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tesseract/lstm/recodebeam.cpp

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///////////////////////////////////////////////////////////////////////
// File: recodebeam.cpp
// Description: Beam search to decode from the re-encoded CJK as a sequence of
// smaller numbers in place of a single large code.
// Author: Ray Smith
// Created: Fri Mar 13 09:39:01 PDT 2015
//
// (C) Copyright 2015, Google Inc.
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
///////////////////////////////////////////////////////////////////////
#include "recodebeam.h"
#include "networkio.h"
#include "pageres.h"
#include "unicharcompress.h"
namespace tesseract {
// Clipping value for certainty inside Tesseract. Reflects the minimum value
// of certainty that will be returned by ExtractBestPathAsUnicharIds.
// Supposedly on a uniform scale that can be compared across languages and
// engines.
const float RecodeBeamSearch::kMinCertainty = -20.0f;
// The beam width at each code position.
const int RecodeBeamSearch::kBeamWidths[RecodedCharID::kMaxCodeLen + 1] = {
5, 10, 16, 16, 16, 16, 16, 16, 16, 16,
};
// Borrows the pointer, which is expected to survive until *this is deleted.
RecodeBeamSearch::RecodeBeamSearch(const UnicharCompress& recoder,
int null_char, bool simple_text, Dict* dict)
: recoder_(recoder),
dict_(dict),
space_delimited_(true),
is_simple_text_(simple_text),
null_char_(null_char) {
if (dict_ != NULL && !dict_->IsSpaceDelimitedLang()) space_delimited_ = false;
}
// Decodes the set of network outputs, storing the lattice internally.
void RecodeBeamSearch::Decode(const NetworkIO& output, double dict_ratio,
double cert_offset, double worst_dict_cert,
const UNICHARSET* charset) {
beam_size_ = 0;
int width = output.Width();
for (int t = 0; t < width; ++t) {
ComputeTopN(output.f(t), output.NumFeatures(), kBeamWidths[0]);
DecodeStep(output.f(t), t, dict_ratio, cert_offset, worst_dict_cert,
charset);
}
}
void RecodeBeamSearch::Decode(const GENERIC_2D_ARRAY<float>& output,
double dict_ratio, double cert_offset,
double worst_dict_cert,
const UNICHARSET* charset) {
beam_size_ = 0;
int width = output.dim1();
for (int t = 0; t < width; ++t) {
ComputeTopN(output[t], output.dim2(), kBeamWidths[0]);
DecodeStep(output[t], t, dict_ratio, cert_offset, worst_dict_cert, charset);
}
}
// Returns the best path as labels/scores/xcoords similar to simple CTC.
void RecodeBeamSearch::ExtractBestPathAsLabels(
GenericVector<int>* labels, GenericVector<int>* xcoords) const {
labels->truncate(0);
xcoords->truncate(0);
GenericVector<const RecodeNode*> best_nodes;
ExtractBestPaths(&best_nodes, NULL);
// Now just run CTC on the best nodes.
int t = 0;
int width = best_nodes.size();
while (t < width) {
int label = best_nodes[t]->code;
if (label != null_char_) {
labels->push_back(label);
xcoords->push_back(t);
}
while (++t < width && !is_simple_text_ && best_nodes[t]->code == label) {
}
}
xcoords->push_back(width);
}
// Returns the best path as unichar-ids/certs/ratings/xcoords skipping
// duplicates, nulls and intermediate parts.
void RecodeBeamSearch::ExtractBestPathAsUnicharIds(
bool debug, const UNICHARSET* unicharset, GenericVector<int>* unichar_ids,
GenericVector<float>* certs, GenericVector<float>* ratings,
GenericVector<int>* xcoords) const {
GenericVector<const RecodeNode*> best_nodes;
ExtractBestPaths(&best_nodes, NULL);
ExtractPathAsUnicharIds(best_nodes, unichar_ids, certs, ratings, xcoords);
if (debug) {
DebugPath(unicharset, best_nodes);
DebugUnicharPath(unicharset, best_nodes, *unichar_ids, *certs, *ratings,
*xcoords);
}
}
// Returns the best path as a set of WERD_RES.
void RecodeBeamSearch::ExtractBestPathAsWords(const TBOX& line_box,
float scale_factor, bool debug,
const UNICHARSET* unicharset,
PointerVector<WERD_RES>* words) {
words->truncate(0);
GenericVector<int> unichar_ids;
GenericVector<float> certs;
GenericVector<float> ratings;
GenericVector<int> xcoords;
GenericVector<const RecodeNode*> best_nodes;
GenericVector<const RecodeNode*> second_nodes;
ExtractBestPaths(&best_nodes, &second_nodes);
if (debug) {
DebugPath(unicharset, best_nodes);
ExtractPathAsUnicharIds(second_nodes, &unichar_ids, &certs, &ratings,
&xcoords);
tprintf("\nSecond choice path:\n");
DebugUnicharPath(unicharset, second_nodes, unichar_ids, certs, ratings,
xcoords);
}
ExtractPathAsUnicharIds(best_nodes, &unichar_ids, &certs, &ratings, &xcoords);
int num_ids = unichar_ids.size();
if (debug) {
DebugUnicharPath(unicharset, best_nodes, unichar_ids, certs, ratings,
xcoords);
}
// Convert labels to unichar-ids.
int word_end = 0;
float prev_space_cert = 0.0f;
for (int word_start = 0; word_start < num_ids; word_start = word_end) {
for (word_end = word_start + 1; word_end < num_ids; ++word_end) {
// A word is terminated when a space character or start_of_word flag is
// hit. We also want to force a separate word for every non
// space-delimited character when not in a dictionary context.
if (unichar_ids[word_end] == UNICHAR_SPACE) break;
int index = xcoords[word_end];
if (best_nodes[index]->start_of_word) break;
if (best_nodes[index]->permuter == TOP_CHOICE_PERM &&
(!unicharset->IsSpaceDelimited(unichar_ids[word_end]) ||
!unicharset->IsSpaceDelimited(unichar_ids[word_end - 1])))
break;
}
float space_cert = 0.0f;
if (word_end < num_ids && unichar_ids[word_end] == UNICHAR_SPACE)
space_cert = certs[word_end];
bool leading_space =
word_start > 0 && unichar_ids[word_start - 1] == UNICHAR_SPACE;
// Create a WERD_RES for the output word.
WERD_RES* word_res = InitializeWord(
leading_space, line_box, word_start, word_end,
MIN(space_cert, prev_space_cert), unicharset, xcoords, scale_factor);
for (int i = word_start; i < word_end; ++i) {
BLOB_CHOICE_LIST* choices = new BLOB_CHOICE_LIST;
BLOB_CHOICE_IT bc_it(choices);
BLOB_CHOICE* choice = new BLOB_CHOICE(
unichar_ids[i], ratings[i], certs[i], -1, 1.0f,
static_cast<float>(MAX_INT16), 0.0f, BCC_STATIC_CLASSIFIER);
int col = i - word_start;
choice->set_matrix_cell(col, col);
bc_it.add_after_then_move(choice);
word_res->ratings->put(col, col, choices);
}
int index = xcoords[word_end - 1];
word_res->FakeWordFromRatings(best_nodes[index]->permuter);
words->push_back(word_res);
prev_space_cert = space_cert;
if (word_end < num_ids && unichar_ids[word_end] == UNICHAR_SPACE)
++word_end;
}
}
// Generates debug output of the content of the beams after a Decode.
void RecodeBeamSearch::DebugBeams(const UNICHARSET& unicharset) const {
for (int p = 0; p < beam_size_; ++p) {
// Print all the best scoring nodes for each unichar found.
tprintf("Position %d: Nondict beam\n", p);
DebugBeamPos(unicharset, beam_[p]->beams_[0]);
tprintf("Position %d: Dict beam\n", p);
DebugBeamPos(unicharset, beam_[p]->dawg_beams_[0]);
}
}
// Generates debug output of the content of a single beam position.
void RecodeBeamSearch::DebugBeamPos(const UNICHARSET& unicharset,
const RecodeHeap& heap) const {
GenericVector<const RecodeNode*> unichar_bests;
unichar_bests.init_to_size(unicharset.size(), NULL);
const RecodeNode* null_best = NULL;
int heap_size = heap.size();
for (int i = 0; i < heap_size; ++i) {
const RecodeNode* node = &heap.get(i).data;
if (node->unichar_id == INVALID_UNICHAR_ID) {
if (null_best == NULL || null_best->score < node->score) null_best = node;
} else {
if (unichar_bests[node->unichar_id] == NULL ||
unichar_bests[node->unichar_id]->score < node->score) {
unichar_bests[node->unichar_id] = node;
}
}
}
for (int u = 0; u < unichar_bests.size(); ++u) {
if (unichar_bests[u] != NULL) {
const RecodeNode& node = *unichar_bests[u];
tprintf("label=%d, uid=%d=%s score=%g, c=%g, s=%d, e=%d, perm=%d\n",
node.code, node.unichar_id,
unicharset.debug_str(node.unichar_id).string(), node.score,
node.certainty, node.start_of_word, node.end_of_word,
node.permuter);
}
}
if (null_best != NULL) {
tprintf("null_char score=%g, c=%g, s=%d, e=%d, perm=%d\n", null_best->score,
null_best->certainty, null_best->start_of_word,
null_best->end_of_word, null_best->permuter);
}
}
// Returns the given best_nodes as unichar-ids/certs/ratings/xcoords skipping
// duplicates, nulls and intermediate parts.
/* static */
void RecodeBeamSearch::ExtractPathAsUnicharIds(
const GenericVector<const RecodeNode*>& best_nodes,
GenericVector<int>* unichar_ids, GenericVector<float>* certs,
GenericVector<float>* ratings, GenericVector<int>* xcoords) {
unichar_ids->truncate(0);
certs->truncate(0);
ratings->truncate(0);
xcoords->truncate(0);
// Backtrack extracting only valid, non-duplicate unichar-ids.
int t = 0;
int width = best_nodes.size();
while (t < width) {
double certainty = 0.0;
double rating = 0.0;
while (t < width && best_nodes[t]->unichar_id == INVALID_UNICHAR_ID) {
double cert = best_nodes[t++]->certainty;
if (cert < certainty) certainty = cert;
rating -= cert;
}
if (t < width) {
int unichar_id = best_nodes[t]->unichar_id;
unichar_ids->push_back(unichar_id);
xcoords->push_back(t);
do {
double cert = best_nodes[t++]->certainty;
// Special-case NO-PERM space to forget the certainty of the previous
// nulls. See long comment in ContinueContext.
if (cert < certainty || (unichar_id == UNICHAR_SPACE &&
best_nodes[t - 1]->permuter == NO_PERM)) {
certainty = cert;
}
rating -= cert;
} while (t < width && best_nodes[t]->duplicate);
certs->push_back(certainty);
ratings->push_back(rating);
} else if (!certs->empty()) {
if (certainty < certs->back()) certs->back() = certainty;
ratings->back() += rating;
}
}
xcoords->push_back(width);
}
// Sets up a word with the ratings matrix and fake blobs with boxes in the
// right places.
WERD_RES* RecodeBeamSearch::InitializeWord(bool leading_space,
const TBOX& line_box, int word_start,
int word_end, float space_certainty,
const UNICHARSET* unicharset,
const GenericVector<int>& xcoords,
float scale_factor) {
// Make a fake blob for each non-zero label.
C_BLOB_LIST blobs;
C_BLOB_IT b_it(&blobs);
for (int i = word_start; i < word_end; ++i) {
int min_half_width = xcoords[i + 1] - xcoords[i];
if (i > 0 && xcoords[i] - xcoords[i - 1] < min_half_width)
min_half_width = xcoords[i] - xcoords[i - 1];
if (min_half_width < 1) min_half_width = 1;
// Make a fake blob.
TBOX box(xcoords[i] - min_half_width, 0, xcoords[i] + min_half_width,
line_box.height());
box.scale(scale_factor);
box.move(ICOORD(line_box.left(), line_box.bottom()));
box.set_top(line_box.top());
b_it.add_after_then_move(C_BLOB::FakeBlob(box));
}
// Make a fake word from the blobs.
WERD* word = new WERD(&blobs, leading_space, NULL);
// Make a WERD_RES from the word.
WERD_RES* word_res = new WERD_RES(word);
word_res->uch_set = unicharset;
word_res->combination = true; // Give it ownership of the word.
word_res->space_certainty = space_certainty;
word_res->ratings = new MATRIX(word_end - word_start, 1);
return word_res;
}
// Fills top_n_flags_ with bools that are true iff the corresponding output
// is one of the top_n.
void RecodeBeamSearch::ComputeTopN(const float* outputs, int num_outputs,
int top_n) {
top_n_flags_.init_to_size(num_outputs, false);
top_heap_.clear();
for (int i = 0; i < num_outputs; ++i) {
if (top_heap_.size() < top_n || outputs[i] > top_heap_.PeekTop().key) {
TopPair entry(outputs[i], i);
top_heap_.Push(&entry);
if (top_heap_.size() > top_n) top_heap_.Pop(&entry);
}
}
while (!top_heap_.empty()) {
TopPair entry;
top_heap_.Pop(&entry);
top_n_flags_[entry.data] = true;
}
}
// Adds the computation for the current time-step to the beam. Call at each
// time-step in sequence from left to right. outputs is the activation vector
// for the current timestep.
void RecodeBeamSearch::DecodeStep(const float* outputs, int t,
double dict_ratio, double cert_offset,
double worst_dict_cert,
const UNICHARSET* charset) {
if (t == beam_.size()) beam_.push_back(new RecodeBeam);
RecodeBeam* step = beam_[t];
beam_size_ = t + 1;
step->Clear();
if (t == 0) {
// The first step can only use singles and initials.
ContinueContext(NULL, 0, outputs, false, true, dict_ratio, cert_offset,
worst_dict_cert, step);
if (dict_ != NULL)
ContinueContext(NULL, 0, outputs, true, true, dict_ratio, cert_offset,
worst_dict_cert, step);
} else {
RecodeBeam* prev = beam_[t - 1];
if (charset != NULL) {
for (int i = prev->dawg_beams_[0].size() - 1; i >= 0; --i) {
GenericVector<const RecodeNode*> path;
ExtractPath(&prev->dawg_beams_[0].get(i).data, &path);
tprintf("Step %d: Dawg beam %d:\n", t, i);
DebugPath(charset, path);
}
}
int total_beam = 0;
// Try true and then false only if the beam is empty. This enables extending
// the context using only the top-n results first, which may have an empty
// intersection with the valid codes, so we fall back to the rest if the
// beam is empty.
for (int flag = 1; flag >= 0 && total_beam == 0; --flag) {
for (int length = 0; length <= RecodedCharID::kMaxCodeLen; ++length) {
// Working backwards through the heaps doesn't guarantee that we see the
// best first, but it comes before a lot of the worst, so it is slightly
// more efficient than going forwards.
for (int i = prev->dawg_beams_[length].size() - 1; i >= 0; --i) {
ContinueContext(&prev->dawg_beams_[length].get(i).data, length,
outputs, true, flag, dict_ratio, cert_offset,
worst_dict_cert, step);
}
for (int i = prev->beams_[length].size() - 1; i >= 0; --i) {
ContinueContext(&prev->beams_[length].get(i).data, length, outputs,
false, flag, dict_ratio, cert_offset, worst_dict_cert,
step);
}
}
for (int length = 0; length <= RecodedCharID::kMaxCodeLen; ++length) {
total_beam += step->beams_[length].size();
total_beam += step->dawg_beams_[length].size();
}
}
// Special case for the best initial dawg. Push it on the heap if good
// enough, but there is only one, so it doesn't blow up the beam.
RecodeHeap* dawg_heap = &step->dawg_beams_[0];
if (step->best_initial_dawg_.code >= 0 &&
(dawg_heap->size() < kBeamWidths[0] ||
step->best_initial_dawg_.score > dawg_heap->PeekTop().data.score)) {
RecodePair entry(step->best_initial_dawg_.score,
step->best_initial_dawg_);
dawg_heap->Push(&entry);
if (dawg_heap->size() > kBeamWidths[0]) dawg_heap->Pop(&entry);
}
}
}
// Adds to the appropriate beams the legal (according to recoder)
// continuations of context prev, which is of the given length, using the
// given network outputs to provide scores to the choices. Uses only those
// choices for which top_n_flags[index] == top_n_flag.
void RecodeBeamSearch::ContinueContext(const RecodeNode* prev, int length,
const float* outputs, bool use_dawgs,
bool top_n_flag, double dict_ratio,
double cert_offset,
double worst_dict_cert,
RecodeBeam* step) {
RecodedCharID prefix;
RecodedCharID full_code;
const RecodeNode* previous = prev;
for (int p = length - 1; p >= 0; --p, previous = previous->prev) {
while (previous != NULL &&
(previous->duplicate || previous->code == null_char_)) {
previous = previous->prev;
}
prefix.Set(p, previous->code);
full_code.Set(p, previous->code);
}
if (prev != NULL && !is_simple_text_) {
float cert = NetworkIO::ProbToCertainty(outputs[prev->code]) + cert_offset;
if ((cert >= kMinCertainty || prev->code == null_char_) &&
top_n_flags_[prev->code] == top_n_flag) {
if (use_dawgs) {
if (cert > worst_dict_cert) {
PushDupIfBetter(kBeamWidths[length], cert, prev,
&step->dawg_beams_[length]);
}
} else {
PushDupIfBetter(kBeamWidths[length], cert * dict_ratio, prev,
&step->beams_[length]);
}
}
if (prev->code != null_char_ && length > 0 &&
top_n_flags_[null_char_] == top_n_flag) {
// Allow nulls within multi code sequences, as the nulls within are not
// explicitly included in the code sequence.
cert = NetworkIO::ProbToCertainty(outputs[null_char_]) + cert_offset;
if (cert >= kMinCertainty && (!use_dawgs || cert > worst_dict_cert)) {
if (use_dawgs) {
PushNoDawgIfBetter(kBeamWidths[length], null_char_,
INVALID_UNICHAR_ID, NO_PERM, cert, prev,
&step->dawg_beams_[length]);
} else {
PushNoDawgIfBetter(kBeamWidths[length], null_char_,
INVALID_UNICHAR_ID, TOP_CHOICE_PERM,
cert * dict_ratio, prev, &step->beams_[length]);
}
}
}
}
const GenericVector<int>* final_codes = recoder_.GetFinalCodes(prefix);
if (final_codes != NULL) {
for (int i = 0; i < final_codes->size(); ++i) {
int code = (*final_codes)[i];
if (top_n_flags_[code] != top_n_flag) continue;
if (prev != NULL && prev->code == code && !is_simple_text_) continue;
float cert = NetworkIO::ProbToCertainty(outputs[code]) + cert_offset;
if (cert < kMinCertainty && code != null_char_) continue;
full_code.Set(length, code);
int unichar_id = recoder_.DecodeUnichar(full_code);
// Map the null char to INVALID.
if (length == 0 && code == null_char_) unichar_id = INVALID_UNICHAR_ID;
if (use_dawgs) {
if (cert > worst_dict_cert) {
ContinueDawg(kBeamWidths[0], code, unichar_id, cert, prev,
&step->dawg_beams_[0], step);
}
} else {
PushNoDawgIfBetter(kBeamWidths[0], code, unichar_id, TOP_CHOICE_PERM,
cert * dict_ratio, prev, &step->beams_[0]);
if (dict_ != NULL &&
((unichar_id == UNICHAR_SPACE && cert > worst_dict_cert) ||
!dict_->getUnicharset().IsSpaceDelimited(unichar_id))) {
// Any top choice position that can start a new word, ie a space or
// any non-space-delimited character, should also be considered
// by the dawg search, so push initial dawg to the dawg heap.
float dawg_cert = cert;
PermuterType permuter = TOP_CHOICE_PERM;
// Since we use the space either side of a dictionary word in the
// certainty of the word, (to properly handle weak spaces) and the
// space is coming from a non-dict word, we need special conditions
// to avoid degrading the certainty of the dict word that follows.
// With a space we don't multiply the certainty by dict_ratio, and we
// flag the space with NO_PERM to indicate that we should not use the
// predecessor nulls to generate the confidence for the space, as they
// have already been multiplied by dict_ratio, and we can't go back to
// insert more entries in any previous heaps.
if (unichar_id == UNICHAR_SPACE)
permuter = NO_PERM;
else
dawg_cert *= dict_ratio;
PushInitialDawgIfBetter(code, unichar_id, permuter, false, false,
dawg_cert, prev, &step->best_initial_dawg_);
}
}
}
}
const GenericVector<int>* next_codes = recoder_.GetNextCodes(prefix);
if (next_codes != NULL) {
for (int i = 0; i < next_codes->size(); ++i) {
int code = (*next_codes)[i];
if (top_n_flags_[code] != top_n_flag) continue;
if (prev != NULL && prev->code == code && !is_simple_text_) continue;
float cert = NetworkIO::ProbToCertainty(outputs[code]) + cert_offset;
if (cert < kMinCertainty && code != null_char_) continue;
if (use_dawgs) {
if (cert > worst_dict_cert) {
ContinueDawg(kBeamWidths[length + 1], code, INVALID_UNICHAR_ID, cert,
prev, &step->dawg_beams_[length + 1], step);
}
} else {
PushNoDawgIfBetter(kBeamWidths[length + 1], code, INVALID_UNICHAR_ID,
TOP_CHOICE_PERM, cert * dict_ratio, prev,
&step->beams_[length + 1]);
}
}
}
}
// Adds a RecodeNode composed of the tuple (code, unichar_id, cert, prev,
// appropriate-dawg-args, cert) to the given heap (dawg_beam_) if unichar_id
// is a valid continuation of whatever is in prev.
void RecodeBeamSearch::ContinueDawg(int max_size, int code, int unichar_id,
float cert, const RecodeNode* prev,
RecodeHeap* heap, RecodeBeam* step) {
if (unichar_id == INVALID_UNICHAR_ID) {
PushNoDawgIfBetter(max_size, code, unichar_id, NO_PERM, cert, prev, heap);
return;
}
// Avoid dictionary probe if score a total loss.
float score = cert;
if (prev != NULL) score += prev->score;
if (heap->size() >= max_size && score <= heap->PeekTop().data.score) return;
const RecodeNode* uni_prev = prev;
// Prev may be a partial code, null_char, or duplicate, so scan back to the
// last valid unichar_id.
while (uni_prev != NULL &&
(uni_prev->unichar_id == INVALID_UNICHAR_ID || uni_prev->duplicate))
uni_prev = uni_prev->prev;
if (unichar_id == UNICHAR_SPACE) {
if (uni_prev != NULL && uni_prev->end_of_word) {
// Space is good. Push initial state, to the dawg beam and a regular
// space to the top choice beam.
PushInitialDawgIfBetter(code, unichar_id, uni_prev->permuter, false,
false, cert, prev, &step->best_initial_dawg_);
PushNoDawgIfBetter(max_size, code, unichar_id, uni_prev->permuter, cert,
prev, &step->beams_[0]);
}
return;
} else if (uni_prev != NULL && uni_prev->start_of_dawg &&
uni_prev->unichar_id != UNICHAR_SPACE &&
dict_->getUnicharset().IsSpaceDelimited(uni_prev->unichar_id) &&
dict_->getUnicharset().IsSpaceDelimited(unichar_id)) {
return; // Can't break words between space delimited chars.
}
DawgPositionVector initial_dawgs;
DawgPositionVector* updated_dawgs = new DawgPositionVector;
DawgArgs dawg_args(&initial_dawgs, updated_dawgs, NO_PERM);
bool word_start = false;
if (uni_prev == NULL) {
// Starting from beginning of line.
dict_->default_dawgs(&initial_dawgs, false);
word_start = true;
} else if (uni_prev->dawgs != NULL) {
// Continuing a previous dict word.
dawg_args.active_dawgs = uni_prev->dawgs;
word_start = uni_prev->start_of_dawg;
} else {
return; // Can't continue if not a dict word.
}
PermuterType permuter = static_cast<PermuterType>(
dict_->def_letter_is_okay(&dawg_args, unichar_id, false));
if (permuter != NO_PERM) {
PushHeapIfBetter(max_size, code, unichar_id, permuter, false, word_start,
dawg_args.valid_end, false, cert, prev,
dawg_args.updated_dawgs, heap);
if (dawg_args.valid_end && !space_delimited_) {
// We can start another word right away, so push initial state as well,
// to the dawg beam, and the regular character to the top choice beam,
// since non-dict words can start here too.
PushInitialDawgIfBetter(code, unichar_id, permuter, word_start, true,
cert, prev, &step->best_initial_dawg_);
PushHeapIfBetter(max_size, code, unichar_id, permuter, false, word_start,
true, false, cert, prev, NULL, &step->beams_[0]);
}
} else {
delete updated_dawgs;
}
}
// Adds a RecodeNode composed of the tuple (code, unichar_id,
// initial-dawg-state, prev, cert) to the given heap if/ there is room or if
// better than the current worst element if already full.
void RecodeBeamSearch::PushInitialDawgIfBetter(int code, int unichar_id,
PermuterType permuter,
bool start, bool end, float cert,
const RecodeNode* prev,
RecodeNode* best_initial_dawg) {
float score = cert;
if (prev != NULL) score += prev->score;
if (best_initial_dawg->code < 0 || score > best_initial_dawg->score) {
DawgPositionVector* initial_dawgs = new DawgPositionVector;
dict_->default_dawgs(initial_dawgs, false);
RecodeNode node(code, unichar_id, permuter, true, start, end, false, cert,
score, prev, initial_dawgs);
*best_initial_dawg = node;
}
}
// Adds a copy of the given prev as a duplicate of and successor to prev, if
// there is room or if better than the current worst element if already full.
/* static */
void RecodeBeamSearch::PushDupIfBetter(int max_size, float cert,
const RecodeNode* prev,
RecodeHeap* heap) {
PushHeapIfBetter(max_size, prev->code, prev->unichar_id, prev->permuter,
false, false, false, true, cert, prev, NULL, heap);
}
// Adds a RecodeNode composed of the tuple (code, unichar_id, permuter,
// false, false, false, false, cert, prev, NULL) to heap if there is room
// or if better than the current worst element if already full.
/* static */
void RecodeBeamSearch::PushNoDawgIfBetter(int max_size, int code,
int unichar_id, PermuterType permuter,
float cert, const RecodeNode* prev,
RecodeHeap* heap) {
float score = cert;
if (prev != NULL) score += prev->score;
if (heap->size() < max_size || score > heap->PeekTop().data.score) {
RecodeNode node(code, unichar_id, permuter, false, false, false, false,
cert, score, prev, NULL);
RecodePair entry(score, node);
heap->Push(&entry);
if (heap->size() > max_size) heap->Pop(&entry);
}
}
// Adds a RecodeNode composed of the tuple (code, unichar_id, permuter,
// dawg_start, word_start, end, dup, cert, prev, d) to heap if there is room
// or if better than the current worst element if already full.
/* static */
void RecodeBeamSearch::PushHeapIfBetter(int max_size, int code, int unichar_id,
PermuterType permuter, bool dawg_start,
bool word_start, bool end, bool dup,
float cert, const RecodeNode* prev,
DawgPositionVector* d,
RecodeHeap* heap) {
float score = cert;
if (prev != NULL) score += prev->score;
if (heap->size() < max_size || score > heap->PeekTop().data.score) {
RecodeNode node(code, unichar_id, permuter, dawg_start, word_start, end,
dup, cert, score, prev, d);
RecodePair entry(score, node);
heap->Push(&entry);
ASSERT_HOST(entry.data.dawgs == NULL);
if (heap->size() > max_size) heap->Pop(&entry);
} else {
delete d;
}
}
// Backtracks to extract the best path through the lattice that was built
// during Decode. On return the best_nodes vector essentially contains the set
// of code, score pairs that make the optimal path with the constraint that
// the recoder can decode the code sequence back to a sequence of unichar-ids.
void RecodeBeamSearch::ExtractBestPaths(
GenericVector<const RecodeNode*>* best_nodes,
GenericVector<const RecodeNode*>* second_nodes) const {
// Scan both beams to extract the best and second best paths.
const RecodeNode* best_node = NULL;
const RecodeNode* second_best_node = NULL;
const RecodeBeam* last_beam = beam_[beam_size_ - 1];
int heap_size = last_beam->beams_[0].size();
for (int i = 0; i < heap_size; ++i) {
const RecodeNode* node = &last_beam->beams_[0].get(i).data;
if (best_node == NULL || node->score > best_node->score) {
second_best_node = best_node;
best_node = node;
} else if (second_best_node == NULL ||
node->score > second_best_node->score) {
second_best_node = node;
}
}
// Scan the entire dawg heap for the best *valid* nodes, if any.
int dawg_size = last_beam->dawg_beams_[0].size();
for (int i = 0; i < dawg_size; ++i) {
const RecodeNode* dawg_node = &last_beam->dawg_beams_[0].get(i).data;
// dawg_node may be a null_char, or duplicate, so scan back to the last
// valid unichar_id.
const RecodeNode* back_dawg_node = dawg_node;
while (back_dawg_node != NULL &&
(back_dawg_node->unichar_id == INVALID_UNICHAR_ID ||
back_dawg_node->duplicate))
back_dawg_node = back_dawg_node->prev;
if (back_dawg_node != NULL &&
(back_dawg_node->end_of_word ||
back_dawg_node->unichar_id == UNICHAR_SPACE)) {
// Dawg node is valid. Use it in preference to back_dawg_node, as the
// score comparison is fair that way.
if (best_node == NULL || dawg_node->score > best_node->score) {
second_best_node = best_node;
best_node = dawg_node;
} else if (second_best_node == NULL ||
dawg_node->score > second_best_node->score) {
second_best_node = dawg_node;
}
}
}
if (second_nodes != NULL) ExtractPath(second_best_node, second_nodes);
ExtractPath(best_node, best_nodes);
}
// Helper backtracks through the lattice from the given node, storing the
// path and reversing it.
void RecodeBeamSearch::ExtractPath(
const RecodeNode* node, GenericVector<const RecodeNode*>* path) const {
path->truncate(0);
while (node != NULL) {
path->push_back(node);
node = node->prev;
}
path->reverse();
}
// Helper prints debug information on the given lattice path.
void RecodeBeamSearch::DebugPath(
const UNICHARSET* unicharset,
const GenericVector<const RecodeNode*>& path) const {
for (int c = 0; c < path.size(); ++c) {
const RecodeNode& node = *path[c];
tprintf("%d %d=%s score=%g, c=%g, s=%d, e=%d, perm=%d\n", c,
node.unichar_id, unicharset->debug_str(node.unichar_id).string(),
node.score, node.certainty, node.start_of_word, node.end_of_word,
node.permuter);
}
}
// Helper prints debug information on the given unichar path.
void RecodeBeamSearch::DebugUnicharPath(
const UNICHARSET* unicharset, const GenericVector<const RecodeNode*>& path,
const GenericVector<int>& unichar_ids, const GenericVector<float>& certs,
const GenericVector<float>& ratings,
const GenericVector<int>& xcoords) const {
int num_ids = unichar_ids.size();
double total_rating = 0.0;
for (int c = 0; c < num_ids; ++c) {
int coord = xcoords[c];
tprintf("%d %d=%s r=%g, c=%g, s=%d, e=%d, perm=%d\n", coord, unichar_ids[c],
unicharset->debug_str(unichar_ids[c]).string(), ratings[c],
certs[c], path[coord]->start_of_word, path[coord]->end_of_word,
path[coord]->permuter);
total_rating += ratings[c];
}
tprintf("Path total rating = %g\n", total_rating);
}
} // namespace tesseract.
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