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tesseract/classify/adaptmatch.cpp
Jim O'Regan 524a61452d Doxygen 
Squashed commit from https://github.com/tesseract-ocr/tesseract/tree/more-doxygen
closes #14

Commits:
6317305  doxygen
9f42f69  doxygen
0fc4d52  doxygen
37b4b55  fix typo
bded8f1  some more doxy
020eb00  slight tweak
524666d  doxygenify
2a36a3e  doxygenify
229d218  doxygenify
7fd28ae  doxygenify
a8c64bc  doxygenify
f5d21b6  fix
5d8ede8  doxygenify
a58a4e0  language_model.cpp
fa85709  lm_pain_points.cpp lm_state.cpp
6418da3  merge
06190ba  Merge branch 'old_doxygen_merge' into more-doxygen
84acf08  Merge branch 'master' into more-doxygen
50fe1ff  pagewalk.cpp cube_reco_context.cpp
2982583  change to relative
192a24a  applybox.cpp, take one
8eeb053  delete docs for obsolete params
52e4c77  modernise classify/ocrfeatures.cpp
2a1cba6  modernise cutil/emalloc.cpp
773e006  silence doxygen warning
aeb1731  silence doxygen warning
f18387f  silence doxygen; new params are unused?
15ad6bd  doxygenify cutil/efio.cpp
c8b5dad  doxygenify cutil/danerror.cpp
784450f  the globals and exceptions parts are obsolete; remove
8bca324  doxygen classify/normfeat.cpp
9bcbe16  doxygen classify/normmatch.cpp
aa9a971  doxygen ccmain/cube_control.cpp
c083ff2  doxygen ccmain/cube_reco_context.cpp
f842850  params changed
5c94f12  doxygen ccmain/cubeclassifier.cpp
15ba750  case sensitive
f5c71d4  case sensitive
f85655b  doxygen classify/intproto.cpp
4bbc7aa  partial doxygen classify/mfx.cpp
dbb6041  partial doxygen classify/intproto.cpp
2aa72db  finish doxygen classify/intproto.cpp
0b8de99  doxygen training/mftraining.cpp
0b5b35c  partial doxygen ccstruct/coutln.cpp
b81c766  partial doxygen ccstruct/coutln.cpp
40fc415  finished? doxygen ccstruct/coutln.cpp
6e4165c  doxygen classify/clusttool.cpp
0267dec  doxygen classify/cutoffs.cpp
7f0c70c  doxygen classify/fpoint.cpp
512f3bd  ignore ~ files
5668a52  doxygen classify/intmatcher.cpp
84788d4  doxygen classify/kdtree.cpp
29f36ca  doxygen classify/mfoutline.cpp
40b94b1  silence doxygen warnings
6c511b9  doxygen classify/mfx.cpp
f9b4080  doxygen classify/outfeat.cpp
aa1df05  doxygen classify/picofeat.cpp
cc5f466  doxygen training/cntraining.cpp
cce044f  doxygen training/commontraining.cpp
167e216  missing param
9498383  renamed params
37eeac2  renamed param
d87b5dd  case
c8ee174  renamed params
b858db8  typo
4c2a838  h2 context?
81a2c0c  fix some param names; add some missing params, no docs
bcf8a4c  add some missing params, no docs
af77f86  add some missing params, no docs; fix some param names
01df24e  fix some params
6161056  fix some params
68508b6  fix some params
285aeb6  doxygen complains here no matter what
529bcfa  rm some missing params, typos
cd21226  rm some missing params, add some new ones
48a4bc2  fix params
c844628  missing param
312ce37  missing param; rename one
ec2fdec  missing param
05e15e0  missing params
d515858  change "<" to &lt; to make doxygen happy
b476a28  wrong place
2015-07-20 18:48:00 +01:00

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/******************************************************************************
** 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 "shapeclassifier.h"
#include "ambigs.h"
#include "blobclass.h"
#include "blobs.h"
#include "callcpp.h"
#include "classify.h"
#include "const.h"
#include "dict.h"
#include "efio.h"
#include "emalloc.h"
#include "featdefs.h"
#include "float2int.h"
#include "genericvector.h"
#include "globals.h"
#include "helpers.h"
#include "intfx.h"
#include "intproto.h"
#include "mfoutline.h"
#include "ndminx.h"
#include "normfeat.h"
#include "normmatch.h"
#include "outfeat.h"
#include "pageres.h"
#include "params.h"
#include "picofeat.h"
#include "shapetable.h"
#include "tessclassifier.h"
#include "trainingsample.h"
#include "unicharset.h"
#include "werd.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 (0.0f)
using tesseract::UnicharRating;
using tesseract::ScoredFont;
struct ADAPT_RESULTS {
inT32 BlobLength;
bool HasNonfragment;
UNICHAR_ID best_unichar_id;
int best_match_index;
FLOAT32 best_rating;
GenericVector<UnicharRating> 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;
ComputeBest();
}
// Computes best_unichar_id, best_match_index and best_rating.
void ComputeBest() {
best_unichar_id = INVALID_UNICHAR_ID;
best_match_index = -1;
best_rating = WORST_POSSIBLE_RATING;
for (int i = 0; i < match.size(); ++i) {
if (match[i].rating > best_rating) {
best_rating = match[i].rating;
best_unichar_id = match[i].unichar_id;
best_match_index = i;
}
}
}
};
struct PROTO_KEY {
ADAPT_TEMPLATES Templates;
CLASS_ID ClassId;
int ConfigId;
};
/*-----------------------------------------------------------------------------
Private Macros
-----------------------------------------------------------------------------*/
inline bool MarginalMatch(float confidence, float matcher_great_threshold) {
return (1.0f - confidence) > matcher_great_threshold;
}
/*-----------------------------------------------------------------------------
Private Function Prototypes
-----------------------------------------------------------------------------*/
// Returns the index of the given id in results, if present, or the size of the
// vector (index it will go at) if not present.
static int FindScoredUnichar(UNICHAR_ID id, const ADAPT_RESULTS& results) {
for (int i = 0; i < results.match.size(); i++) {
if (results.match[i].unichar_id == id)
return i;
}
return results.match.size();
}
// Returns the current rating for a unichar id if we have rated it, defaulting
// to WORST_POSSIBLE_RATING.
static float ScoredUnichar(UNICHAR_ID id, const ADAPT_RESULTS& results) {
int index = FindScoredUnichar(id, results);
if (index >= results.match.size()) return WORST_POSSIBLE_RATING;
return results.match[index].rating;
}
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(&UnicharRating::SortDescendingRating);
RemoveExtraPuncs(Results);
Results->ComputeBest();
ConvertMatchesToChoices(Blob->denorm(), Blob->bounding_box(), Results,
Choices);
// TODO(rays) Move to before ConvertMatchesToChoices!
if (LargeSpeckle(*Blob) || Choices->length() == 0)
AddLargeSpeckleTo(Results->BlobLength, Choices);
if (matcher_debug_level >= 1) {
tprintf("AD Matches = ");
PrintAdaptiveMatchResults(*Results);
}
#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 fontname is not NULL, then LearnBlob
// is called and the data will be saved in an internal buffer.
// Otherwise AdaptToBlob is called for adaption within a document.
void Classify::LearnWord(const char* fontname, WERD_RES* word) {
int word_len = word->correct_text.size();
if (word_len == 0) return;
float* thresholds = NULL;
if (fontname == 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(fontname, 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(fontname, 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(fontname, 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(fontname, 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(fontname, 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 fontname is not NULL, then LearnBlob is called and the data will be
// saved in an internal buffer 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* fontname, 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) {
SEAM::JoinPieces(word->seam_array, word->chopped_word->blobs, start,
start + length - 1);
}
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 (fontname != 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(fontname, rotated_blob, 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, AdaptedTemplates);
if (BackupAdaptedTemplates != NULL) {
// Adapt the backup templates too. They will be used if the primary gets
// too full.
AdaptToChar(rotated_blob, class_id, font_id, threshold,
BackupAdaptedTemplates);
}
} 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;
}
SEAM::BreakPieces(word->seam_array, word->chopped_word->blobs, start,
start + length - 1);
} // 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;
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);
}
}
if (AdaptedTemplates != NULL) {
free_adapted_templates(AdaptedTemplates);
AdaptedTemplates = NULL;
}
if (BackupAdaptedTemplates != NULL) {
free_adapted_templates(BackupAdaptedTemplates);
BackupAdaptedTemplates = 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 {
cprintf("\nReading pre-adapted templates from %s ...\n",
Filename.string());
fflush(stdout);
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);
if (BackupAdaptedTemplates != NULL)
free_adapted_templates(BackupAdaptedTemplates);
BackupAdaptedTemplates = NULL;
NumAdaptationsFailed = 0;
}
// If there are backup adapted templates, switches to those, otherwise resets
// the main adaptive classifier (because it is full.)
void Classify::SwitchAdaptiveClassifier() {
if (BackupAdaptedTemplates == NULL) {
ResetAdaptiveClassifierInternal();
return;
}
if (classify_learning_debug_level > 0) {
tprintf("Switch to backup adaptive classifier (NumAdaptationsFailed=%d)\n",
NumAdaptationsFailed);
}
free_adapted_templates(AdaptedTemplates);
AdaptedTemplates = BackupAdaptedTemplates;
BackupAdaptedTemplates = NULL;
NumAdaptationsFailed = 0;
}
// Resets the backup adaptive classifier to empty.
void Classify::StartBackupAdaptiveClassifier() {
if (BackupAdaptedTemplates != NULL)
free_adapted_templates(BackupAdaptedTemplates);
BackupAdaptedTemplates = NewAdaptedTemplates(true);
}
/*---------------------------------------------------------------------------*/
/**
* 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) {
tprintf("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
*
* @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
* @param adaptive_templates current set of adapted templates
*
* Globals:
* - 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,
ADAPT_TEMPLATES adaptive_templates) {
int NumFeatures;
INT_FEATURE_ARRAY IntFeatures;
UnicharRating int_result;
INT_CLASS IClass;
ADAPT_CLASS Class;
TEMP_CONFIG TempConfig;
FEATURE_SET FloatFeatures;
int NewTempConfigId;
if (!LegalClassId (ClassId))
return;
int_result.unichar_id = ClassId;
Class = adaptive_templates->Class[ClassId];
assert(Class != NULL);
if (IsEmptyAdaptedClass(Class)) {
InitAdaptedClass(Blob, ClassId, FontinfoId, Class, adaptive_templates);
} else {
IClass = ClassForClassId(adaptive_templates->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,
&int_result, classify_adapt_feature_threshold,
NO_DEBUG, matcher_debug_separate_windows);
FreeBitVector(MatchingFontConfigs);
SetAdaptiveThreshold(Threshold);
if (1.0f - int_result.rating <= Threshold) {
if (ConfigIsPermanent(Class, int_result.config)) {
if (classify_learning_debug_level >= 1)
tprintf("Found good match to perm config %d = %4.1f%%.\n",
int_result.config, int_result.rating * 100.0);
FreeFeatureSet(FloatFeatures);
return;
}
TempConfig = TempConfigFor(Class, int_result.config);
IncreaseConfidence(TempConfig);
if (TempConfig->NumTimesSeen > Class->MaxNumTimesSeen) {
Class->MaxNumTimesSeen = TempConfig->NumTimesSeen;
}
if (classify_learning_debug_level >= 1)
tprintf("Increasing reliability of temp config %d to %d.\n",
int_result.config, TempConfig->NumTimesSeen);
if (TempConfigReliable(ClassId, TempConfig)) {
MakePermanent(adaptive_templates, ClassId, int_result.config, Blob);
UpdateAmbigsGroup(ClassId, Blob);
}
} else {
if (classify_learning_debug_level >= 1) {
tprintf("Found poor match to temp config %d = %4.1f%%.\n",
int_result.config, int_result.rating * 100.0);
if (classify_learning_debug_level > 2)
DisplayAdaptedChar(Blob, IClass);
}
NewTempConfigId =
MakeNewTemporaryConfig(adaptive_templates, ClassId, FontinfoId,
NumFeatures, IntFeatures, FloatFeatures);
if (NewTempConfigId >= 0 &&
TempConfigReliable(ClassId, TempConfigFor(Class, NewTempConfigId))) {
MakePermanent(adaptive_templates, 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;
UnicharRating int_result;
im_.Match(int_class, AllProtosOn, AllConfigsOn,
bl_features.size(), &bl_features[0],
&int_result, classify_adapt_feature_threshold,
NO_DEBUG, matcher_debug_separate_windows);
tprintf("Best match to temp config %d = %4.1f%%.\n",
int_result.config, int_result.rating * 100.0);
if (classify_learning_debug_level >= 2) {
uinT32 ConfigMask;
ConfigMask = 1 << int_result.config;
ShowMatchDisplay();
im_.Match(int_class, AllProtosOn, (BIT_VECTOR)&ConfigMask,
bl_features.size(), &bl_features[0],
&int_result, 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 new_result new result to add
* @param[out] results results to add new result to
*
* @note Exceptions: none
* @note History: Tue Mar 12 18:19:29 1991, DSJ, Created.
*/
void Classify::AddNewResult(const UnicharRating& new_result,
ADAPT_RESULTS *results) {
int old_match = FindScoredUnichar(new_result.unichar_id, *results);
if (new_result.rating + matcher_bad_match_pad < results->best_rating ||
(old_match < results->match.size() &&
new_result.rating <= results->match[old_match].rating))
return; // New one not good enough.
if (!unicharset.get_fragment(new_result.unichar_id))
results->HasNonfragment = true;
if (old_match < results->match.size()) {
results->match[old_match].rating = new_result.rating;
} else {
results->match.push_back(new_result);
}
if (new_result.rating > results->best_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(new_result.unichar_id)) {
results->best_match_index = old_match;
results->best_rating = new_result.rating;
results->best_unichar_id = new_result.unichar_id;
}
} /* 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 unichar id's to match against
* @param[out] results place to put match results
* @param int_features
* @param fx_info
*
* @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()];
UnicharRating int_result;
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;
int_result.unichar_id = class_id;
im_.Match(ClassForClassId(templates, class_id),
AllProtosOn, AllConfigsOn,
int_features.size(), &int_features[0],
&int_result,
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, &int_result, 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();
UnicharRating int_result;
for (int c = 0; c < results.size(); c++) {
CLASS_ID class_id = results[c].Class;
BIT_VECTOR protos = classes != NULL ? classes[class_id]->PermProtos
: AllProtosOn;
BIT_VECTOR configs = classes != NULL ? classes[class_id]->PermConfigs
: AllConfigsOn;
int_result.unichar_id = class_id;
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,
UnicharRating* int_result, ADAPT_RESULTS* final_results) {
if (classes != NULL) {
// Adapted result. Convert configs to fontinfo_ids.
int_result->adapted = true;
for (int f = 0; f < int_result->fonts.size(); ++f) {
int_result->fonts[f].fontinfo_id =
GetFontinfoId(classes[class_id], int_result->fonts[f].fontinfo_id);
}
} else {
// Pre-trained result. Map fonts using font_sets_.
int_result->adapted = false;
for (int f = 0; f < int_result->fonts.size(); ++f) {
int_result->fonts[f].fontinfo_id =
ClassAndConfigIDToFontOrShapeID(class_id,
int_result->fonts[f].fontinfo_id);
}
if (shape_table_ != NULL) {
// Two possible cases:
// 1. Flat shapetable. All unichar-ids of the shapes referenced by
// int_result->fonts are the same. In this case build a new vector of
// mapped fonts and replace the fonts in int_result.
// 2. Multi-unichar shapetable. Variable unichars in the shapes referenced
// by int_result. In this case, build a vector of UnicharRating to
// gather together different font-ids for each unichar. Also covers case1.
GenericVector<UnicharRating> mapped_results;
for (int f = 0; f < int_result->fonts.size(); ++f) {
int shape_id = int_result->fonts[f].fontinfo_id;
const Shape& shape = shape_table_->GetShape(shape_id);
for (int c = 0; c < shape.size(); ++c) {
int unichar_id = shape[c].unichar_id;
if (!unicharset.get_enabled(unichar_id)) continue;
// Find the mapped_result for unichar_id.
int r = 0;
for (r = 0; r < mapped_results.size() &&
mapped_results[r].unichar_id != unichar_id; ++r) {}
if (r == mapped_results.size()) {
mapped_results.push_back(*int_result);
mapped_results[r].unichar_id = unichar_id;
mapped_results[r].fonts.truncate(0);
}
for (int i = 0; i < shape[c].font_ids.size(); ++i) {
mapped_results[r].fonts.push_back(
ScoredFont(shape[c].font_ids[i], int_result->fonts[f].score));
}
}
}
for (int m = 0; m < mapped_results.size(); ++m) {
mapped_results[m].rating =
ComputeCorrectedRating(debug, mapped_results[m].unichar_id,
cp_rating, int_result->rating,
int_result->feature_misses, bottom, top,
blob_length, matcher_multiplier, cn_factors);
AddNewResult(mapped_results[m], final_results);
}
return;
}
}
if (unicharset.get_enabled(class_id)) {
int_result->rating = ComputeCorrectedRating(debug, class_id, cp_rating,
int_result->rating,
int_result->feature_misses,
bottom, top, blob_length,
matcher_multiplier, cn_factors);
AddNewResult(*int_result, final_results);
}
}
// Applies a set of corrections to the confidence im_rating,
// including the cn_correction, miss penalty and additional penalty
// for non-alnums being vertical misfits. Returns the corrected confidence.
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(1.0 - 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 = 1.0 - (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,
(1.0 - im_rating) * 100.0,
(cn_corrected - (1.0 - 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
* @param int_features
* @param fx_info
*
* @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(), -1, &int_features[0],
CharNormArray, BaselineCutoffs, &Results->CPResults);
if (matcher_debug_level >= 2 || classify_debug_level > 1)
tprintf("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_unichar_id;
if (ClassId == INVALID_UNICHAR_ID || Results->best_match_index < 0)
return NULL;
return Templates->Class[ClassId]->
Config[Results->match[Results->best_match_index].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 sample templates to classify unknown against
* @param adapt_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) {
AddNewResult(unichar_results[r], adapt_results);
}
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, keep_this, 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++) {
results->push_back(adapt_results->match[i]);
}
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) {
float rating = results->BlobLength / matcher_avg_noise_size;
rating *= rating;
rating /= 1.0 + rating;
AddNewResult(UnicharRating(UNICHAR_SPACE, 1.0f - rating), results);
} /* ClassifyAsNoise */
/// 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++) {
const UnicharRating& result = Results->match[i];
bool adapted = result.adapted;
bool current_is_frag = (unicharset.get_fragment(result.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 = (1.0f - result.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(result.unichar_id, unicharset, box,
&min_xheight, &max_xheight, &yshift);
BLOB_CHOICE* choice =
new BLOB_CHOICE(result.unichar_id, Rating, Certainty,
unicharset.get_script(result.unichar_id),
min_xheight, max_xheight, yshift,
adapted ? BCC_ADAPTED_CLASSIFIER
: BCC_STATIC_CLASSIFIER);
choice->set_fonts(result.fonts);
temp_it.add_to_end(choice);
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;
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_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_reliable_adaptive_result 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_rating,
matcher_reliable_adaptive_result) &&
!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(&UnicharRating::SortDescendingRating);
/* 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 templates used to compute char norm adjustments
* @param pruner_norm_array Array of factors from blob normalization
* process
* @param char_norm_array array to fill with dummy char norm adjustments
* @param fx_info
*
* 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 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(const ADAPT_RESULTS& results) {
for (int i = 0; i < results.match.size(); ++i) {
tprintf("%s ", unicharset.debug_str(results.match[i].unichar_id).string());
results.match[i].Print();
}
} /* 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_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;
float scored_one = ScoredUnichar(unichar_id_one, *Results);
float scored_zero = ScoredUnichar(unichar_id_zero, *Results);
for (Next = NextGood = 0; Next < Results->match.size(); Next++) {
const UnicharRating& match = Results->match[Next];
if (match.rating >= BadMatchThreshold) {
if (!unicharset.get_isalpha(match.unichar_id) ||
strstr(romans,
unicharset.id_to_unichar(match.unichar_id)) != NULL) {
} else if (unicharset.eq(match.unichar_id, "l") &&
scored_one < BadMatchThreshold) {
Results->match[Next].unichar_id = unichar_id_one;
} else if (unicharset.eq(match.unichar_id, "O") &&
scored_zero < BadMatchThreshold) {
Results->match[Next].unichar_id = unichar_id_zero;
} else {
Results->match[Next].unichar_id = INVALID_UNICHAR_ID; // Don't copy.
}
if (Results->match[Next].unichar_id != INVALID_UNICHAR_ID) {
if (NextGood == Next) {
++NextGood;
} else {
Results->match[NextGood++] = Results->match[Next];
}
}
}
}
} else {
for (Next = NextGood = 0; Next < Results->match.size(); Next++) {
if (Results->match[Next].rating >= BadMatchThreshold) {
if (NextGood == Next) {
++NextGood;
} else {
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++) {
const UnicharRating& match = Results->match[Next];
bool keep = true;
if (strstr(punc_chars,
unicharset.id_to_unichar(match.unichar_id)) != NULL) {
if (punc_count >= 2)
keep = false;
punc_count++;
} else {
if (strstr(digit_chars,
unicharset.id_to_unichar(match.unichar_id)) != NULL) {
if (digit_count >= 1)
keep = false;
digit_count++;
}
}
if (keep) {
if (NextGood == Next) {
++NextGood;
} 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;
}
UnicharRating 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
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