnichar(ADAPT_RESULTS *results, UNICHAR_ID id);
ScoredClass ScoredUnichar(ADAPT_RESULTS *results, UNICHAR_ID id);
void InitMatcherRatings(register FLOAT32 *Rating);
int MakeTempProtoPerm(void *item1, void *item2);
void SetAdaptiveThreshold(FLOAT32 Threshold);
/*-----------------------------------------------------------------------------
Public Code
-----------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
namespace tesseract {
/**
* This routine calls the adaptive matcher
* which returns (in an array) the class id of each
* class matched.
*
* It also returns the number of classes matched.
* For each class matched it places the best rating
* found for that class into the Ratings array.
*
* Bad matches are then removed so that they don't
* need to be sorted. The remaining good matches are
* then sorted and converted to choices.
*
* This routine also performs some simple speckle
* filtering.
*
* @note Exceptions: none
* @note History: Mon Mar 11 10:00:58 1991, DSJ, Created.
*
* @param Blob blob to be classified
* @param[out] Choices List of choices found by adaptive matcher.
* filled on return with the choices found by the
* class pruner and the ratings therefrom. Also
* contains the detailed results of the integer matcher.
*
*/
void Classify::AdaptiveClassifier(TBLOB *Blob, BLOB_CHOICE_LIST *Choices) {
assert(Choices != NULL);
ADAPT_RESULTS *Results = new ADAPT_RESULTS;
Results->Initialize();
ASSERT_HOST(AdaptedTemplates != NULL);
DoAdaptiveMatch(Blob, Results);
RemoveBadMatches(Results);
Results->match.sort(CompareByRating);
RemoveExtraPuncs(Results);
ConvertMatchesToChoices(Blob->denorm(), Blob->bounding_box(), Results,
Choices);
if (matcher_debug_level >= 1) {
cprintf ("AD Matches = ");
PrintAdaptiveMatchResults(stdout, Results);
}
if (LargeSpeckle(*Blob) || Choices->length() == 0)
AddLargeSpeckleTo(Results->BlobLength, Choices);
#ifndef GRAPHICS_DISABLED
if (classify_enable_adaptive_debugger)
DebugAdaptiveClassifier(Blob, Results);
#endif
delete Results;
} /* AdaptiveClassifier */
// If *win is NULL, sets it to a new ScrollView() object with title msg.
// Clears the window and draws baselines.
void Classify::RefreshDebugWindow(ScrollView **win, const char *msg,
int y_offset, const TBOX &wbox) {
#ifndef GRAPHICS_DISABLED
const int kSampleSpaceWidth = 500;
if (*win == NULL) {
*win = new ScrollView(msg, 100, y_offset, kSampleSpaceWidth * 2, 200,
kSampleSpaceWidth * 2, 200, true);
}
(*win)->Clear();
(*win)->Pen(64, 64, 64);
(*win)->Line(-kSampleSpaceWidth, kBlnBaselineOffset,
kSampleSpaceWidth, kBlnBaselineOffset);
(*win)->Line(-kSampleSpaceWidth, kBlnXHeight + kBlnBaselineOffset,
kSampleSpaceWidth, kBlnXHeight + kBlnBaselineOffset);
(*win)->ZoomToRectangle(wbox.left(), wbox.top(),
wbox.right(), wbox.bottom());
#endif // GRAPHICS_DISABLED
}
// Learns the given word using its chopped_word, seam_array, denorm,
// box_word, best_state, and correct_text to learn both correctly and
// incorrectly segmented blobs. If filename is not NULL, then LearnBlob
// is called and the data will be written to a file for static training.
// Otherwise AdaptToBlob is called for adaption within a document.
// If rejmap is not NULL, then only chars with a rejmap entry of '1' will
// be learned, otherwise all chars with good correct_text are learned.
void Classify::LearnWord(const char* filename, WERD_RES *word) {
int word_len = word->correct_text.size();
if (word_len == 0) return;
float* thresholds = NULL;
if (filename == NULL) {
// Adaption mode.
if (!EnableLearning || word->best_choice == NULL)
return; // Can't or won't adapt.
if (classify_learning_debug_level >= 1)
tprintf("\n\nAdapting to word = %s\n",
word->best_choice->debug_string().string());
thresholds = new float[word_len];
word->ComputeAdaptionThresholds(certainty_scale,
matcher_perfect_threshold,
matcher_good_threshold,
matcher_rating_margin, thresholds);
}
int start_blob = 0;
#ifndef GRAPHICS_DISABLED
if (classify_debug_character_fragments) {
if (learn_fragmented_word_debug_win_ != NULL) {
window_wait(learn_fragmented_word_debug_win_);
}
RefreshDebugWindow(&learn_fragments_debug_win_, "LearnPieces", 400,
word->chopped_word->bounding_box());
RefreshDebugWindow(&learn_fragmented_word_debug_win_, "LearnWord", 200,
word->chopped_word->bounding_box());
word->chopped_word->plot(learn_fragmented_word_debug_win_);
ScrollView::Update();
}
#endif // GRAPHICS_DISABLED
for (int ch = 0; ch < word_len; ++ch) {
if (classify_debug_character_fragments) {
tprintf("\nLearning %s\n", word->correct_text[ch].string());
}
if (word->correct_text[ch].length() > 0) {
float threshold = thresholds != NULL ? thresholds[ch] : 0.0f;
LearnPieces(filename, start_blob, word->best_state[ch],
threshold, CST_WHOLE, word->correct_text[ch].string(), word);
if (word->best_state[ch] > 1 && !disable_character_fragments) {
// Check that the character breaks into meaningful fragments
// that each match a whole character with at least
// classify_character_fragments_garbage_certainty_threshold
bool garbage = false;
int frag;
for (frag = 0; frag < word->best_state[ch]; ++frag) {
TBLOB* frag_blob = word->chopped_word->blobs[start_blob + frag];
if (classify_character_fragments_garbage_certainty_threshold < 0) {
garbage |= LooksLikeGarbage(frag_blob);
}
}
// Learn the fragments.
if (!garbage) {
bool pieces_all_natural = word->PiecesAllNatural(start_blob,
word->best_state[ch]);
if (pieces_all_natural || !prioritize_division) {
for (frag = 0; frag < word->best_state[ch]; ++frag) {
GenericVector<STRING> tokens;
word->correct_text[ch].split(' ', &tokens);
tokens[0] = CHAR_FRAGMENT::to_string(
tokens[0].string(), frag, word->best_state[ch],
pieces_all_natural);
STRING full_string;
for (int i = 0; i < tokens.size(); i++) {
full_string += tokens[i];
if (i != tokens.size() - 1)
full_string += ' ';
}
LearnPieces(filename, start_blob + frag, 1,
threshold, CST_FRAGMENT, full_string.string(), word);
}
}
}
}
// TODO(rays): re-enable this part of the code when we switch to the
// new classifier that needs to see examples of garbage.
/*
if (word->best_state[ch] > 1) {
// If the next blob is good, make junk with the rightmost fragment.
if (ch + 1 < word_len && word->correct_text[ch + 1].length() > 0) {
LearnPieces(filename, start_blob + word->best_state[ch] - 1,
word->best_state[ch + 1] + 1,
threshold, CST_IMPROPER, INVALID_UNICHAR, word);
}
// If the previous blob is good, make junk with the leftmost fragment.
if (ch > 0 && word->correct_text[ch - 1].length() > 0) {
LearnPieces(filename, start_blob - word->best_state[ch - 1],
word->best_state[ch - 1] + 1,
threshold, CST_IMPROPER, INVALID_UNICHAR, word);
}
}
// If the next blob is good, make a join with it.
if (ch + 1 < word_len && word->correct_text[ch + 1].length() > 0) {
STRING joined_text = word->correct_text[ch];
joined_text += word->correct_text[ch + 1];
LearnPieces(filename, start_blob,
word->best_state[ch] + word->best_state[ch + 1],
threshold, CST_NGRAM, joined_text.string(), word);
}
*/
}
start_blob += word->best_state[ch];
}
delete [] thresholds;
} // LearnWord.
// Builds a blob of length fragments, from the word, starting at start,
// and then learns it, as having the given correct_text.
// If filename is not NULL, then LearnBlob
// is called and the data will be written to a file for static training.
// Otherwise AdaptToBlob is called for adaption within a document.
// threshold is a magic number required by AdaptToChar and generated by
// ComputeAdaptionThresholds.
// Although it can be partly inferred from the string, segmentation is
// provided to explicitly clarify the character segmentation.
void Classify::LearnPieces(const char* filename, int start, int length,
float threshold, CharSegmentationType segmentation,
const char* correct_text, WERD_RES *word) {
// TODO(daria) Remove/modify this if/when we want
// to train and/or adapt to n-grams.
if (segmentation != CST_WHOLE &&
(segmentation != CST_FRAGMENT || disable_character_fragments))
return;
if (length > 1) {
join_pieces(word->seam_array, start, start + length - 1,
word->chopped_word);
}
TBLOB* blob = word->chopped_word->blobs[start];
// Rotate the blob if needed for classification.
TBLOB* rotated_blob = blob->ClassifyNormalizeIfNeeded();
if (rotated_blob == NULL)
rotated_blob = blob;
#ifndef GRAPHICS_DISABLED
// Draw debug windows showing the blob that is being learned if needed.
if (strcmp(classify_learn_debug_str.string(), correct_text) == 0) {
RefreshDebugWindow(&learn_debug_win_, "LearnPieces", 600,
word->chopped_word->bounding_box());
rotated_blob->plot(learn_debug_win_, ScrollView::GREEN, ScrollView::BROWN);
learn_debug_win_->Update();
window_wait(learn_debug_win_);
}
if (classify_debug_character_fragments && segmentation == CST_FRAGMENT) {
ASSERT_HOST(learn_fragments_debug_win_ != NULL); // set up in LearnWord
blob->plot(learn_fragments_debug_win_,
ScrollView::BLUE, ScrollView::BROWN);
learn_fragments_debug_win_->Update();
}
#endif // GRAPHICS_DISABLED
if (filename != NULL) {
classify_norm_method.set_value(character); // force char norm spc 30/11/93
tess_bn_matching.set_value(false); // turn it off
tess_cn_matching.set_value(false);
DENORM bl_denorm, cn_denorm;
INT_FX_RESULT_STRUCT fx_info;
SetupBLCNDenorms(*rotated_blob, classify_nonlinear_norm,
&bl_denorm, &cn_denorm, &fx_info);
LearnBlob(feature_defs_, filename, rotated_blob, bl_denorm, cn_denorm,
fx_info, correct_text);
} else if (unicharset.contains_unichar(correct_text)) {
UNICHAR_ID class_id = unicharset.unichar_to_id(correct_text);
int font_id = word->fontinfo != NULL
? fontinfo_table_.get_id(*word->fontinfo)
: 0;
if (classify_learning_debug_level >= 1)
tprintf("Adapting to char = %s, thr= %g font_id= %d\n",
unicharset.id_to_unichar(class_id), threshold, font_id);
// If filename is not NULL we are doing recognition
// (as opposed to training), so we must have already set word fonts.
AdaptToChar(rotated_blob, class_id, font_id, threshold);
} else if (classify_debug_level >= 1) {
tprintf("Can't adapt to %s not in unicharset\n", correct_text);
}
if (rotated_blob != blob) {
delete rotated_blob;
}
break_pieces(word->seam_array, start, start + length - 1, word->chopped_word);
} // LearnPieces.
/*---------------------------------------------------------------------------*/
/**
* This routine performs cleanup operations
* on the adaptive classifier. It should be called
* before the program is terminated. Its main function
* is to save the adapted templates to a file.
*
* Globals:
* - #AdaptedTemplates current set of adapted templates
* - #classify_save_adapted_templates TRUE if templates should be saved
* - #classify_enable_adaptive_matcher TRUE if adaptive matcher is enabled
*
* @note Exceptions: none
* @note History: Tue Mar 19 14:37:06 1991, DSJ, Created.
*/
void Classify::EndAdaptiveClassifier() {
STRING Filename;
FILE *File;
#ifndef SECURE_NAMES
if (AdaptedTemplates != NULL &&
classify_enable_adaptive_matcher && classify_save_adapted_templates) {
Filename = imagefile + ADAPT_TEMPLATE_SUFFIX;
File = fopen (Filename.string(), "wb");
if (File == NULL)
cprintf ("Unable to save adapted templates to %s!\n", Filename.string());
else {
cprintf ("\nSaving adapted templates to %s ...", Filename.string());
fflush(stdout);
WriteAdaptedTemplates(File, AdaptedTemplates);
cprintf ("\n");
fclose(File);
}
}
#endif
if (AdaptedTemplates != NULL) {
free_adapted_templates(AdaptedTemplates);
AdaptedTemplates = NULL;
}
if (PreTrainedTemplates != NULL) {
free_int_templates(PreTrainedTemplates);
PreTrainedTemplates = NULL;
}
getDict().EndDangerousAmbigs();
FreeNormProtos();
if (AllProtosOn != NULL) {
FreeBitVector(AllProtosOn);
FreeBitVector(AllConfigsOn);
FreeBitVector(AllConfigsOff);
FreeBitVector(TempProtoMask);
AllProtosOn = NULL;
AllConfigsOn = NULL;
AllConfigsOff = NULL;
TempProtoMask = NULL;
}
delete shape_table_;
shape_table_ = NULL;
if (static_classifier_ != NULL) {
delete static_classifier_;
static_classifier_ = NULL;
}
} /* EndAdaptiveClassifier */
/*---------------------------------------------------------------------------*/
/**
* This routine reads in the training
* information needed by the adaptive classifier
* and saves it into global variables.
* Parameters:
* load_pre_trained_templates Indicates whether the pre-trained
* templates (inttemp, normproto and pffmtable components)
* should be lodaded. Should only be set to true if the
* necesary classifier components are present in the
* [lang].traineddata file.
* Globals:
* BuiltInTemplatesFile file to get built-in temps from
* BuiltInCutoffsFile file to get avg. feat per class from
* classify_use_pre_adapted_templates
* enables use of pre-adapted templates
* @note History: Mon Mar 11 12:49:34 1991, DSJ, Created.
*/
void Classify::InitAdaptiveClassifier(bool load_pre_trained_templates) {
if (!classify_enable_adaptive_matcher)
return;
if (AllProtosOn != NULL)
EndAdaptiveClassifier(); // Don't leak with multiple inits.
// If there is no language_data_path_prefix, the classifier will be
// adaptive only.
if (language_data_path_prefix.length() > 0 &&
load_pre_trained_templates) {
ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_INTTEMP));
PreTrainedTemplates =
ReadIntTemplates(tessdata_manager.GetDataFilePtr());
if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded inttemp\n");
if (tessdata_manager.SeekToStart(TESSDATA_SHAPE_TABLE)) {
shape_table_ = new ShapeTable(unicharset);
if (!shape_table_->DeSerialize(tessdata_manager.swap(),
tessdata_manager.GetDataFilePtr())) {
tprintf("Error loading shape table!\n");
delete shape_table_;
shape_table_ = NULL;
} else if (tessdata_manager.DebugLevel() > 0) {
tprintf("Successfully loaded shape table!\n");
}
}
ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_PFFMTABLE));
ReadNewCutoffs(tessdata_manager.GetDataFilePtr(),
tessdata_manager.swap(),
tessdata_manager.GetEndOffset(TESSDATA_PFFMTABLE),
CharNormCutoffs);
if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded pffmtable\n");
ASSERT_HOST(tessdata_manager.SeekToStart(TESSDATA_NORMPROTO));
NormProtos =
ReadNormProtos(tessdata_manager.GetDataFilePtr(),
tessdata_manager.GetEndOffset(TESSDATA_NORMPROTO));
if (tessdata_manager.DebugLevel() > 0) tprintf("Loaded normproto\n");
static_classifier_ = new TessClassifier(false, this);
}
im_.Init(&classify_debug_level);
InitIntegerFX();
AllProtosOn = NewBitVector(MAX_NUM_PROTOS);
AllConfigsOn = NewBitVector(MAX_NUM_CONFIGS);
AllConfigsOff = NewBitVector(MAX_NUM_CONFIGS);
TempProtoMask = NewBitVector(MAX_NUM_PROTOS);
set_all_bits(AllProtosOn, WordsInVectorOfSize(MAX_NUM_PROTOS));
set_all_bits(AllConfigsOn, WordsInVectorOfSize(MAX_NUM_CONFIGS));
zero_all_bits(AllConfigsOff, WordsInVectorOfSize(MAX_NUM_CONFIGS));
for (int i = 0; i < MAX_NUM_CLASSES; i++) {
BaselineCutoffs[i] = 0;
}
if (classify_use_pre_adapted_templates) {
FILE *File;
STRING Filename;
Filename = imagefile;
Filename += ADAPT_TEMPLATE_SUFFIX;
File = fopen(Filename.string(), "rb");
if (File == NULL) {
AdaptedTemplates = NewAdaptedTemplates(true);
} else {
#ifndef SECURE_NAMES
cprintf("\nReading pre-adapted templates from %s ...\n",
Filename.string());
fflush(stdout);
#endif
AdaptedTemplates = ReadAdaptedTemplates(File);
cprintf("\n");
fclose(File);
PrintAdaptedTemplates(stdout, AdaptedTemplates);
for (int i = 0; i < AdaptedTemplates->Templates->NumClasses; i++) {
BaselineCutoffs[i] = CharNormCutoffs[i];
}
}
} else {
if (AdaptedTemplates != NULL)
free_adapted_templates(AdaptedTemplates);
AdaptedTemplates = NewAdaptedTemplates(true);
}
} /* InitAdaptiveClassifier */
void Classify::ResetAdaptiveClassifierInternal() {
if (classify_learning_debug_level > 0) {
tprintf("Resetting adaptive classifier (NumAdaptationsFailed=%d)\n",
NumAdaptationsFailed);
}
free_adapted_templates(AdaptedTemplates);
AdaptedTemplates = NewAdaptedTemplates(true);
NumAdaptationsFailed = 0;
}
/*---------------------------------------------------------------------------*/
/**
* This routine prepares the adaptive
* matcher for the start
* of the first pass. Learning is enabled (unless it
* is disabled for the whole program).
*
* @note this is somewhat redundant, it simply says that if learning is
* enabled then it will remain enabled on the first pass. If it is
* disabled, then it will remain disabled. This is only put here to
* make it very clear that learning is controlled directly by the global
* setting of EnableLearning.
*
* Globals:
* - #EnableLearning
* set to TRUE by this routine
*
* @note Exceptions: none
* @note History: Mon Apr 15 16:39:29 1991, DSJ, Created.
*/
void Classify::SettupPass1() {
EnableLearning = classify_enable_learning;
getDict().SettupStopperPass1();
} /* SettupPass1 */
/*---------------------------------------------------------------------------*/
/**
* This routine prepares the adaptive
* matcher for the start of the second pass. Further
* learning is disabled.
*
* Globals:
* - #EnableLearning set to FALSE by this routine
*
* @note Exceptions: none
* @note History: Mon Apr 15 16:39:29 1991, DSJ, Created.
*/
void Classify::SettupPass2() {
EnableLearning = FALSE;
getDict().SettupStopperPass2();
} /* SettupPass2 */
/*---------------------------------------------------------------------------*/
/**
* This routine creates a new adapted
* class and uses Blob as the model for the first
* config in that class.
*
* @param Blob blob to model new class after
* @param ClassId id of the class to be initialized
* @param FontinfoId font information inferred from pre-trained templates
* @param Class adapted class to be initialized
* @param Templates adapted templates to add new class to
*
* Globals:
* - #AllProtosOn dummy mask with all 1's
* - BaselineCutoffs kludge needed to get cutoffs
* - #PreTrainedTemplates kludge needed to get cutoffs
*
* @note Exceptions: none
* @note History: Thu Mar 14 12:49:39 1991, DSJ, Created.
*/
void Classify::InitAdaptedClass(TBLOB *Blob,
CLASS_ID ClassId,
int FontinfoId,
ADAPT_CLASS Class,
ADAPT_TEMPLATES Templates) {
FEATURE_SET Features;
int Fid, Pid;
FEATURE Feature;
int NumFeatures;
TEMP_PROTO TempProto;
PROTO Proto;
INT_CLASS IClass;
TEMP_CONFIG Config;
classify_norm_method.set_value(baseline);
Features = ExtractOutlineFeatures(Blob);
NumFeatures = Features->NumFeatures;
if (NumFeatures > UNLIKELY_NUM_FEAT || NumFeatures <= 0) {
FreeFeatureSet(Features);
return;
}
Config = NewTempConfig(NumFeatures - 1, FontinfoId);
TempConfigFor(Class, 0) = Config;
/* this is a kludge to construct cutoffs for adapted templates */
if (Templates == AdaptedTemplates)
BaselineCutoffs[ClassId] = CharNormCutoffs[ClassId];
IClass = ClassForClassId (Templates->Templates, ClassId);
for (Fid = 0; Fid < Features->NumFeatures; Fid++) {
Pid = AddIntProto (IClass);
assert (Pid != NO_PROTO);
Feature = Features->Features[Fid];
TempProto = NewTempProto ();
Proto = &(TempProto->Proto);
/* compute proto params - NOTE that Y_DIM_OFFSET must be used because
ConvertProto assumes that the Y dimension varies from -0.5 to 0.5
instead of the -0.25 to 0.75 used in baseline normalization */
Proto->Angle = Feature->Params[OutlineFeatDir];
Proto->X = Feature->Params[OutlineFeatX];
Proto->Y = Feature->Params[OutlineFeatY] - Y_DIM_OFFSET;
Proto->Length = Feature->Params[OutlineFeatLength];
FillABC(Proto);
TempProto->ProtoId = Pid;
SET_BIT (Config->Protos, Pid);
ConvertProto(Proto, Pid, IClass);
AddProtoToProtoPruner(Proto, Pid, IClass,
classify_learning_debug_level >= 2);
Class->TempProtos = push (Class->TempProtos, TempProto);
}
FreeFeatureSet(Features);
AddIntConfig(IClass);
ConvertConfig (AllProtosOn, 0, IClass);
if (classify_learning_debug_level >= 1) {
cprintf ("Added new class '%s' with class id %d and %d protos.\n",
unicharset.id_to_unichar(ClassId), ClassId, NumFeatures);
if (classify_learning_debug_level > 1)
DisplayAdaptedChar(Blob, IClass);
}
if (IsEmptyAdaptedClass(Class))
(Templates->NumNonEmptyClasses)++;
} /* InitAdaptedClass */
/*---------------------------------------------------------------------------*/
/**
* This routine sets up the feature
* extractor to extract baseline normalized
* pico-features.
*
* The extracted pico-features are converted
* to integer form and placed in IntFeatures. The
* original floating-pt. features are returned in
* FloatFeatures.
*
* Globals: none
* @param Blob blob to extract features from
* @param[out] IntFeatures array to fill with integer features
* @param[out] FloatFeatures place to return actual floating-pt features
*
* @return Number of pico-features returned (0 if
* an error occurred)
* @note Exceptions: none
* @note History: Tue Mar 12 17:55:18 1991, DSJ, Created.
*/
int Classify::GetAdaptiveFeatures(TBLOB *Blob,
INT_FEATURE_ARRAY IntFeatures,
FEATURE_SET *FloatFeatures) {
FEATURE_SET Features;
int NumFeatures;
classify_norm_method.set_value(baseline);
Features = ExtractPicoFeatures(Blob);
NumFeatures = Features->NumFeatures;
if (NumFeatures > UNLIKELY_NUM_FEAT) {
FreeFeatureSet(Features);
return 0;
}
ComputeIntFeatures(Features, IntFeatures);
*FloatFeatures = Features;
return NumFeatures;
} /* GetAdaptiveFeatures */
/*-----------------------------------------------------------------------------
Private Code
-----------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
/**
* Return TRUE if the specified word is
* acceptable for adaptation.
*
* Globals: none
*
* @param Word current word
* @param BestChoiceWord best overall choice for word with context
*
* @return TRUE or FALSE
* @note Exceptions: none
* @note History: Thu May 30 14:25:06 1991, DSJ, Created.
*/
bool Classify::AdaptableWord(WERD_RES* word) {
if (word->best_choice == NULL) return false;
int BestChoiceLength = word->best_choice->length();
float adaptable_score =
getDict().segment_penalty_dict_case_ok + ADAPTABLE_WERD_ADJUSTMENT;
return // rules that apply in general - simplest to compute first
BestChoiceLength > 0 &&
BestChoiceLength == word->rebuild_word->NumBlobs() &&
BestChoiceLength <= MAX_ADAPTABLE_WERD_SIZE &&
// This basically ensures that the word is at least a dictionary match
// (freq word, user word, system dawg word, etc).
// Since all the other adjustments will make adjust factor higher
// than higher than adaptable_score=1.1+0.05=1.15
// Since these are other flags that ensure that the word is dict word,
// this check could be at times redundant.
word->best_choice->adjust_factor() <= adaptable_score &&
// Make sure that alternative choices are not dictionary words.
word->AlternativeChoiceAdjustmentsWorseThan(adaptable_score);
}
/*---------------------------------------------------------------------------*/
/**
* @param Blob blob to add to templates for ClassId
* @param ClassId class to add blob to
* @param FontinfoId font information from pre-trained templates
* @param Threshold minimum match rating to existing template
*
* Globals:
* - AdaptedTemplates current set of adapted templates
* - AllProtosOn dummy mask to match against all protos
* - AllConfigsOn dummy mask to match against all configs
*
* @return none
* @note Exceptions: none
* @note History: Thu Mar 14 09:36:03 1991, DSJ, Created.
*/
void Classify::AdaptToChar(TBLOB *Blob,
CLASS_ID ClassId,
int FontinfoId,
FLOAT32 Threshold) {
int NumFeatures;
INT_FEATURE_ARRAY IntFeatures;
INT_RESULT_STRUCT IntResult;
INT_CLASS IClass;
ADAPT_CLASS Class;
TEMP_CONFIG TempConfig;
FEATURE_SET FloatFeatures;
int NewTempConfigId;
if (!LegalClassId (ClassId))
return;
Class = AdaptedTemplates->Class[ClassId];
assert(Class != NULL);
if (IsEmptyAdaptedClass(Class)) {
InitAdaptedClass(Blob, ClassId, FontinfoId, Class, AdaptedTemplates);
}
else {
IClass = ClassForClassId (AdaptedTemplates->Templates, ClassId);
NumFeatures = GetAdaptiveFeatures(Blob, IntFeatures, &FloatFeatures);
if (NumFeatures <= 0)
return;
// Only match configs with the matching font.
BIT_VECTOR MatchingFontConfigs = NewBitVector(MAX_NUM_PROTOS);
for (int cfg = 0; cfg < IClass->NumConfigs; ++cfg) {
if (GetFontinfoId(Class, cfg) == FontinfoId) {
SET_BIT(MatchingFontConfigs, cfg);
} else {
reset_bit(MatchingFontConfigs, cfg);
}
}
im_.Match(IClass, AllProtosOn, MatchingFontConfigs,
NumFeatures, IntFeatures,
&IntResult, classify_adapt_feature_threshold,
NO_DEBUG, matcher_debug_separate_windows);
FreeBitVector(MatchingFontConfigs);
SetAdaptiveThreshold(Threshold);
if (IntResult.Rating <= Threshold) {
if (ConfigIsPermanent (Class, IntResult.Config)) {
if (classify_learning_debug_level >= 1)
cprintf ("Found good match to perm config %d = %4.1f%%.\n",
IntResult.Config, (1.0 - IntResult.Rating) * 100.0);
FreeFeatureSet(FloatFeatures);
return;
}
TempConfig = TempConfigFor (Class, IntResult.Config);
IncreaseConfidence(TempConfig);
if (TempConfig->NumTimesSeen > Class->MaxNumTimesSeen) {
Class->MaxNumTimesSeen = TempConfig->NumTimesSeen;
}
if (classify_learning_debug_level >= 1)
cprintf ("Increasing reliability of temp config %d to %d.\n",
IntResult.Config, TempConfig->NumTimesSeen);
if (TempConfigReliable(ClassId, TempConfig)) {
MakePermanent(AdaptedTemplates, ClassId, IntResult.Config, Blob);
UpdateAmbigsGroup(ClassId, Blob);
}
}
else {
if (classify_learning_debug_level >= 1) {
cprintf ("Found poor match to temp config %d = %4.1f%%.\n",
IntResult.Config, (1.0 - IntResult.Rating) * 100.0);
if (classify_learning_debug_level > 2)
DisplayAdaptedChar(Blob, IClass);
}
NewTempConfigId = MakeNewTemporaryConfig(AdaptedTemplates,
ClassId,
FontinfoId,
NumFeatures,
IntFeatures,
FloatFeatures);
if (NewTempConfigId >= 0 &&
TempConfigReliable(ClassId, TempConfigFor(Class, NewTempConfigId))) {
MakePermanent(AdaptedTemplates, ClassId, NewTempConfigId, Blob);
UpdateAmbigsGroup(ClassId, Blob);
}
#ifndef GRAPHICS_DISABLED
if (classify_learning_debug_level > 1) {
DisplayAdaptedChar(Blob, IClass);
}
#endif
}
FreeFeatureSet(FloatFeatures);
}
} /* AdaptToChar */
void Classify::DisplayAdaptedChar(TBLOB* blob, INT_CLASS_STRUCT* int_class) {
#ifndef GRAPHICS_DISABLED
INT_FX_RESULT_STRUCT fx_info;
GenericVector<INT_FEATURE_STRUCT> bl_features;
TrainingSample* sample =
BlobToTrainingSample(*blob, classify_nonlinear_norm, &fx_info,
&bl_features);
if (sample == NULL) return;
INT_RESULT_STRUCT IntResult;
im_.Match(int_class, AllProtosOn, AllConfigsOn,
bl_features.size(), &bl_features[0],
&IntResult, classify_adapt_feature_threshold,
NO_DEBUG, matcher_debug_separate_windows);
cprintf ("Best match to temp config %d = %4.1f%%.\n",
IntResult.Config, (1.0 - IntResult.Rating) * 100.0);
if (classify_learning_debug_level >= 2) {
uinT32 ConfigMask;
ConfigMask = 1 << IntResult.Config;
ShowMatchDisplay();
im_.Match(int_class, AllProtosOn, (BIT_VECTOR)&ConfigMask,
bl_features.size(), &bl_features[0],
&IntResult, classify_adapt_feature_threshold,
6 | 0x19, matcher_debug_separate_windows);
UpdateMatchDisplay();
}
#endif
}
/*---------------------------------------------------------------------------*/
/**
* This routine adds the result of a classification into
* Results. If the new rating is much worse than the current
* best rating, it is not entered into results because it
* would end up being stripped later anyway. If the new rating
* is better than the old rating for the class, it replaces the
* old rating. If this is the first rating for the class, the
* class is added to the list of matched classes in Results.
* If the new rating is better than the best so far, it
* becomes the best so far.
*
* Globals:
* - #matcher_bad_match_pad defines limits of an acceptable match
*
* @param[out] results results to add new result to
* @param class_id class of new result
* @param shape_id shape index
* @param rating rating of new result
* @param adapted adapted match or not
* @param config config id of new result
* @param fontinfo_id font information of the new result
* @param fontinfo_id2 font information of the 2nd choice result
*
* @note Exceptions: none
* @note History: Tue Mar 12 18:19:29 1991, DSJ, Created.
*/
void Classify::AddNewResult(ADAPT_RESULTS *results,
CLASS_ID class_id,
int shape_id,
FLOAT32 rating,
bool adapted,
int config,
int fontinfo_id,
int fontinfo_id2) {
ScoredClass *old_match = FindScoredUnichar(results, class_id);
ScoredClass match =
{ class_id,
shape_id,
rating,
adapted,
static_cast<inT16>(config),
static_cast<inT16>(fontinfo_id),
static_cast<inT16>(fontinfo_id2) };
if (rating > results->best_match.rating + matcher_bad_match_pad ||
(old_match && rating >= old_match->rating))
return;
if (!unicharset.get_fragment(class_id))
results->HasNonfragment = true;
if (old_match)
old_match->rating = rating;
else
results->match.push_back(match);
if (rating < results->best_match.rating &&
// Ensure that fragments do not affect best rating, class and config.
// This is needed so that at least one non-fragmented character is
// always present in the results.
// TODO(daria): verify that this helps accuracy and does not
// hurt performance.
!unicharset.get_fragment(class_id)) {
results->best_match = match;
}
} /* AddNewResult */
/*---------------------------------------------------------------------------*/
/**
* This routine is identical to CharNormClassifier()
* except that it does no class pruning. It simply matches
* the unknown blob against the classes listed in
* Ambiguities.
*
* Globals:
* - #AllProtosOn mask that enables all protos
* - #AllConfigsOn mask that enables all configs
*
* @param Blob blob to be classified
* @param Templates built-in templates to classify against
* @param Classes adapted class templates
* @param Ambiguities array of class id's to match against
* @param[out] Results place to put match results
*
* @note Exceptions: none
* @note History: Tue Mar 12 19:40:36 1991, DSJ, Created.
*/
void Classify::AmbigClassifier(
const GenericVector<INT_FEATURE_STRUCT>& int_features,
const INT_FX_RESULT_STRUCT& fx_info,
const TBLOB *blob,
INT_TEMPLATES templates,
ADAPT_CLASS *classes,
UNICHAR_ID *ambiguities,
ADAPT_RESULTS *results) {
if (int_features.empty()) return;
uinT8* CharNormArray = new uinT8[unicharset.size()];
INT_RESULT_STRUCT IntResult;
results->BlobLength = GetCharNormFeature(fx_info, templates, NULL,
CharNormArray);
bool debug = matcher_debug_level >= 2 || classify_debug_level > 1;
if (debug)
tprintf("AM Matches = ");
int top = blob->bounding_box().top();
int bottom = blob->bounding_box().bottom();
while (*ambiguities >= 0) {
CLASS_ID class_id = *ambiguities;
im_.Match(ClassForClassId(templates, class_id),
AllProtosOn, AllConfigsOn,
int_features.size(), &int_features[0],
&IntResult,
classify_adapt_feature_threshold, NO_DEBUG,
matcher_debug_separate_windows);
ExpandShapesAndApplyCorrections(NULL, debug, class_id, bottom, top, 0,
results->BlobLength,
classify_integer_matcher_multiplier,
CharNormArray, IntResult, results);
ambiguities++;
}
delete [] CharNormArray;
} /* AmbigClassifier */
/*---------------------------------------------------------------------------*/
/// Factored-out calls to IntegerMatcher based on class pruner results.
/// Returns integer matcher results inside CLASS_PRUNER_RESULTS structure.
void Classify::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) {
int top = blob_box.top();
int bottom = blob_box.bottom();
for (int c = 0; c < results.size(); c++) {
CLASS_ID class_id = results[c].Class;
INT_RESULT_STRUCT& int_result = results[c].IMResult;
BIT_VECTOR protos = classes != NULL ? classes[class_id]->PermProtos
: AllProtosOn;
BIT_VECTOR configs = classes != NULL ? classes[class_id]->PermConfigs
: AllConfigsOn;
im_.Match(ClassForClassId(templates, class_id),
protos, configs,
num_features, features,
&int_result, classify_adapt_feature_threshold, debug,
matcher_debug_separate_windows);
bool debug = matcher_debug_level >= 2 || classify_debug_level > 1;
ExpandShapesAndApplyCorrections(classes, debug, class_id, bottom, top,
results[c].Rating,
final_results->BlobLength,
matcher_multiplier, norm_factors,
int_result, final_results);
}
}
// Converts configs to fonts, and if the result is not adapted, and a
// shape_table_ is present, the shape is expanded to include all
// unichar_ids represented, before applying a set of corrections to the
// distance rating in int_result, (see ComputeCorrectedRating.)
// The results are added to the final_results output.
void Classify::ExpandShapesAndApplyCorrections(
ADAPT_CLASS* classes, bool debug, int class_id, int bottom, int top,
float cp_rating, int blob_length, int matcher_multiplier,
const uinT8* cn_factors,
INT_RESULT_STRUCT& int_result, ADAPT_RESULTS* final_results) {
// Compute the fontinfo_ids.
int fontinfo_id = kBlankFontinfoId;
int fontinfo_id2 = kBlankFontinfoId;
if (classes != NULL) {
// Adapted result.
fontinfo_id = GetFontinfoId(classes[class_id], int_result.Config);
fontinfo_id2 = GetFontinfoId(classes[class_id], int_result.Config2);
} else {
// Pre-trained result.
fontinfo_id = ClassAndConfigIDToFontOrShapeID(class_id, int_result.Config);
fontinfo_id2 = ClassAndConfigIDToFontOrShapeID(class_id,
int_result.Config2);
if (shape_table_ != NULL) {
// Actually fontinfo_id is an index into the shape_table_ and it
// contains a list of unchar_id/font_id pairs.
int shape_id = fontinfo_id;
const Shape& shape = shape_table_->GetShape(fontinfo_id);
double min_rating = 0.0;
for (int c = 0; c < shape.size(); ++c) {
int unichar_id = shape[c].unichar_id;
fontinfo_id = shape[c].font_ids[0];
if (shape[c].font_ids.size() > 1)
fontinfo_id2 = shape[c].font_ids[1];
else if (fontinfo_id2 != kBlankFontinfoId)
fontinfo_id2 = shape_table_->GetShape(fontinfo_id2)[0].font_ids[0];
double rating = ComputeCorrectedRating(debug, unichar_id, cp_rating,
int_result.Rating,
int_result.FeatureMisses,
bottom, top, blob_length,
matcher_multiplier, cn_factors);
if (c == 0 || rating < min_rating)
min_rating = rating;
if (unicharset.get_enabled(unichar_id)) {
AddNewResult(final_results, unichar_id, shape_id, rating,
classes != NULL, int_result.Config,
fontinfo_id, fontinfo_id2);
}
}
int_result.Rating = min_rating;
return;
}
}
double rating = ComputeCorrectedRating(debug, class_id, cp_rating,
int_result.Rating,
int_result.FeatureMisses,
bottom, top, blob_length,
matcher_multiplier, cn_factors);
if (unicharset.get_enabled(class_id)) {
AddNewResult(final_results, class_id, -1, rating,
classes != NULL, int_result.Config,
fontinfo_id, fontinfo_id2);
}
int_result.Rating = rating;
}
// Applies a set of corrections to the distance im_rating,
// including the cn_correction, miss penalty and additional penalty
// for non-alnums being vertical misfits. Returns the corrected distance.
double Classify::ComputeCorrectedRating(bool debug, int unichar_id,
double cp_rating, double im_rating,
int feature_misses,
int bottom, int top,
int blob_length, int matcher_multiplier,
const uinT8* cn_factors) {
// Compute class feature corrections.
double cn_corrected = im_.ApplyCNCorrection(im_rating, blob_length,
cn_factors[unichar_id],
matcher_multiplier);
double miss_penalty = tessedit_class_miss_scale * feature_misses;
double vertical_penalty = 0.0;
// Penalize non-alnums for being vertical misfits.
if (!unicharset.get_isalpha(unichar_id) &&
!unicharset.get_isdigit(unichar_id) &&
cn_factors[unichar_id] != 0 && classify_misfit_junk_penalty > 0.0) {
int min_bottom, max_bottom, min_top, max_top;
unicharset.get_top_bottom(unichar_id, &min_bottom, &max_bottom,
&min_top, &max_top);
if (debug) {
tprintf("top=%d, vs [%d, %d], bottom=%d, vs [%d, %d]\n",
top, min_top, max_top, bottom, min_bottom, max_bottom);
}
if (top < min_top || top > max_top ||
bottom < min_bottom || bottom > max_bottom) {
vertical_penalty = classify_misfit_junk_penalty;
}
}
double result =cn_corrected + miss_penalty + vertical_penalty;
if (result > WORST_POSSIBLE_RATING)
result = WORST_POSSIBLE_RATING;
if (debug) {
tprintf("%s: %2.1f(CP%2.1f, IM%2.1f + CN%.2f(%d) + MP%2.1f + VP%2.1f)\n",
unicharset.id_to_unichar(unichar_id),
result * 100.0,
cp_rating * 100.0,
im_rating * 100.0,
(cn_corrected - im_rating) * 100.0,
cn_factors[unichar_id],
miss_penalty * 100.0,
vertical_penalty * 100.0);
}
return result;
}
/*---------------------------------------------------------------------------*/
/**
* This routine extracts baseline normalized features
* from the unknown character and matches them against the
* specified set of templates. The classes which match
* are added to Results.
*
* Globals:
* - BaselineCutoffs expected num features for each class
*
* @param Blob blob to be classified
* @param Templates current set of adapted templates
* @param Results place to put match results
*
* @return Array of possible ambiguous chars that should be checked.
* @note Exceptions: none
* @note History: Tue Mar 12 19:38:03 1991, DSJ, Created.
*/
UNICHAR_ID *Classify::BaselineClassifier(
TBLOB *Blob, const GenericVector<INT_FEATURE_STRUCT>& int_features,
const INT_FX_RESULT_STRUCT& fx_info,
ADAPT_TEMPLATES Templates, ADAPT_RESULTS *Results) {
if (int_features.empty()) return NULL;
uinT8* CharNormArray = new uinT8[unicharset.size()];
ClearCharNormArray(CharNormArray);
Results->BlobLength = IntCastRounded(fx_info.Length / kStandardFeatureLength);
PruneClasses(Templates->Templates, int_features.size(), &int_features[0],
CharNormArray, BaselineCutoffs, &Results->CPResults);
if (matcher_debug_level >= 2 || classify_debug_level > 1)
cprintf ("BL Matches = ");
MasterMatcher(Templates->Templates, int_features.size(), &int_features[0],
CharNormArray,
Templates->Class, matcher_debug_flags, 0,
Blob->bounding_box(), Results->CPResults, Results);
delete [] CharNormArray;
CLASS_ID ClassId = Results->best_match.unichar_id;
if (ClassId == NO_CLASS)
return (NULL);
/* this is a bug - maybe should return "" */
return Templates->Class[ClassId]->
Config[Results->best_match.config].Perm->Ambigs;
} /* BaselineClassifier */
/*---------------------------------------------------------------------------*/
/**
* This routine extracts character normalized features
* from the unknown character and matches them against the
* specified set of templates. The classes which match
* are added to Results.
*
* @param Blob blob to be classified
* @param Templates templates to classify unknown against
* @param Results place to put match results
*
* Globals:
* - CharNormCutoffs expected num features for each class
* - AllProtosOn mask that enables all protos
* - AllConfigsOn mask that enables all configs
*
* @note Exceptions: none
* @note History: Tue Mar 12 16:02:52 1991, DSJ, Created.
*/
int Classify::CharNormClassifier(TBLOB *blob,
const TrainingSample& sample,
ADAPT_RESULTS *adapt_results) {
// This is the length that is used for scaling ratings vs certainty.
adapt_results->BlobLength =
IntCastRounded(sample.outline_length() / kStandardFeatureLength);
GenericVector<UnicharRating> unichar_results;
static_classifier_->UnicharClassifySample(sample, blob->denorm().pix(), 0,
-1, &unichar_results);
// Convert results to the format used internally by AdaptiveClassifier.
for (int r = 0; r < unichar_results.size(); ++r) {
int unichar_id = unichar_results[r].unichar_id;
// Fonts are listed in order of preference.
int font1 = unichar_results[r].fonts.size() >= 1
? unichar_results[r].fonts[0] : kBlankFontinfoId;
int font2 = unichar_results[r].fonts.size() >= 2
? unichar_results[r].fonts[1] : kBlankFontinfoId;
float rating = 1.0f - unichar_results[r].rating;
AddNewResult(adapt_results, unichar_id, -1, rating, false, 0, font1, font2);
}
return sample.num_features();
} /* CharNormClassifier */
// As CharNormClassifier, but operates on a TrainingSample and outputs to
// a GenericVector of ShapeRating without conversion to classes.
int Classify::CharNormTrainingSample(bool pruner_only,
int keep_this,
const TrainingSample& sample,
GenericVector<UnicharRating>* results) {
results->clear();
ADAPT_RESULTS* adapt_results = new ADAPT_RESULTS();
adapt_results->Initialize();
// Compute the bounding box of the features.
int num_features = sample.num_features();
// Only the top and bottom of the blob_box are used by MasterMatcher, so
// fabricate right and left using top and bottom.
TBOX blob_box(sample.geo_feature(GeoBottom), sample.geo_feature(GeoBottom),
sample.geo_feature(GeoTop), sample.geo_feature(GeoTop));
// Compute the char_norm_array from the saved cn_feature.
FEATURE norm_feature = sample.GetCNFeature();
uinT8* char_norm_array = new uinT8[unicharset.size()];
int num_pruner_classes = MAX(unicharset.size(),
PreTrainedTemplates->NumClasses);
uinT8* pruner_norm_array = new uinT8[num_pruner_classes];
adapt_results->BlobLength =
static_cast<int>(ActualOutlineLength(norm_feature) * 20 + 0.5);
ComputeCharNormArrays(norm_feature, PreTrainedTemplates, char_norm_array,
pruner_norm_array);
PruneClasses(PreTrainedTemplates, num_features, sample.features(),
pruner_norm_array,
shape_table_ != NULL ? &shapetable_cutoffs_[0] : CharNormCutoffs,
&adapt_results->CPResults);
delete [] pruner_norm_array;
if (keep_this >= 0) {
adapt_results->CPResults[0].Class = keep_this;
adapt_results->CPResults.truncate(1);
}
if (pruner_only) {
// Convert pruner results to output format.
for (int i = 0; i < adapt_results->CPResults.size(); ++i) {
int class_id = adapt_results->CPResults[i].Class;
results->push_back(
UnicharRating(class_id, 1.0f - adapt_results->CPResults[i].Rating));
}
} else {
MasterMatcher(PreTrainedTemplates, num_features, sample.features(),
char_norm_array,
NULL, matcher_debug_flags,
classify_integer_matcher_multiplier,
blob_box, adapt_results->CPResults, adapt_results);
// Convert master matcher results to output format.
for (int i = 0; i < adapt_results->match.size(); i++) {
ScoredClass next = adapt_results->match[i];
UnicharRating rating(next.unichar_id, 1.0f - next.rating);
if (next.fontinfo_id >= 0) {
rating.fonts.push_back(next.fontinfo_id);
if (next.fontinfo_id2 >= 0)
rating.fonts.push_back(next.fontinfo_id2);
}
results->push_back(rating);
}
results->sort(&UnicharRating::SortDescendingRating);
}
delete [] char_norm_array;
delete adapt_results;
return num_features;
} /* CharNormTrainingSample */
/*---------------------------------------------------------------------------*/
/**
* This routine computes a rating which reflects the
* likelihood that the blob being classified is a noise
* blob. NOTE: assumes that the blob length has already been
* computed and placed into Results.
*
* @param Results results to add noise classification to
*
* Globals:
* - matcher_avg_noise_size avg. length of a noise blob
*
* @note Exceptions: none
* @note History: Tue Mar 12 18:36:52 1991, DSJ, Created.
*/
void Classify::ClassifyAsNoise(ADAPT_RESULTS *Results) {
register FLOAT32 Rating;
Rating = Results->BlobLength / matcher_avg_noise_size;
Rating *= Rating;
Rating /= 1.0 + Rating;
AddNewResult(Results, NO_CLASS, -1, Rating, false, -1,
kBlankFontinfoId, kBlankFontinfoId);
} /* ClassifyAsNoise */
} // namespace tesseract
/*---------------------------------------------------------------------------*/
// Return a pointer to the scored unichar in results, or NULL if not present.
ScoredClass *FindScoredUnichar(ADAPT_RESULTS *results, UNICHAR_ID id) {
for (int i = 0; i < results->match.size(); i++) {
if (results->match[i].unichar_id == id)
return &results->match[i];
}
return NULL;
}
// Retrieve the current rating for a unichar id if we have rated it, defaulting
// to WORST_POSSIBLE_RATING.
ScoredClass ScoredUnichar(ADAPT_RESULTS *results, UNICHAR_ID id) {
ScoredClass poor_result =
{id, -1, WORST_POSSIBLE_RATING, false, -1,
kBlankFontinfoId, kBlankFontinfoId};
ScoredClass *entry = FindScoredUnichar(results, id);
return (entry == NULL) ? poor_result : *entry;
}
// Compare character classes by rating as for qsort(3).
// For repeatability, use character class id as a tie-breaker.
int CompareByRating(const void *arg1, // ScoredClass *class1
const void *arg2) { // ScoredClass *class2
const ScoredClass *class1 = (const ScoredClass *)arg1;
const ScoredClass *class2 = (const ScoredClass *)arg2;
if (class1->rating < class2->rating)
return -1;
else if (class1->rating > class2->rating)
return 1;
if (class1->unichar_id < class2->unichar_id)
return -1;
else if (class1->unichar_id > class2->unichar_id)
return 1;
return 0;
}
/*---------------------------------------------------------------------------*/
namespace tesseract {
/// The function converts the given match ratings to the list of blob
/// choices with ratings and certainties (used by the context checkers).
/// If character fragments are present in the results, this function also makes
/// sure that there is at least one non-fragmented classification included.
/// For each classification result check the unicharset for "definite"
/// ambiguities and modify the resulting Choices accordingly.
void Classify::ConvertMatchesToChoices(const DENORM& denorm, const TBOX& box,
ADAPT_RESULTS *Results,
BLOB_CHOICE_LIST *Choices) {
assert(Choices != NULL);
FLOAT32 Rating;
FLOAT32 Certainty;
BLOB_CHOICE_IT temp_it;
bool contains_nonfrag = false;
temp_it.set_to_list(Choices);
int choices_length = 0;
// With no shape_table_ maintain the previous MAX_MATCHES as the maximum
// number of returned results, but with a shape_table_ we want to have room
// for at least the biggest shape (which might contain hundreds of Indic
// grapheme fragments) and more, so use double the size of the biggest shape
// if that is more than the default.
int max_matches = MAX_MATCHES;
if (shape_table_ != NULL) {
max_matches = shape_table_->MaxNumUnichars() * 2;
if (max_matches < MAX_MATCHES)
max_matches = MAX_MATCHES;
}
float best_certainty = -MAX_FLOAT32;
for (int i = 0; i < Results->match.size(); i++) {
ScoredClass next = Results->match[i];
int fontinfo_id = next.fontinfo_id;
int fontinfo_id2 = next.fontinfo_id2;
bool adapted = next.adapted;
bool current_is_frag = (unicharset.get_fragment(next.unichar_id) != NULL);
if (temp_it.length()+1 == max_matches &&
!contains_nonfrag && current_is_frag) {
continue; // look for a non-fragmented character to fill the
// last spot in Choices if only fragments are present
}
// BlobLength can never be legally 0, this means recognition failed.
// But we must return a classification result because some invoking
// functions (chopper/permuter) do not anticipate a null blob choice.
// So we need to assign a poor, but not infinitely bad score.
if (Results->BlobLength == 0) {
Certainty = -20;
Rating = 100; // should be -certainty * real_blob_length
} else {
Rating = Certainty = next.rating;
Rating *= rating_scale * Results->BlobLength;
Certainty *= -(getDict().certainty_scale);
}
// Adapted results, by their very nature, should have good certainty.
// Those that don't are at best misleading, and often lead to errors,
// so don't accept adapted results that are too far behind the best result,
// whether adapted or static.
// TODO(rays) find some way of automatically tuning these constants.
if (Certainty > best_certainty) {
best_certainty = MIN(Certainty, classify_adapted_pruning_threshold);
} else if (adapted &&
Certainty / classify_adapted_pruning_factor < best_certainty) {
continue; // Don't accept bad adapted results.
}
float min_xheight, max_xheight, yshift;
denorm.XHeightRange(next.unichar_id, unicharset, box,
&min_xheight, &max_xheight, &yshift);
temp_it.add_to_end(new BLOB_CHOICE(next.unichar_id, Rating, Certainty,
fontinfo_id, fontinfo_id2,
unicharset.get_script(next.unichar_id),
min_xheight, max_xheight, yshift,
adapted ? BCC_ADAPTED_CLASSIFIER
: BCC_STATIC_CLASSIFIER));
contains_nonfrag |= !current_is_frag; // update contains_nonfrag
choices_length++;
if (choices_length >= max_matches) break;
}
Results->match.truncate(choices_length);
} // ConvertMatchesToChoices
/*---------------------------------------------------------------------------*/
#ifndef GRAPHICS_DISABLED
/**
*
* @param Blob blob whose classification is being debugged
* @param Results results of match being debugged
*
* Globals: none
*
* @note Exceptions: none
* @note History: Wed Mar 13 16:44:41 1991, DSJ, Created.
*/
void Classify::DebugAdaptiveClassifier(TBLOB *blob,
ADAPT_RESULTS *Results) {
if (static_classifier_ == NULL) return;
for (int i = 0; i < Results->match.size(); i++) {
if (i == 0 || Results->match[i].rating < Results->best_match.rating)
Results->best_match = Results->match[i];
}
INT_FX_RESULT_STRUCT fx_info;
GenericVector<INT_FEATURE_STRUCT> bl_features;
TrainingSample* sample =
BlobToTrainingSample(*blob, false, &fx_info, &bl_features);
if (sample == NULL) return;
static_classifier_->DebugDisplay(*sample, blob->denorm().pix(),
Results->best_match.unichar_id);
} /* DebugAdaptiveClassifier */
#endif
/*---------------------------------------------------------------------------*/
/**
* This routine performs an adaptive classification.
* If we have not yet adapted to enough classes, a simple
* classification to the pre-trained templates is performed.
* Otherwise, we match the blob against the adapted templates.
* If the adapted templates do not match well, we try a
* match against the pre-trained templates. If an adapted
* template match is found, we do a match to any pre-trained
* templates which could be ambiguous. The results from all
* of these classifications are merged together into Results.
*
* @param Blob blob to be classified
* @param Results place to put match results
*
* Globals:
* - PreTrainedTemplates built-in training templates
* - AdaptedTemplates templates adapted for this page
* - matcher_great_threshold rating limit for a great match
*
* @note Exceptions: none
* @note History: Tue Mar 12 08:50:11 1991, DSJ, Created.
*/
void Classify::DoAdaptiveMatch(TBLOB *Blob, ADAPT_RESULTS *Results) {
UNICHAR_ID *Ambiguities;
INT_FX_RESULT_STRUCT fx_info;
GenericVector<INT_FEATURE_STRUCT> bl_features;
TrainingSample* sample =
BlobToTrainingSample(*Blob, classify_nonlinear_norm, &fx_info,
&bl_features);
if (sample == NULL) return;
if (AdaptedTemplates->NumPermClasses < matcher_permanent_classes_min ||
tess_cn_matching) {
CharNormClassifier(Blob, *sample, Results);
} else {
Ambiguities = BaselineClassifier(Blob, bl_features, fx_info,
AdaptedTemplates, Results);
if ((!Results->match.empty() && MarginalMatch(Results->best_match.rating) &&
!tess_bn_matching) ||
Results->match.empty()) {
CharNormClassifier(Blob, *sample, Results);
} else if (Ambiguities && *Ambiguities >= 0 && !tess_bn_matching) {
AmbigClassifier(bl_features, fx_info, Blob,
PreTrainedTemplates,
AdaptedTemplates->Class,
Ambiguities,
Results);
}
}
// Force the blob to be classified as noise
// if the results contain only fragments.
// TODO(daria): verify that this is better than
// just adding a NULL classification.
if (!Results->HasNonfragment || Results->match.empty())
ClassifyAsNoise(Results);
delete sample;
} /* DoAdaptiveMatch */
/*---------------------------------------------------------------------------*/
/**
* This routine matches blob to the built-in templates
* to find out if there are any classes other than the correct
* class which are potential ambiguities.
*
* @param Blob blob to get classification ambiguities for
* @param CorrectClass correct class for Blob
*
* Globals:
* - CurrentRatings used by qsort compare routine
* - PreTrainedTemplates built-in templates
*
* @return String containing all possible ambiguous classes.
* @note Exceptions: none
* @note History: Fri Mar 15 08:08:22 1991, DSJ, Created.
*/
UNICHAR_ID *Classify::GetAmbiguities(TBLOB *Blob,
CLASS_ID CorrectClass) {
ADAPT_RESULTS *Results = new ADAPT_RESULTS();
UNICHAR_ID *Ambiguities;
int i;
Results->Initialize();
INT_FX_RESULT_STRUCT fx_info;
GenericVector<INT_FEATURE_STRUCT> bl_features;
TrainingSample* sample =
BlobToTrainingSample(*Blob, classify_nonlinear_norm, &fx_info,
&bl_features);
if (sample == NULL) {
delete Results;
return NULL;
}
CharNormClassifier(Blob, *sample, Results);
delete sample;
RemoveBadMatches(Results);
Results->match.sort(CompareByRating);
/* copy the class id's into an string of ambiguities - don't copy if
the correct class is the only class id matched */
Ambiguities = new UNICHAR_ID[Results->match.size() + 1];
if (Results->match.size() > 1 ||
(Results->match.size() == 1 &&
Results->match[0].unichar_id != CorrectClass)) {
for (i = 0; i < Results->match.size(); i++)
Ambiguities[i] = Results->match[i].unichar_id;
Ambiguities[i] = -1;
} else {
Ambiguities[0] = -1;
}
delete Results;
return Ambiguities;
} /* GetAmbiguities */
// Returns true if the given blob looks too dissimilar to any character
// present in the classifier templates.
bool Classify::LooksLikeGarbage(TBLOB *blob) {
BLOB_CHOICE_LIST *ratings = new BLOB_CHOICE_LIST();
AdaptiveClassifier(blob, ratings);
BLOB_CHOICE_IT ratings_it(ratings);
const UNICHARSET &unicharset = getDict().getUnicharset();
if (classify_debug_character_fragments) {
print_ratings_list("======================\nLooksLikeGarbage() got ",
ratings, unicharset);
}
for (ratings_it.mark_cycle_pt(); !ratings_it.cycled_list();
ratings_it.forward()) {
if (unicharset.get_fragment(ratings_it.data()->unichar_id()) != NULL) {
continue;
}
float certainty = ratings_it.data()->certainty();
delete ratings;
return certainty <
classify_character_fragments_garbage_certainty_threshold;
}
delete ratings;
return true; // no whole characters in ratings
}
/*---------------------------------------------------------------------------*/
/**
* This routine calls the integer (Hardware) feature
* extractor if it has not been called before for this blob.
*
* The results from the feature extractor are placed into
* globals so that they can be used in other routines without
* re-extracting the features.
*
* It then copies the char norm features into the IntFeatures
* array provided by the caller.
*
* @param Blob blob to extract features from
* @param Templates used to compute char norm adjustments
* @param IntFeatures array to fill with integer features
* @param PrunerNormArray Array of factors from blob normalization
* process
* @param CharNormArray array to fill with dummy char norm adjustments
* @param BlobLength length of blob in baseline-normalized units
*
* Globals:
*
* @return Number of features extracted or 0 if an error occured.
* @note Exceptions: none
* @note History: Tue May 28 10:40:52 1991, DSJ, Created.
*/
int Classify::GetCharNormFeature(const INT_FX_RESULT_STRUCT& fx_info,
INT_TEMPLATES templates,
uinT8* pruner_norm_array,
uinT8* char_norm_array) {
FEATURE norm_feature = NewFeature(&CharNormDesc);
float baseline = kBlnBaselineOffset;
float scale = MF_SCALE_FACTOR;
norm_feature->Params[CharNormY] = (fx_info.Ymean - baseline) * scale;
norm_feature->Params[CharNormLength] =
fx_info.Length * scale / LENGTH_COMPRESSION;
norm_feature->Params[CharNormRx] = fx_info.Rx * scale;
norm_feature->Params[CharNormRy] = fx_info.Ry * scale;
// Deletes norm_feature.
ComputeCharNormArrays(norm_feature, templates, char_norm_array,
pruner_norm_array);
return IntCastRounded(fx_info.Length / kStandardFeatureLength);
} /* GetCharNormFeature */
// Computes the char_norm_array for the unicharset and, if not NULL, the
// pruner_array as appropriate according to the existence of the shape_table.
void Classify::ComputeCharNormArrays(FEATURE_STRUCT* norm_feature,
INT_TEMPLATES_STRUCT* templates,
uinT8* char_norm_array,
uinT8* pruner_array) {
ComputeIntCharNormArray(*norm_feature, char_norm_array);
if (pruner_array != NULL) {
if (shape_table_ == NULL) {
ComputeIntCharNormArray(*norm_feature, pruner_array);
} else {
memset(pruner_array, MAX_UINT8,
templates->NumClasses * sizeof(pruner_array[0]));
// Each entry in the pruner norm array is the MIN of all the entries of
// the corresponding unichars in the CharNormArray.
for (int id = 0; id < templates->NumClasses; ++id) {
int font_set_id = templates->Class[id]->font_set_id;
const FontSet &fs = fontset_table_.get(font_set_id);
for (int config = 0; config < fs.size; ++config) {
const Shape& shape = shape_table_->GetShape(fs.configs[config]);
for (int c = 0; c < shape.size(); ++c) {
if (char_norm_array[shape[c].unichar_id] < pruner_array[id])
pruner_array[id] = char_norm_array[shape[c].unichar_id];
}
}
}
}
}
FreeFeature(norm_feature);
}
/*---------------------------------------------------------------------------*/
/**
*
* @param Templates adapted templates to add new config to
* @param ClassId class id to associate with new config
* @param FontinfoId font information inferred from pre-trained templates
* @param NumFeatures number of features in IntFeatures
* @param Features features describing model for new config
* @param FloatFeatures floating-pt representation of features
*
* @return The id of the new config created, a negative integer in
* case of error.
* @note Exceptions: none
* @note History: Fri Mar 15 08:49:46 1991, DSJ, Created.
*/
int Classify::MakeNewTemporaryConfig(ADAPT_TEMPLATES Templates,
CLASS_ID ClassId,
int FontinfoId,
int NumFeatures,
INT_FEATURE_ARRAY Features,
FEATURE_SET FloatFeatures) {
INT_CLASS IClass;
ADAPT_CLASS Class;
PROTO_ID OldProtos[MAX_NUM_PROTOS];
FEATURE_ID BadFeatures[MAX_NUM_INT_FEATURES];
int NumOldProtos;
int NumBadFeatures;
int MaxProtoId, OldMaxProtoId;
int BlobLength = 0;
int MaskSize;
int ConfigId;
TEMP_CONFIG Config;
int i;
int debug_level = NO_DEBUG;
if (classify_learning_debug_level >= 3)
debug_level =
PRINT_MATCH_SUMMARY | PRINT_FEATURE_MATCHES | PRINT_PROTO_MATCHES;
IClass = ClassForClassId(Templates->Templates, ClassId);
Class = Templates->Class[ClassId];
if (IClass->NumConfigs >= MAX_NUM_CONFIGS) {
++NumAdaptationsFailed;
if (classify_learning_debug_level >= 1)
cprintf("Cannot make new temporary config: maximum number exceeded.\n");
return -1;
}
OldMaxProtoId = IClass->NumProtos - 1;
NumOldProtos = im_.FindGoodProtos(IClass, AllProtosOn, AllConfigsOff,
BlobLength, NumFeatures, Features,
OldProtos, classify_adapt_proto_threshold,
debug_level);
MaskSize = WordsInVectorOfSize(MAX_NUM_PROTOS);
zero_all_bits(TempProtoMask, MaskSize);
for (i = 0; i < NumOldProtos; i++)
SET_BIT(TempProtoMask, OldProtos[i]);
NumBadFeatures = im_.FindBadFeatures(IClass, TempProtoMask, AllConfigsOn,
BlobLength, NumFeatures, Features,
BadFeatures,
classify_adapt_feature_threshold,
debug_level);
MaxProtoId = MakeNewTempProtos(FloatFeatures, NumBadFeatures, BadFeatures,
IClass, Class, TempProtoMask);
if (MaxProtoId == NO_PROTO) {
++NumAdaptationsFailed;
if (classify_learning_debug_level >= 1)
cprintf("Cannot make new temp protos: maximum number exceeded.\n");
return -1;
}
ConfigId = AddIntConfig(IClass);
ConvertConfig(TempProtoMask, ConfigId, IClass);
Config = NewTempConfig(MaxProtoId, FontinfoId);
TempConfigFor(Class, ConfigId) = Config;
copy_all_bits(TempProtoMask, Config->Protos, Config->ProtoVectorSize);
if (classify_learning_debug_level >= 1)
cprintf("Making new temp config %d fontinfo id %d"
" using %d old and %d new protos.\n",
ConfigId, Config->FontinfoId,
NumOldProtos, MaxProtoId - OldMaxProtoId);
return ConfigId;
} /* MakeNewTemporaryConfig */
/*---------------------------------------------------------------------------*/
/**
* This routine finds sets of sequential bad features
* that all have the same angle and converts each set into
* a new temporary proto. The temp proto is added to the
* proto pruner for IClass, pushed onto the list of temp
* protos in Class, and added to TempProtoMask.
*
* @param Features floating-pt features describing new character
* @param NumBadFeat number of bad features to turn into protos
* @param BadFeat feature id's of bad features
* @param IClass integer class templates to add new protos to
* @param Class adapted class templates to add new protos to
* @param TempProtoMask proto mask to add new protos to
*
* Globals: none
*
* @return Max proto id in class after all protos have been added.
* Exceptions: none
* History: Fri Mar 15 11:39:38 1991, DSJ, Created.
*/
PROTO_ID Classify::MakeNewTempProtos(FEATURE_SET Features,
int NumBadFeat,
FEATURE_ID BadFeat[],
INT_CLASS IClass,
ADAPT_CLASS Class,
BIT_VECTOR TempProtoMask) {
FEATURE_ID *ProtoStart;
FEATURE_ID *ProtoEnd;
FEATURE_ID *LastBad;
TEMP_PROTO TempProto;
PROTO Proto;
FEATURE F1, F2;
FLOAT32 X1, X2, Y1, Y2;
FLOAT32 A1, A2, AngleDelta;
FLOAT32 SegmentLength;
PROTO_ID Pid;
for (ProtoStart = BadFeat, LastBad = ProtoStart + NumBadFeat;
ProtoStart < LastBad; ProtoStart = ProtoEnd) {
F1 = Features->Features[*ProtoStart];
X1 = F1->Params[PicoFeatX];
Y1 = F1->Params[PicoFeatY];
A1 = F1->Params[PicoFeatDir];
for (ProtoEnd = ProtoStart + 1,
SegmentLength = GetPicoFeatureLength();
ProtoEnd < LastBad;
ProtoEnd++, SegmentLength += GetPicoFeatureLength()) {
F2 = Features->Features[*ProtoEnd];
X2 = F2->Params[PicoFeatX];
Y2 = F2->Params[PicoFeatY];
A2 = F2->Params[PicoFeatDir];
AngleDelta = fabs(A1 - A2);
if (AngleDelta > 0.5)
AngleDelta = 1.0 - AngleDelta;
if (AngleDelta > matcher_clustering_max_angle_delta ||
fabs(X1 - X2) > SegmentLength ||
fabs(Y1 - Y2) > SegmentLength)
break;
}
F2 = Features->Features[*(ProtoEnd - 1)];
X2 = F2->Params[PicoFeatX];
Y2 = F2->Params[PicoFeatY];
A2 = F2->Params[PicoFeatDir];
Pid = AddIntProto(IClass);
if (Pid == NO_PROTO)
return (NO_PROTO);
TempProto = NewTempProto();
Proto = &(TempProto->Proto);
/* compute proto params - NOTE that Y_DIM_OFFSET must be used because
ConvertProto assumes that the Y dimension varies from -0.5 to 0.5
instead of the -0.25 to 0.75 used in baseline normalization */
Proto->Length = SegmentLength;
Proto->Angle = A1;
Proto->X = (X1 + X2) / 2.0;
Proto->Y = (Y1 + Y2) / 2.0 - Y_DIM_OFFSET;
FillABC(Proto);
TempProto->ProtoId = Pid;
SET_BIT(TempProtoMask, Pid);
ConvertProto(Proto, Pid, IClass);
AddProtoToProtoPruner(Proto, Pid, IClass,
classify_learning_debug_level >= 2);
Class->TempProtos = push(Class->TempProtos, TempProto);
}
return IClass->NumProtos - 1;
} /* MakeNewTempProtos */
/*---------------------------------------------------------------------------*/
/**
*
* @param Templates current set of adaptive templates
* @param ClassId class containing config to be made permanent
* @param ConfigId config to be made permanent
* @param Blob current blob being adapted to
*
* Globals: none
*
* @note Exceptions: none
* @note History: Thu Mar 14 15:54:08 1991, DSJ, Created.
*/
void Classify::MakePermanent(ADAPT_TEMPLATES Templates,
CLASS_ID ClassId,
int ConfigId,
TBLOB *Blob) {
UNICHAR_ID *Ambigs;
TEMP_CONFIG Config;
ADAPT_CLASS Class;
PROTO_KEY ProtoKey;
Class = Templates->Class[ClassId];
Config = TempConfigFor(Class, ConfigId);
MakeConfigPermanent(Class, ConfigId);
if (Class->NumPermConfigs == 0)
Templates->NumPermClasses++;
Class->NumPermConfigs++;
// Initialize permanent config.
Ambigs = GetAmbiguities(Blob, ClassId);
PERM_CONFIG Perm = (PERM_CONFIG) alloc_struct(sizeof(PERM_CONFIG_STRUCT),
"PERM_CONFIG_STRUCT");
Perm->Ambigs = Ambigs;
Perm->FontinfoId = Config->FontinfoId;
// Free memory associated with temporary config (since ADAPTED_CONFIG
// is a union we need to clean up before we record permanent config).
ProtoKey.Templates = Templates;
ProtoKey.ClassId = ClassId;
ProtoKey.ConfigId = ConfigId;
Class->TempProtos = delete_d(Class->TempProtos, &ProtoKey, MakeTempProtoPerm);
FreeTempConfig(Config);
// Record permanent config.
PermConfigFor(Class, ConfigId) = Perm;
if (classify_learning_debug_level >= 1) {
tprintf("Making config %d for %s (ClassId %d) permanent:"
" fontinfo id %d, ambiguities '",
ConfigId, getDict().getUnicharset().debug_str(ClassId).string(),
ClassId, PermConfigFor(Class, ConfigId)->FontinfoId);
for (UNICHAR_ID *AmbigsPointer = Ambigs;
*AmbigsPointer >= 0; ++AmbigsPointer)
tprintf("%s", unicharset.id_to_unichar(*AmbigsPointer));
tprintf("'.\n");
}
} /* MakePermanent */
} // namespace tesseract
/*---------------------------------------------------------------------------*/
/**
* This routine converts TempProto to be permanent if
* its proto id is used by the configuration specified in
* ProtoKey.
*
* @param item1 (TEMP_PROTO) temporary proto to compare to key
* @param item2 (PROTO_KEY) defines which protos to make permanent
*
* Globals: none
*
* @return TRUE if TempProto is converted, FALSE otherwise
* @note Exceptions: none
* @note History: Thu Mar 14 18:49:54 1991, DSJ, Created.
*/
int MakeTempProtoPerm(void *item1, void *item2) {
ADAPT_CLASS Class;
TEMP_CONFIG Config;
TEMP_PROTO TempProto;
PROTO_KEY *ProtoKey;
TempProto = (TEMP_PROTO) item1;
ProtoKey = (PROTO_KEY *) item2;
Class = ProtoKey->Templates->Class[ProtoKey->ClassId];
Config = TempConfigFor(Class, ProtoKey->ConfigId);
if (TempProto->ProtoId > Config->MaxProtoId ||
!test_bit (Config->Protos, TempProto->ProtoId))
return FALSE;
MakeProtoPermanent(Class, TempProto->ProtoId);
AddProtoToClassPruner(&(TempProto->Proto), ProtoKey->ClassId,
ProtoKey->Templates->Templates);
FreeTempProto(TempProto);
return TRUE;
} /* MakeTempProtoPerm */
/*---------------------------------------------------------------------------*/
namespace tesseract {
/**
* This routine writes the matches in Results to File.
*
* @param File open text file to write Results to
* @param Results match results to write to File
*
* Globals: none
*
* @note Exceptions: none
* @note History: Mon Mar 18 09:24:53 1991, DSJ, Created.
*/
void Classify::PrintAdaptiveMatchResults(FILE *File, ADAPT_RESULTS *Results) {
for (int i = 0; i < Results->match.size(); ++i) {
tprintf("%s(%d), shape %d, %.2f ",
unicharset.debug_str(Results->match[i].unichar_id).string(),
Results->match[i].unichar_id, Results->match[i].shape_id,
Results->match[i].rating * 100.0);
}
tprintf("\n");
} /* PrintAdaptiveMatchResults */
/*---------------------------------------------------------------------------*/
/**
* This routine steps thru each matching class in Results
* and removes it from the match list if its rating
* is worse than the BestRating plus a pad. In other words,
* all good matches get moved to the front of the classes
* array.
*
* @param Results contains matches to be filtered
*
* Globals:
* - matcher_bad_match_pad defines a "bad match"
*
* @note Exceptions: none
* @note History: Tue Mar 12 13:51:03 1991, DSJ, Created.
*/
void Classify::RemoveBadMatches(ADAPT_RESULTS *Results) {
int Next, NextGood;
FLOAT32 BadMatchThreshold;
static const char* romans = "i v x I V X";
BadMatchThreshold = Results->best_match.rating + matcher_bad_match_pad;
if (classify_bln_numeric_mode) {
UNICHAR_ID unichar_id_one = unicharset.contains_unichar("1") ?
unicharset.unichar_to_id("1") : -1;
UNICHAR_ID unichar_id_zero = unicharset.contains_unichar("0") ?
unicharset.unichar_to_id("0") : -1;
ScoredClass scored_one = ScoredUnichar(Results, unichar_id_one);
ScoredClass scored_zero = ScoredUnichar(Results, unichar_id_zero);
for (Next = NextGood = 0; Next < Results->match.size(); Next++) {
if (Results->match[Next].rating <= BadMatchThreshold) {
ScoredClass match = Results->match[Next];
if (!unicharset.get_isalpha(match.unichar_id) ||
strstr(romans,
unicharset.id_to_unichar(match.unichar_id)) != NULL) {
Results->match[NextGood++] = Results->match[Next];
} else if (unicharset.eq(match.unichar_id, "l") &&
scored_one.rating >= BadMatchThreshold) {
Results->match[NextGood] = scored_one;
Results->match[NextGood].rating = match.rating;
NextGood++;
} else if (unicharset.eq(match.unichar_id, "O") &&
scored_zero.rating >= BadMatchThreshold) {
Results->match[NextGood] = scored_zero;
Results->match[NextGood].rating = match.rating;
NextGood++;
}
}
}
} else {
for (Next = NextGood = 0; Next < Results->match.size(); Next++) {
if (Results->match[Next].rating <= BadMatchThreshold)
Results->match[NextGood++] = Results->match[Next];
}
}
Results->match.truncate(NextGood);
} /* RemoveBadMatches */
/*----------------------------------------------------------------------------*/
/**
* This routine discards extra digits or punctuation from the results.
* We keep only the top 2 punctuation answers and the top 1 digit answer if
* present.
*
* @param Results contains matches to be filtered
*
* @note History: Tue Mar 12 13:51:03 1991, DSJ, Created.
*/
void Classify::RemoveExtraPuncs(ADAPT_RESULTS *Results) {
int Next, NextGood;
int punc_count; /*no of garbage characters */
int digit_count;
/*garbage characters */
static char punc_chars[] = ". , ; : / ` ~ ' - = \\ | \" ! _ ^";
static char digit_chars[] = "0 1 2 3 4 5 6 7 8 9";
punc_count = 0;
digit_count = 0;
for (Next = NextGood = 0; Next < Results->match.size(); Next++) {
ScoredClass match = Results->match[Next];
if (strstr(punc_chars,
unicharset.id_to_unichar(match.unichar_id)) != NULL) {
if (punc_count < 2)
Results->match[NextGood++] = match;
punc_count++;
} else {
if (strstr(digit_chars,
unicharset.id_to_unichar(match.unichar_id)) != NULL) {
if (digit_count < 1)
Results->match[NextGood++] = match;
digit_count++;
} else {
Results->match[NextGood++] = match;
}
}
}
Results->match.truncate(NextGood);
} /* RemoveExtraPuncs */
/*---------------------------------------------------------------------------*/
/**
* This routine resets the internal thresholds inside
* the integer matcher to correspond to the specified
* threshold.
*
* @param Threshold threshold for creating new templates
*
* Globals:
* - matcher_good_threshold default good match rating
*
* @note Exceptions: none
* @note History: Tue Apr 9 08:33:13 1991, DSJ, Created.
*/
void Classify::SetAdaptiveThreshold(FLOAT32 Threshold) {
Threshold = (Threshold == matcher_good_threshold) ? 0.9: (1.0 - Threshold);
classify_adapt_proto_threshold.set_value(
ClipToRange<int>(255 * Threshold, 0, 255));
classify_adapt_feature_threshold.set_value(
ClipToRange<int>(255 * Threshold, 0, 255));
} /* SetAdaptiveThreshold */
/*---------------------------------------------------------------------------*/
/**
* This routine displays debug information for the best config
* of the given shape_id for the given set of features.
*
* @param shape_id classifier id to work with
* @param features features of the unknown character
* @param num_features Number of features in the features array.
*
* @note Exceptions: none
* @note History: Fri Mar 22 08:43:52 1991, DSJ, Created.
*/
void Classify::ShowBestMatchFor(int shape_id,
const INT_FEATURE_STRUCT* features,
int num_features) {
#ifndef GRAPHICS_DISABLED
uinT32 config_mask;
if (UnusedClassIdIn(PreTrainedTemplates, shape_id)) {
tprintf("No built-in templates for class/shape %d\n", shape_id);
return;
}
if (num_features <= 0) {
tprintf("Illegal blob (char norm features)!\n");
return;
}
INT_RESULT_STRUCT cn_result;
classify_norm_method.set_value(character);
im_.Match(ClassForClassId(PreTrainedTemplates, shape_id),
AllProtosOn, AllConfigsOn,
num_features, features, &cn_result,
classify_adapt_feature_threshold, NO_DEBUG,
matcher_debug_separate_windows);
tprintf("\n");
config_mask = 1 << cn_result.Config;
tprintf("Static Shape ID: %d\n", shape_id);
ShowMatchDisplay();
im_.Match(ClassForClassId(PreTrainedTemplates, shape_id),
AllProtosOn, reinterpret_cast<BIT_VECTOR>(&config_mask),
num_features, features, &cn_result,
classify_adapt_feature_threshold,
matcher_debug_flags,
matcher_debug_separate_windows);
UpdateMatchDisplay();
#endif // GRAPHICS_DISABLED
} /* ShowBestMatchFor */
// Returns a string for the classifier class_id: either the corresponding
// unicharset debug_str or the shape_table_ debug str.
STRING Classify::ClassIDToDebugStr(const INT_TEMPLATES_STRUCT* templates,
int class_id, int config_id) const {
STRING class_string;
if (templates == PreTrainedTemplates && shape_table_ != NULL) {
int shape_id = ClassAndConfigIDToFontOrShapeID(class_id, config_id);
class_string = shape_table_->DebugStr(shape_id);
} else {
class_string = unicharset.debug_str(class_id);
}
return class_string;
}
// Converts a classifier class_id index to a shape_table_ index
int Classify::ClassAndConfigIDToFontOrShapeID(int class_id,
int int_result_config) const {
int font_set_id = PreTrainedTemplates->Class[class_id]->font_set_id;
// Older inttemps have no font_ids.
if (font_set_id < 0)
return kBlankFontinfoId;
const FontSet &fs = fontset_table_.get(font_set_id);
ASSERT_HOST(int_result_config >= 0 && int_result_config < fs.size);
return fs.configs[int_result_config];
}
// Converts a shape_table_ index to a classifier class_id index (not a
// unichar-id!). Uses a search, so not fast.
int Classify::ShapeIDToClassID(int shape_id) const {
for (int id = 0; id < PreTrainedTemplates->NumClasses; ++id) {
int font_set_id = PreTrainedTemplates->Class[id]->font_set_id;
ASSERT_HOST(font_set_id >= 0);
const FontSet &fs = fontset_table_.get(font_set_id);
for (int config = 0; config < fs.size; ++config) {
if (fs.configs[config] == shape_id)
return id;
}
}
tprintf("Shape %d not found\n", shape_id);
return -1;
}
// Returns true if the given TEMP_CONFIG is good enough to make it
// a permanent config.
bool Classify::TempConfigReliable(CLASS_ID class_id,
const TEMP_CONFIG &config) {
if (classify_learning_debug_level >= 1) {
tprintf("NumTimesSeen for config of %s is %d\n",
getDict().getUnicharset().debug_str(class_id).string(),
config->NumTimesSeen);
}
if (config->NumTimesSeen >= matcher_sufficient_examples_for_prototyping) {
return true;
} else if (config->NumTimesSeen < matcher_min_examples_for_prototyping) {
return false;
} else if (use_ambigs_for_adaption) {
// Go through the ambigs vector and see whether we have already seen
// enough times all the characters represented by the ambigs vector.
const UnicharIdVector *ambigs =
getDict().getUnicharAmbigs().AmbigsForAdaption(class_id);
int ambigs_size = (ambigs == NULL) ? 0 : ambigs->size();
for (int ambig = 0; ambig < ambigs_size; ++ambig) {
ADAPT_CLASS ambig_class = AdaptedTemplates->Class[(*ambigs)[ambig]];
assert(ambig_class != NULL);
if (ambig_class->NumPermConfigs == 0 &&
ambig_class->MaxNumTimesSeen <
matcher_min_examples_for_prototyping) {
if (classify_learning_debug_level >= 1) {
tprintf("Ambig %s has not been seen enough times,"
" not making config for %s permanent\n",
getDict().getUnicharset().debug_str(
(*ambigs)[ambig]).string(),
getDict().getUnicharset().debug_str(class_id).string());
}
return false;
}
}
}
return true;
}
void Classify::UpdateAmbigsGroup(CLASS_ID class_id, TBLOB *Blob) {
const UnicharIdVector *ambigs =
getDict().getUnicharAmbigs().ReverseAmbigsForAdaption(class_id);
int ambigs_size = (ambigs == NULL) ? 0 : ambigs->size();
if (classify_learning_debug_level >= 1) {
tprintf("Running UpdateAmbigsGroup for %s class_id=%d\n",
getDict().getUnicharset().debug_str(class_id).string(), class_id);
}
for (int ambig = 0; ambig < ambigs_size; ++ambig) {
CLASS_ID ambig_class_id = (*ambigs)[ambig];
const ADAPT_CLASS ambigs_class = AdaptedTemplates->Class[ambig_class_id];
for (int cfg = 0; cfg < MAX_NUM_CONFIGS; ++cfg) {
if (ConfigIsPermanent(ambigs_class, cfg)) continue;
const TEMP_CONFIG config =
TempConfigFor(AdaptedTemplates->Class[ambig_class_id], cfg);
if (config != NULL && TempConfigReliable(ambig_class_id, config)) {
if (classify_learning_debug_level >= 1) {
tprintf("Making config %d of %s permanent\n", cfg,
getDict().getUnicharset().debug_str(
ambig_class_id).string());
}
MakePermanent(AdaptedTemplates, ambig_class_id, cfg, Blob);
}
}
}
}
} // namespace tesseract
Reference in New Issue View Git Blame Copy Permalink