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tesseract/ccmain/applybox.cpp
Ray Smith 84920b92b3 Font and classifier output structure cleanup. 
Font recognition was poor, due to forcing a 1st and 2nd choice at
a character level, when the total score for the correct font is often
correct at the word level, so allowed the propagation of a full set
of fonts and scores to the word recognizer, which can now decide word
level fonts using the scores instead of simple votes.

Change precipitated a cleanup of output data structures for classifier
results, eliminating ScoredClass and INT_RESULT_STRUCT, with a few
extra elements going in UnicharRating, and using that wherever possible.
That added the extra complexity of 1-rating due to a flip between 0 is
good and 0 is bad for the internal classifier scores before they are
converted to rating and certainty.
2015-05-12 17:24:34 -07:00

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/**********************************************************************
* File: applybox.cpp (Formerly applybox.c)
* Description: Re segment rows according to box file data
* Author: Phil Cheatle
* Created: Wed Nov 24 09:11:23 GMT 1993
*
* (C) Copyright 1993, Hewlett-Packard Ltd.
** 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.
*
**********************************************************************/
#ifdef _MSC_VER
#pragma warning(disable:4244) // Conversion warnings
#endif
#include <ctype.h>
#include <string.h>
#ifdef __UNIX__
#include <assert.h>
#include <errno.h>
#endif
#include "allheaders.h"
#include "boxread.h"
#include "chopper.h"
#include "pageres.h"
#include "unichar.h"
#include "unicharset.h"
#include "tesseractclass.h"
#include "genericvector.h"
// Max number of blobs to classify together in FindSegmentation.
const int kMaxGroupSize = 4;
// Max fraction of median allowed as deviation in xheight before switching
// to median.
const double kMaxXHeightDeviationFraction = 0.125;
/*************************************************************************
* The box file is assumed to contain box definitions, one per line, of the
* following format for blob-level boxes:
* <UTF8 str> <left> <bottom> <right> <top> <page id>
* and for word/line-level boxes:
* WordStr <left> <bottom> <right> <top> <page id> #<space-delimited word str>
* NOTES:
* The boxes use tesseract coordinates, i.e. 0,0 is at BOTTOM-LEFT.
*
* <page id> is 0-based, and the page number is used for multipage input (tiff).
*
* In the blob-level form, each line represents a recognizable unit, which may
* be several UTF-8 bytes, but there is a bounding box around each recognizable
* unit, and no classifier is needed to train in this mode (bootstrapping.)
*
* In the word/line-level form, the line begins with the literal "WordStr", and
* the bounding box bounds either a whole line or a whole word. The recognizable
* units in the word/line are listed after the # at the end of the line and
* are space delimited, ignoring any original spaces on the line.
* Eg.
* word -> #w o r d
* multi word line -> #m u l t i w o r d l i n e
* The recognizable units must be space-delimited in order to allow multiple
* unicodes to be used for a single recognizable unit, eg Hindi.
* In this mode, the classifier must have been pre-trained with the desired
* character set, or it will not be able to find the character segmentations.
*************************************************************************/
namespace tesseract {
static void clear_any_old_text(BLOCK_LIST *block_list) {
BLOCK_IT block_it(block_list);
for (block_it.mark_cycle_pt();
!block_it.cycled_list(); block_it.forward()) {
ROW_IT row_it(block_it.data()->row_list());
for (row_it.mark_cycle_pt(); !row_it.cycled_list(); row_it.forward()) {
WERD_IT word_it(row_it.data()->word_list());
for (word_it.mark_cycle_pt();
!word_it.cycled_list(); word_it.forward()) {
word_it.data()->set_text("");
}
}
}
}
// Applies the box file based on the image name fname, and resegments
// the words in the block_list (page), with:
// blob-mode: one blob per line in the box file, words as input.
// word/line-mode: one blob per space-delimited unit after the #, and one word
// per line in the box file. (See comment above for box file format.)
// If find_segmentation is true, (word/line mode) then the classifier is used
// to re-segment words/lines to match the space-delimited truth string for
// each box. In this case, the input box may be for a word or even a whole
// text line, and the output words will contain multiple blobs corresponding
// to the space-delimited input string.
// With find_segmentation false, no classifier is needed, but the chopper
// can still be used to correctly segment touching characters with the help
// of the input boxes.
// In the returned PAGE_RES, the WERD_RES are setup as they would be returned
// from normal classification, ie. with a word, chopped_word, rebuild_word,
// seam_array, denorm, box_word, and best_state, but NO best_choice or
// raw_choice, as they would require a UNICHARSET, which we aim to avoid.
// Instead, the correct_text member of WERD_RES is set, and this may be later
// converted to a best_choice using CorrectClassifyWords. CorrectClassifyWords
// is not required before calling ApplyBoxTraining.
PAGE_RES* Tesseract::ApplyBoxes(const STRING& fname,
bool find_segmentation,
BLOCK_LIST *block_list) {
GenericVector<TBOX> boxes;
GenericVector<STRING> texts, full_texts;
if (!ReadAllBoxes(applybox_page, true, fname, &boxes, &texts, &full_texts,
NULL)) {
return NULL; // Can't do it.
}
int box_count = boxes.size();
int box_failures = 0;
// Add an empty everything to the end.
boxes.push_back(TBOX());
texts.push_back(STRING());
full_texts.push_back(STRING());
// In word mode, we use the boxes to make a word for each box, but
// in blob mode we use the existing words and maximally chop them first.
PAGE_RES* page_res = find_segmentation ?
NULL : SetupApplyBoxes(boxes, block_list);
clear_any_old_text(block_list);
for (int i = 0; i < boxes.size() - 1; i++) {
bool foundit = false;
if (page_res != NULL) {
if (i == 0) {
foundit = ResegmentCharBox(page_res, NULL, boxes[i], boxes[i + 1],
full_texts[i].string());
} else {
foundit = ResegmentCharBox(page_res, &boxes[i-1], boxes[i],
boxes[i + 1], full_texts[i].string());
}
} else {
foundit = ResegmentWordBox(block_list, boxes[i], boxes[i + 1],
texts[i].string());
}
if (!foundit) {
box_failures++;
ReportFailedBox(i, boxes[i], texts[i].string(),
"FAILURE! Couldn't find a matching blob");
}
}
if (page_res == NULL) {
// In word/line mode, we now maximally chop all the words and resegment
// them with the classifier.
page_res = SetupApplyBoxes(boxes, block_list);
ReSegmentByClassification(page_res);
}
if (applybox_debug > 0) {
tprintf("APPLY_BOXES:\n");
tprintf(" Boxes read from boxfile: %6d\n", box_count);
if (box_failures > 0)
tprintf(" Boxes failed resegmentation: %6d\n", box_failures);
}
TidyUp(page_res);
return page_res;
}
// Helper computes median xheight in the image.
static double MedianXHeight(BLOCK_LIST *block_list) {
BLOCK_IT block_it(block_list);
STATS xheights(0, block_it.data()->bounding_box().height());
for (block_it.mark_cycle_pt();
!block_it.cycled_list(); block_it.forward()) {
ROW_IT row_it(block_it.data()->row_list());
for (row_it.mark_cycle_pt(); !row_it.cycled_list(); row_it.forward()) {
xheights.add(IntCastRounded(row_it.data()->x_height()), 1);
}
}
return xheights.median();
}
// Any row xheight that is significantly different from the median is set
// to the median.
void Tesseract::PreenXHeights(BLOCK_LIST *block_list) {
double median_xheight = MedianXHeight(block_list);
double max_deviation = kMaxXHeightDeviationFraction * median_xheight;
// Strip all fuzzy space markers to simplify the PAGE_RES.
BLOCK_IT b_it(block_list);
for (b_it.mark_cycle_pt(); !b_it.cycled_list(); b_it.forward()) {
BLOCK* block = b_it.data();
ROW_IT r_it(block->row_list());
for (r_it.mark_cycle_pt(); !r_it.cycled_list(); r_it.forward ()) {
ROW* row = r_it.data();
float diff = fabs(row->x_height() - median_xheight);
if (diff > max_deviation) {
if (applybox_debug) {
tprintf("row xheight=%g, but median xheight = %g\n",
row->x_height(), median_xheight);
}
row->set_x_height(static_cast<float>(median_xheight));
}
}
}
}
// Builds a PAGE_RES from the block_list in the way required for ApplyBoxes:
// All fuzzy spaces are removed, and all the words are maximally chopped.
PAGE_RES* Tesseract::SetupApplyBoxes(const GenericVector<TBOX>& boxes,
BLOCK_LIST *block_list) {
PreenXHeights(block_list);
// Strip all fuzzy space markers to simplify the PAGE_RES.
BLOCK_IT b_it(block_list);
for (b_it.mark_cycle_pt(); !b_it.cycled_list(); b_it.forward()) {
BLOCK* block = b_it.data();
ROW_IT r_it(block->row_list());
for (r_it.mark_cycle_pt(); !r_it.cycled_list(); r_it.forward ()) {
ROW* row = r_it.data();
WERD_IT w_it(row->word_list());
for (w_it.mark_cycle_pt(); !w_it.cycled_list(); w_it.forward()) {
WERD* word = w_it.data();
if (word->cblob_list()->empty()) {
delete w_it.extract();
} else {
word->set_flag(W_FUZZY_SP, false);
word->set_flag(W_FUZZY_NON, false);
}
}
}
}
PAGE_RES* page_res = new PAGE_RES(false, block_list, NULL);
PAGE_RES_IT pr_it(page_res);
WERD_RES* word_res;
while ((word_res = pr_it.word()) != NULL) {
MaximallyChopWord(boxes, pr_it.block()->block,
pr_it.row()->row, word_res);
pr_it.forward();
}
return page_res;
}
// Tests the chopper by exhaustively running chop_one_blob.
// The word_res will contain filled chopped_word, seam_array, denorm,
// box_word and best_state for the maximally chopped word.
void Tesseract::MaximallyChopWord(const GenericVector<TBOX>& boxes,
BLOCK* block, ROW* row,
WERD_RES* word_res) {
if (!word_res->SetupForRecognition(unicharset, this, BestPix(),
tessedit_ocr_engine_mode, NULL,
classify_bln_numeric_mode,
textord_use_cjk_fp_model,
poly_allow_detailed_fx,
row, block)) {
word_res->CloneChoppedToRebuild();
return;
}
if (chop_debug) {
tprintf("Maximally chopping word at:");
word_res->word->bounding_box().print();
}
GenericVector<BLOB_CHOICE*> blob_choices;
ASSERT_HOST(!word_res->chopped_word->blobs.empty());
float rating = static_cast<float>(MAX_INT8);
for (int i = 0; i < word_res->chopped_word->NumBlobs(); ++i) {
// The rating and certainty are not quite arbitrary. Since
// select_blob_to_chop uses the worst certainty to choose, they all have
// to be different, so starting with MAX_INT8, subtract 1/8 for each blob
// in here, and then divide by e each time they are chopped, which
// should guarantee a set of unequal values for the whole tree of blobs
// produced, however much chopping is required. The chops are thus only
// limited by the ability of the chopper to find suitable chop points,
// and not by the value of the certainties.
BLOB_CHOICE* choice =
new BLOB_CHOICE(0, rating, -rating, -1, 0.0f, 0.0f, 0.0f, BCC_FAKE);
blob_choices.push_back(choice);
rating -= 0.125f;
}
const double e = exp(1.0); // The base of natural logs.
int blob_number;
int right_chop_index = 0;
if (!assume_fixed_pitch_char_segment) {
// We only chop if the language is not fixed pitch like CJK.
SEAM* seam = NULL;
while ((seam = chop_one_blob(boxes, blob_choices, word_res,
&blob_number)) != NULL) {
word_res->InsertSeam(blob_number, seam);
BLOB_CHOICE* left_choice = blob_choices[blob_number];
rating = left_choice->rating() / e;
left_choice->set_rating(rating);
left_choice->set_certainty(-rating);
// combine confidence w/ serial #
BLOB_CHOICE* right_choice = new BLOB_CHOICE(++right_chop_index,
rating - 0.125f, -rating, -1,
0.0f, 0.0f, 0.0f, BCC_FAKE);
blob_choices.insert(right_choice, blob_number + 1);
}
}
word_res->CloneChoppedToRebuild();
word_res->FakeClassifyWord(blob_choices.size(), &blob_choices[0]);
}
// Helper to compute the dispute resolution metric.
// Disputed blob resolution. The aim is to give the blob to the most
// appropriate boxfile box. Most of the time it is obvious, but if
// two boxfile boxes overlap significantly it is not. If a small boxfile
// box takes most of the blob, and a large boxfile box does too, then
// we want the small boxfile box to get it, but if the small box
// is much smaller than the blob, we don't want it to get it.
// Details of the disputed blob resolution:
// Given a box with area A, and a blob with area B, with overlap area C,
// then the miss metric is (A-C)(B-C)/(AB) and the box with minimum
// miss metric gets the blob.
static double BoxMissMetric(const TBOX& box1, const TBOX& box2) {
int overlap_area = box1.intersection(box2).area();
double miss_metric = box1.area()- overlap_area;
miss_metric /= box1.area();
miss_metric *= box2.area() - overlap_area;
miss_metric /= box2.area();
return miss_metric;
}
// Gather consecutive blobs that match the given box into the best_state
// and corresponding correct_text.
// Fights over which box owns which blobs are settled by pre-chopping and
// applying the blobs to box or next_box with the least non-overlap.
// Returns false if the box was in error, which can only be caused by
// failing to find an appropriate blob for a box.
// This means that occasionally, blobs may be incorrectly segmented if the
// chopper fails to find a suitable chop point.
bool Tesseract::ResegmentCharBox(PAGE_RES* page_res, const TBOX *prev_box,
const TBOX& box, const TBOX& next_box,
const char* correct_text) {
if (applybox_debug > 1) {
tprintf("\nAPPLY_BOX: in ResegmentCharBox() for %s\n", correct_text);
}
PAGE_RES_IT page_res_it(page_res);
WERD_RES* word_res;
for (word_res = page_res_it.word(); word_res != NULL;
word_res = page_res_it.forward()) {
if (!word_res->box_word->bounding_box().major_overlap(box))
continue;
if (applybox_debug > 1) {
tprintf("Checking word box:");
word_res->box_word->bounding_box().print();
}
int word_len = word_res->box_word->length();
for (int i = 0; i < word_len; ++i) {
TBOX char_box = TBOX();
int blob_count = 0;
for (blob_count = 0; i + blob_count < word_len; ++blob_count) {
TBOX blob_box = word_res->box_word->BlobBox(i + blob_count);
if (!blob_box.major_overlap(box))
break;
if (word_res->correct_text[i + blob_count].length() > 0)
break; // Blob is claimed already.
double current_box_miss_metric = BoxMissMetric(blob_box, box);
double next_box_miss_metric = BoxMissMetric(blob_box, next_box);
if (applybox_debug > 2) {
tprintf("Checking blob:");
blob_box.print();
tprintf("Current miss metric = %g, next = %g\n",
current_box_miss_metric, next_box_miss_metric);
}
if (current_box_miss_metric > next_box_miss_metric)
break; // Blob is a better match for next box.
char_box += blob_box;
}
if (blob_count > 0) {
if (applybox_debug > 1) {
tprintf("Index [%d, %d) seem good.\n", i, i + blob_count);
}
if (!char_box.almost_equal(box, 3) &&
(box.x_gap(next_box) < -3 ||
(prev_box != NULL && prev_box->x_gap(box) < -3))) {
return false;
}
// We refine just the box_word, best_state and correct_text here.
// The rebuild_word is made in TidyUp.
// blob_count blobs are put together to match the box. Merge the
// box_word boxes, save the blob_count in the state and the text.
word_res->box_word->MergeBoxes(i, i + blob_count);
word_res->best_state[i] = blob_count;
word_res->correct_text[i] = correct_text;
if (applybox_debug > 2) {
tprintf("%d Blobs match: blob box:", blob_count);
word_res->box_word->BlobBox(i).print();
tprintf("Matches box:");
box.print();
tprintf("With next box:");
next_box.print();
}
// Eliminated best_state and correct_text entries for the consumed
// blobs.
for (int j = 1; j < blob_count; ++j) {
word_res->best_state.remove(i + 1);
word_res->correct_text.remove(i + 1);
}
// Assume that no box spans multiple source words, so we are done with
// this box.
if (applybox_debug > 1) {
tprintf("Best state = ");
for (int j = 0; j < word_res->best_state.size(); ++j) {
tprintf("%d ", word_res->best_state[j]);
}
tprintf("\n");
tprintf("Correct text = [[ ");
for (int j = 0; j < word_res->correct_text.size(); ++j) {
tprintf("%s ", word_res->correct_text[j].string());
}
tprintf("]]\n");
}
return true;
}
}
}
if (applybox_debug > 0) {
tprintf("FAIL!\n");
}
return false; // Failure.
}
// Consume all source blobs that strongly overlap the given box,
// putting them into a new word, with the correct_text label.
// Fights over which box owns which blobs are settled by
// applying the blobs to box or next_box with the least non-overlap.
// Returns false if the box was in error, which can only be caused by
// failing to find an overlapping blob for a box.
bool Tesseract::ResegmentWordBox(BLOCK_LIST *block_list,
const TBOX& box, const TBOX& next_box,
const char* correct_text) {
if (applybox_debug > 1) {
tprintf("\nAPPLY_BOX: in ResegmentWordBox() for %s\n", correct_text);
}
WERD* new_word = NULL;
BLOCK_IT b_it(block_list);
for (b_it.mark_cycle_pt(); !b_it.cycled_list(); b_it.forward()) {
BLOCK* block = b_it.data();
if (!box.major_overlap(block->bounding_box()))
continue;
ROW_IT r_it(block->row_list());
for (r_it.mark_cycle_pt(); !r_it.cycled_list(); r_it.forward()) {
ROW* row = r_it.data();
if (!box.major_overlap(row->bounding_box()))
continue;
WERD_IT w_it(row->word_list());
for (w_it.mark_cycle_pt(); !w_it.cycled_list(); w_it.forward()) {
WERD* word = w_it.data();
if (applybox_debug > 2) {
tprintf("Checking word:");
word->bounding_box().print();
}
if (word->text() != NULL && word->text()[0] != '\0')
continue; // Ignore words that are already done.
if (!box.major_overlap(word->bounding_box()))
continue;
C_BLOB_IT blob_it(word->cblob_list());
for (blob_it.mark_cycle_pt(); !blob_it.cycled_list();
blob_it.forward()) {
C_BLOB* blob = blob_it.data();
TBOX blob_box = blob->bounding_box();
if (!blob_box.major_overlap(box))
continue;
double current_box_miss_metric = BoxMissMetric(blob_box, box);
double next_box_miss_metric = BoxMissMetric(blob_box, next_box);
if (applybox_debug > 2) {
tprintf("Checking blob:");
blob_box.print();
tprintf("Current miss metric = %g, next = %g\n",
current_box_miss_metric, next_box_miss_metric);
}
if (current_box_miss_metric > next_box_miss_metric)
continue; // Blob is a better match for next box.
if (applybox_debug > 2) {
tprintf("Blob match: blob:");
blob_box.print();
tprintf("Matches box:");
box.print();
tprintf("With next box:");
next_box.print();
}
if (new_word == NULL) {
// Make a new word with a single blob.
new_word = word->shallow_copy();
new_word->set_text(correct_text);
w_it.add_to_end(new_word);
}
C_BLOB_IT new_blob_it(new_word->cblob_list());
new_blob_it.add_to_end(blob_it.extract());
}
}
}
}
if (new_word == NULL && applybox_debug > 0) tprintf("FAIL!\n");
return new_word != NULL;
}
// Resegments the words by running the classifier in an attempt to find the
// correct segmentation that produces the required string.
void Tesseract::ReSegmentByClassification(PAGE_RES* page_res) {
PAGE_RES_IT pr_it(page_res);
WERD_RES* word_res;
for (; (word_res = pr_it.word()) != NULL; pr_it.forward()) {
WERD* word = word_res->word;
if (word->text() == NULL || word->text()[0] == '\0')
continue; // Ignore words that have no text.
// Convert the correct text to a vector of UNICHAR_ID
GenericVector<UNICHAR_ID> target_text;
if (!ConvertStringToUnichars(word->text(), &target_text)) {
tprintf("APPLY_BOX: FAILURE: can't find class_id for '%s'\n",
word->text());
pr_it.DeleteCurrentWord();
continue;
}
if (!FindSegmentation(target_text, word_res)) {
tprintf("APPLY_BOX: FAILURE: can't find segmentation for '%s'\n",
word->text());
pr_it.DeleteCurrentWord();
continue;
}
}
}
// Converts the space-delimited string of utf8 text to a vector of UNICHAR_ID.
// Returns false if an invalid UNICHAR_ID is encountered.
bool Tesseract::ConvertStringToUnichars(const char* utf8,
GenericVector<UNICHAR_ID>* class_ids) {
for (int step = 0; *utf8 != '\0'; utf8 += step) {
const char* next_space = strchr(utf8, ' ');
if (next_space == NULL)
next_space = utf8 + strlen(utf8);
step = next_space - utf8;
UNICHAR_ID class_id = unicharset.unichar_to_id(utf8, step);
if (class_id == INVALID_UNICHAR_ID) {
return false;
}
while (utf8[step] == ' ')
++step;
class_ids->push_back(class_id);
}
return true;
}
// Resegments the word to achieve the target_text from the classifier.
// Returns false if the re-segmentation fails.
// Uses brute-force combination of up to kMaxGroupSize adjacent blobs, and
// applies a full search on the classifier results to find the best classified
// segmentation. As a compromise to obtain better recall, 1-1 ambiguity
// substitutions ARE used.
bool Tesseract::FindSegmentation(const GenericVector<UNICHAR_ID>& target_text,
WERD_RES* word_res) {
// Classify all required combinations of blobs and save results in choices.
int word_length = word_res->box_word->length();
GenericVector<BLOB_CHOICE_LIST*>* choices =
new GenericVector<BLOB_CHOICE_LIST*>[word_length];
for (int i = 0; i < word_length; ++i) {
for (int j = 1; j <= kMaxGroupSize && i + j <= word_length; ++j) {
BLOB_CHOICE_LIST* match_result = classify_piece(
word_res->seam_array, i, i + j - 1, "Applybox",
word_res->chopped_word, word_res->blamer_bundle);
if (applybox_debug > 2) {
tprintf("%d+%d:", i, j);
print_ratings_list("Segment:", match_result, unicharset);
}
choices[i].push_back(match_result);
}
}
// Search the segmentation graph for the target text. Must be an exact
// match. Using wildcards makes it difficult to find the correct
// segmentation even when it is there.
word_res->best_state.clear();
GenericVector<int> search_segmentation;
float best_rating = 0.0f;
SearchForText(choices, 0, word_length, target_text, 0, 0.0f,
&search_segmentation, &best_rating, &word_res->best_state);
for (int i = 0; i < word_length; ++i)
choices[i].delete_data_pointers();
delete [] choices;
if (word_res->best_state.empty()) {
// Build the original segmentation and if it is the same length as the
// truth, assume it will do.
int blob_count = 1;
for (int s = 0; s < word_res->seam_array.size(); ++s) {
SEAM* seam = word_res->seam_array[s];
if (!seam->HasAnySplits()) {
word_res->best_state.push_back(blob_count);
blob_count = 1;
} else {
++blob_count;
}
}
word_res->best_state.push_back(blob_count);
if (word_res->best_state.size() != target_text.size()) {
word_res->best_state.clear(); // No good. Original segmentation bad size.
return false;
}
}
word_res->correct_text.clear();
for (int i = 0; i < target_text.size(); ++i) {
word_res->correct_text.push_back(
STRING(unicharset.id_to_unichar(target_text[i])));
}
return true;
}
// Recursive helper to find a match to the target_text (from text_index
// position) in the choices (from choices_pos position).
// Choices is an array of GenericVectors, of length choices_length, with each
// element representing a starting position in the word, and the
// GenericVector holding classification results for a sequence of consecutive
// blobs, with index 0 being a single blob, index 1 being 2 blobs etc.
void Tesseract::SearchForText(const GenericVector<BLOB_CHOICE_LIST*>* choices,
int choices_pos, int choices_length,
const GenericVector<UNICHAR_ID>& target_text,
int text_index,
float rating, GenericVector<int>* segmentation,
float* best_rating,
GenericVector<int>* best_segmentation) {
const UnicharAmbigsVector& table = getDict().getUnicharAmbigs().dang_ambigs();
for (int length = 1; length <= choices[choices_pos].size(); ++length) {
// Rating of matching choice or worst choice if no match.
float choice_rating = 0.0f;
// Find the corresponding best BLOB_CHOICE.
BLOB_CHOICE_IT choice_it(choices[choices_pos][length - 1]);
for (choice_it.mark_cycle_pt(); !choice_it.cycled_list();
choice_it.forward()) {
BLOB_CHOICE* choice = choice_it.data();
choice_rating = choice->rating();
UNICHAR_ID class_id = choice->unichar_id();
if (class_id == target_text[text_index]) {
break;
}
// Search ambigs table.
if (class_id < table.size() && table[class_id] != NULL) {
AmbigSpec_IT spec_it(table[class_id]);
for (spec_it.mark_cycle_pt(); !spec_it.cycled_list();
spec_it.forward()) {
const AmbigSpec *ambig_spec = spec_it.data();
// We'll only do 1-1.
if (ambig_spec->wrong_ngram[1] == INVALID_UNICHAR_ID &&
ambig_spec->correct_ngram_id == target_text[text_index])
break;
}
if (!spec_it.cycled_list())
break; // Found an ambig.
}
}
if (choice_it.cycled_list())
continue; // No match.
segmentation->push_back(length);
if (choices_pos + length == choices_length &&
text_index + 1 == target_text.size()) {
// This is a complete match. If the rating is good record a new best.
if (applybox_debug > 2) {
tprintf("Complete match, rating = %g, best=%g, seglength=%d, best=%d\n",
rating + choice_rating, *best_rating, segmentation->size(),
best_segmentation->size());
}
if (best_segmentation->empty() || rating + choice_rating < *best_rating) {
*best_segmentation = *segmentation;
*best_rating = rating + choice_rating;
}
} else if (choices_pos + length < choices_length &&
text_index + 1 < target_text.size()) {
if (applybox_debug > 3) {
tprintf("Match found for %d=%s:%s, at %d+%d, recursing...\n",
target_text[text_index],
unicharset.id_to_unichar(target_text[text_index]),
choice_it.data()->unichar_id() == target_text[text_index]
? "Match" : "Ambig",
choices_pos, length);
}
SearchForText(choices, choices_pos + length, choices_length, target_text,
text_index + 1, rating + choice_rating, segmentation,
best_rating, best_segmentation);
if (applybox_debug > 3) {
tprintf("End recursion for %d=%s\n", target_text[text_index],
unicharset.id_to_unichar(target_text[text_index]));
}
}
segmentation->truncate(segmentation->size() - 1);
}
}
// Counts up the labelled words and the blobs within.
// Deletes all unused or emptied words, counting the unused ones.
// Resets W_BOL and W_EOL flags correctly.
// Builds the rebuild_word and rebuilds the box_word and the best_choice.
void Tesseract::TidyUp(PAGE_RES* page_res) {
int ok_blob_count = 0;
int bad_blob_count = 0;
int ok_word_count = 0;
int unlabelled_words = 0;
PAGE_RES_IT pr_it(page_res);
WERD_RES* word_res;
for (; (word_res = pr_it.word()) != NULL; pr_it.forward()) {
int ok_in_word = 0;
int blob_count = word_res->correct_text.size();
WERD_CHOICE* word_choice = new WERD_CHOICE(word_res->uch_set, blob_count);
word_choice->set_permuter(TOP_CHOICE_PERM);
for (int c = 0; c < blob_count; ++c) {
if (word_res->correct_text[c].length() > 0) {
++ok_in_word;
}
// Since we only need a fake word_res->best_choice, the actual
// unichar_ids do not matter. Which is fortunate, since TidyUp()
// can be called while training Tesseract, at the stage where
// unicharset is not meaningful yet.
word_choice->append_unichar_id_space_allocated(
INVALID_UNICHAR_ID, word_res->best_state[c], 1.0f, -1.0f);
}
if (ok_in_word > 0) {
ok_blob_count += ok_in_word;
bad_blob_count += word_res->correct_text.size() - ok_in_word;
word_res->LogNewRawChoice(word_choice);
word_res->LogNewCookedChoice(1, false, word_choice);
} else {
++unlabelled_words;
if (applybox_debug > 0) {
tprintf("APPLY_BOXES: Unlabelled word at :");
word_res->word->bounding_box().print();
}
pr_it.DeleteCurrentWord();
delete word_choice;
}
}
pr_it.restart_page();
for (; (word_res = pr_it.word()) != NULL; pr_it.forward()) {
// Denormalize back to a BoxWord.
word_res->RebuildBestState();
word_res->SetupBoxWord();
word_res->word->set_flag(W_BOL, pr_it.prev_row() != pr_it.row());
word_res->word->set_flag(W_EOL, pr_it.next_row() != pr_it.row());
}
if (applybox_debug > 0) {
tprintf(" Found %d good blobs.\n", ok_blob_count);
if (bad_blob_count > 0) {
tprintf(" Leaving %d unlabelled blobs in %d words.\n",
bad_blob_count, ok_word_count);
}
if (unlabelled_words > 0)
tprintf(" %d remaining unlabelled words deleted.\n", unlabelled_words);
}
}
// Logs a bad box by line in the box file and box coords.
void Tesseract::ReportFailedBox(int boxfile_lineno, TBOX box,
const char *box_ch, const char *err_msg) {
tprintf("APPLY_BOXES: boxfile line %d/%s ((%d,%d),(%d,%d)): %s\n",
boxfile_lineno + 1, box_ch,
box.left(), box.bottom(), box.right(), box.top(), err_msg);
}
// Creates a fake best_choice entry in each WERD_RES with the correct text.
void Tesseract::CorrectClassifyWords(PAGE_RES* page_res) {
PAGE_RES_IT pr_it(page_res);
for (WERD_RES *word_res = pr_it.word(); word_res != NULL;
word_res = pr_it.forward()) {
WERD_CHOICE* choice = new WERD_CHOICE(word_res->uch_set,
word_res->correct_text.size());
for (int i = 0; i < word_res->correct_text.size(); ++i) {
// The part before the first space is the real ground truth, and the
// rest is the bounding box location and page number.
GenericVector<STRING> tokens;
word_res->correct_text[i].split(' ', &tokens);
UNICHAR_ID char_id = unicharset.unichar_to_id(tokens[0].string());
choice->append_unichar_id_space_allocated(char_id,
word_res->best_state[i],
0.0f, 0.0f);
}
word_res->ClearWordChoices();
word_res->LogNewRawChoice(choice);
word_res->LogNewCookedChoice(1, false, choice);
}
}
// Calls LearnWord to extract features for labelled blobs within each word.
// Features are stored in an internal buffer.
void Tesseract::ApplyBoxTraining(const STRING& fontname, PAGE_RES* page_res) {
PAGE_RES_IT pr_it(page_res);
int word_count = 0;
for (WERD_RES *word_res = pr_it.word(); word_res != NULL;
word_res = pr_it.forward()) {
LearnWord(fontname.string(), word_res);
++word_count;
}
tprintf("Generated training data for %d words\n", word_count);
}
} // namespace tesseract
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