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tesseract/lstm/lstmrecognizer.cpp

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///////////////////////////////////////////////////////////////////////
// File: lstmrecognizer.cpp
// Description: Top-level line recognizer class for LSTM-based networks.
// Author: Ray Smith
// Created: Thu May 02 10:59:06 PST 2013
//
// (C) Copyright 2013, 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 "lstmrecognizer.h"
#include "allheaders.h"
#include "callcpp.h"
#include "dict.h"
#include "genericheap.h"
#include "helpers.h"
#include "imagedata.h"
#include "input.h"
#include "lstm.h"
#include "normalis.h"
#include "pageres.h"
#include "ratngs.h"
#include "recodebeam.h"
#include "scrollview.h"
#include "shapetable.h"
#include "statistc.h"
#include "tprintf.h"
namespace tesseract {
// Max number of blob choices to return in any given position.
const int kMaxChoices = 4;
// Default ratio between dict and non-dict words.
const double kDictRatio = 2.25;
// Default certainty offset to give the dictionary a chance.
const double kCertOffset = -0.085;
LSTMRecognizer::LSTMRecognizer()
: network_(NULL),
training_flags_(0),
training_iteration_(0),
sample_iteration_(0),
null_char_(UNICHAR_BROKEN),
weight_range_(0.0f),
learning_rate_(0.0f),
momentum_(0.0f),
dict_(NULL),
search_(NULL),
debug_win_(NULL) {}
LSTMRecognizer::~LSTMRecognizer() {
delete network_;
delete dict_;
delete search_;
}
// Writes to the given file. Returns false in case of error.
bool LSTMRecognizer::Serialize(TFile* fp) const {
if (!network_->Serialize(fp)) return false;
if (!GetUnicharset().save_to_file(fp)) return false;
if (!network_str_.Serialize(fp)) return false;
if (fp->FWrite(&training_flags_, sizeof(training_flags_), 1) != 1)
return false;
if (fp->FWrite(&training_iteration_, sizeof(training_iteration_), 1) != 1)
return false;
if (fp->FWrite(&sample_iteration_, sizeof(sample_iteration_), 1) != 1)
return false;
if (fp->FWrite(&null_char_, sizeof(null_char_), 1) != 1) return false;
if (fp->FWrite(&weight_range_, sizeof(weight_range_), 1) != 1) return false;
if (fp->FWrite(&learning_rate_, sizeof(learning_rate_), 1) != 1) return false;
if (fp->FWrite(&momentum_, sizeof(momentum_), 1) != 1) return false;
if (IsRecoding() && !recoder_.Serialize(fp)) return false;
return true;
}
// Reads from the given file. Returns false in case of error.
// If swap is true, assumes a big/little-endian swap is needed.
bool LSTMRecognizer::DeSerialize(bool swap, TFile* fp) {
delete network_;
network_ = Network::CreateFromFile(swap, fp);
if (network_ == NULL) return false;
if (!ccutil_.unicharset.load_from_file(fp, false)) return false;
if (!network_str_.DeSerialize(swap, fp)) return false;
if (fp->FRead(&training_flags_, sizeof(training_flags_), 1) != 1)
return false;
if (fp->FRead(&training_iteration_, sizeof(training_iteration_), 1) != 1)
return false;
if (fp->FRead(&sample_iteration_, sizeof(sample_iteration_), 1) != 1)
return false;
if (fp->FRead(&null_char_, sizeof(null_char_), 1) != 1) return false;
if (fp->FRead(&weight_range_, sizeof(weight_range_), 1) != 1) return false;
if (fp->FRead(&learning_rate_, sizeof(learning_rate_), 1) != 1) return false;
if (fp->FRead(&momentum_, sizeof(momentum_), 1) != 1) return false;
if (IsRecoding()) {
if (!recoder_.DeSerialize(swap, fp)) return false;
RecodedCharID code;
recoder_.EncodeUnichar(UNICHAR_SPACE, &code);
if (code(0) != UNICHAR_SPACE) {
tprintf("Space was garbled in recoding!!\n");
return false;
}
}
// TODO(rays) swaps!
network_->SetRandomizer(&randomizer_);
network_->CacheXScaleFactor(network_->XScaleFactor());
return true;
}
// Loads the dictionary if possible from the traineddata file.
// Prints a warning message, and returns false but otherwise fails silently
// and continues to work without it if loading fails.
// Note that dictionary load is independent from DeSerialize, but dependent
// on the unicharset matching. This enables training to deserialize a model
// from checkpoint or restore without having to go back and reload the
// dictionary.
bool LSTMRecognizer::LoadDictionary(const char* data_file_name,
const char* lang) {
delete dict_;
dict_ = new Dict(&ccutil_);
dict_->SetupForLoad(Dict::GlobalDawgCache());
dict_->LoadLSTM(data_file_name, lang);
if (dict_->FinishLoad()) return true; // Success.
tprintf("Failed to load any lstm-specific dictionaries for lang %s!!\n",
lang);
delete dict_;
dict_ = NULL;
return false;
}
// Recognizes the line image, contained within image_data, returning the
// ratings matrix and matching box_word for each WERD_RES in the output.
void LSTMRecognizer::RecognizeLine(const ImageData& image_data, bool invert,
bool debug, double worst_dict_cert,
bool use_alternates,
const UNICHARSET* target_unicharset,
const TBOX& line_box, float score_ratio,
bool one_word,
PointerVector<WERD_RES>* words) {
NetworkIO outputs;
float label_threshold = use_alternates ? 0.75f : 0.0f;
float scale_factor;
NetworkIO inputs;
if (!RecognizeLine(image_data, invert, debug, false, label_threshold,
&scale_factor, &inputs, &outputs))
return;
if (IsRecoding()) {
if (search_ == NULL) {
search_ =
new RecodeBeamSearch(recoder_, null_char_, SimpleTextOutput(), dict_);
}
search_->Decode(outputs, kDictRatio, kCertOffset, worst_dict_cert, NULL);
search_->ExtractBestPathAsWords(line_box, scale_factor, debug,
&GetUnicharset(), words);
} else {
GenericVector<int> label_coords;
GenericVector<int> labels;
LabelsFromOutputs(outputs, label_threshold, &labels, &label_coords);
WordsFromOutputs(outputs, labels, label_coords, line_box, debug,
use_alternates, one_word, score_ratio, scale_factor,
target_unicharset, words);
}
}
// Builds a set of tesseract-compatible WERD_RESs aligned to line_box,
// corresponding to the network output in outputs, labels, label_coords.
// one_word generates a single word output, that may include spaces inside.
// use_alternates generates alternative BLOB_CHOICEs and segmentation paths.
// If not NULL, we attempt to translate the output to target_unicharset, but do
// not guarantee success, due to mismatches. In that case the output words are
// marked with our UNICHARSET, not the caller's.
void LSTMRecognizer::WordsFromOutputs(
const NetworkIO& outputs, const GenericVector<int>& labels,
const GenericVector<int> label_coords, const TBOX& line_box, bool debug,
bool use_alternates, bool one_word, float score_ratio, float scale_factor,
const UNICHARSET* target_unicharset, PointerVector<WERD_RES>* words) {
// Convert labels to unichar-ids.
int word_end = 0;
float prev_space_cert = 0.0f;
for (int i = 0; i < labels.size(); i = word_end) {
word_end = i + 1;
if (labels[i] == null_char_ || labels[i] == UNICHAR_SPACE) {
continue;
}
float space_cert = 0.0f;
if (one_word) {
word_end = labels.size();
} else {
// Find the end of the word at the first null_char_ that leads to the
// first UNICHAR_SPACE.
while (word_end < labels.size() && labels[word_end] != UNICHAR_SPACE)
++word_end;
if (word_end < labels.size()) {
float rating;
outputs.ScoresOverRange(label_coords[word_end],
label_coords[word_end] + 1, UNICHAR_SPACE,
null_char_, &rating, &space_cert);
}
while (word_end > i && labels[word_end - 1] == null_char_) --word_end;
}
ASSERT_HOST(word_end > i);
// Create a WERD_RES for the output word.
if (debug)
tprintf("Creating word from outputs over [%d,%d)\n", i, word_end);
WERD_RES* word =
WordFromOutput(line_box, outputs, i, word_end, score_ratio,
MIN(prev_space_cert, space_cert), debug,
use_alternates && !SimpleTextOutput(), target_unicharset,
labels, label_coords, scale_factor);
if (word == NULL && target_unicharset != NULL) {
// Unicharset translation failed - use decoder_ instead, and disable
// the segmentation search on output, as it won't understand the encoding.
word = WordFromOutput(line_box, outputs, i, word_end, score_ratio,
MIN(prev_space_cert, space_cert), debug, false,
NULL, labels, label_coords, scale_factor);
}
prev_space_cert = space_cert;
words->push_back(word);
}
}
// Helper computes min and mean best results in the output.
void LSTMRecognizer::OutputStats(const NetworkIO& outputs, float* min_output,
float* mean_output, float* sd) {
const int kOutputScale = MAX_INT8;
STATS stats(0, kOutputScale + 1);
for (int t = 0; t < outputs.Width(); ++t) {
int best_label = outputs.BestLabel(t, NULL);
if (best_label != null_char_ || t == 0) {
float best_output = outputs.f(t)[best_label];
stats.add(static_cast<int>(kOutputScale * best_output), 1);
}
}
*min_output = static_cast<float>(stats.min_bucket()) / kOutputScale;
*mean_output = stats.mean() / kOutputScale;
*sd = stats.sd() / kOutputScale;
}
// Recognizes the image_data, returning the labels,
// scores, and corresponding pairs of start, end x-coords in coords.
// If label_threshold is positive, uses it for making the labels, otherwise
// uses standard ctc.
bool LSTMRecognizer::RecognizeLine(const ImageData& image_data, bool invert,
bool debug, bool re_invert,
float label_threshold, float* scale_factor,
NetworkIO* inputs, NetworkIO* outputs) {
// Maximum width of image to train on.
const int kMaxImageWidth = 2560;
// This ensures consistent recognition results.
SetRandomSeed();
int min_width = network_->XScaleFactor();
Pix* pix = Input::PrepareLSTMInputs(image_data, network_, min_width,
&randomizer_, scale_factor);
if (pix == NULL) {
tprintf("Line cannot be recognized!!\n");
return false;
}
if (network_->IsTraining() && pixGetWidth(pix) > kMaxImageWidth) {
tprintf("Image too large to learn!! Size = %dx%d\n", pixGetWidth(pix),
pixGetHeight(pix));
pixDestroy(&pix);
return false;
}
// Reduction factor from image to coords.
*scale_factor = min_width / *scale_factor;
inputs->set_int_mode(IsIntMode());
SetRandomSeed();
Input::PreparePixInput(network_->InputShape(), pix, &randomizer_, inputs);
network_->Forward(debug, *inputs, NULL, &scratch_space_, outputs);
// Check for auto inversion.
float pos_min, pos_mean, pos_sd;
OutputStats(*outputs, &pos_min, &pos_mean, &pos_sd);
if (invert && pos_min < 0.5) {
// Run again inverted and see if it is any better.
NetworkIO inv_inputs, inv_outputs;
inv_inputs.set_int_mode(IsIntMode());
SetRandomSeed();
pixInvert(pix, pix);
Input::PreparePixInput(network_->InputShape(), pix, &randomizer_,
&inv_inputs);
network_->Forward(debug, inv_inputs, NULL, &scratch_space_, &inv_outputs);
float inv_min, inv_mean, inv_sd;
OutputStats(inv_outputs, &inv_min, &inv_mean, &inv_sd);
if (inv_min > pos_min && inv_mean > pos_mean && inv_sd < pos_sd) {
// Inverted did better. Use inverted data.
if (debug) {
tprintf("Inverting image: old min=%g, mean=%g, sd=%g, inv %g,%g,%g\n",
pos_min, pos_mean, pos_sd, inv_min, inv_mean, inv_sd);
}
*outputs = inv_outputs;
*inputs = inv_inputs;
} else if (re_invert) {
// Inverting was not an improvement, so undo and run again, so the
// outputs match the best forward result.
SetRandomSeed();
network_->Forward(debug, *inputs, NULL, &scratch_space_, outputs);
}
}
pixDestroy(&pix);
if (debug) {
GenericVector<int> labels, coords;
LabelsFromOutputs(*outputs, label_threshold, &labels, &coords);
DisplayForward(*inputs, labels, coords, "LSTMForward", &debug_win_);
DebugActivationPath(*outputs, labels, coords);
}
return true;
}
// Returns a tesseract-compatible WERD_RES from the line recognizer outputs.
// line_box should be the bounding box of the line image in the main image,
// outputs the output of the network,
// [word_start, word_end) the interval over which to convert,
// score_ratio for choosing alternate classifier choices,
// use_alternates to control generation of alternative segmentations,
// labels, label_coords, scale_factor from RecognizeLine above.
// If target_unicharset is not NULL, attempts to translate the internal
// unichar_ids to the target_unicharset, but falls back to untranslated ids
// if the translation should fail.
WERD_RES* LSTMRecognizer::WordFromOutput(
const TBOX& line_box, const NetworkIO& outputs, int word_start,
int word_end, float score_ratio, float space_certainty, bool debug,
bool use_alternates, const UNICHARSET* target_unicharset,
const GenericVector<int>& labels, const GenericVector<int>& label_coords,
float scale_factor) {
WERD_RES* word_res = InitializeWord(
line_box, word_start, word_end, space_certainty, use_alternates,
target_unicharset, labels, label_coords, scale_factor);
int max_blob_run = word_res->ratings->bandwidth();
for (int width = 1; width <= max_blob_run; ++width) {
int col = 0;
for (int i = word_start; i + width <= word_end; ++i) {
if (labels[i] != null_char_) {
// Starting at i, use width labels, but stop at the next null_char_.
// This forms all combinations of blobs between regions of null_char_.
int j = i + 1;
while (j - i < width && labels[j] != null_char_) ++j;
if (j - i == width) {
// Make the blob choices.
int end_coord = label_coords[j];
if (j < word_end && labels[j] == null_char_)
end_coord = label_coords[j + 1];
BLOB_CHOICE_LIST* choices = GetBlobChoices(
col, col + width - 1, debug, outputs, target_unicharset,
label_coords[i], end_coord, score_ratio);
if (choices == NULL) {
delete word_res;
return NULL;
}
word_res->ratings->put(col, col + width - 1, choices);
}
++col;
}
}
}
if (use_alternates) {
// Merge adjacent single results over null_char boundaries.
int col = 0;
for (int i = word_start; i + 2 < word_end; ++i) {
if (labels[i] != null_char_ && labels[i + 1] == null_char_ &&
labels[i + 2] != null_char_ &&
(i == word_start || labels[i - 1] == null_char_) &&
(i + 3 == word_end || labels[i + 3] == null_char_)) {
int end_coord = label_coords[i + 3];
if (i + 3 < word_end && labels[i + 3] == null_char_)
end_coord = label_coords[i + 4];
BLOB_CHOICE_LIST* choices =
GetBlobChoices(col, col + 1, debug, outputs, target_unicharset,
label_coords[i], end_coord, score_ratio);
if (choices == NULL) {
delete word_res;
return NULL;
}
word_res->ratings->put(col, col + 1, choices);
}
if (labels[i] != null_char_) ++col;
}
} else {
word_res->FakeWordFromRatings(TOP_CHOICE_PERM);
}
return word_res;
}
// Sets up a word with the ratings matrix and fake blobs with boxes in the
// right places.
WERD_RES* LSTMRecognizer::InitializeWord(const TBOX& line_box, int word_start,
int word_end, float space_certainty,
bool use_alternates,
const UNICHARSET* target_unicharset,
const GenericVector<int>& labels,
const GenericVector<int>& label_coords,
float scale_factor) {
// Make a fake blob for each non-zero label.
C_BLOB_LIST blobs;
C_BLOB_IT b_it(&blobs);
// num_blobs is the length of the diagonal of the ratings matrix.
int num_blobs = 0;
// max_blob_run is the diagonal width of the ratings matrix
int max_blob_run = 0;
int blob_run = 0;
for (int i = word_start; i < word_end; ++i) {
if (IsRecoding() && !recoder_.IsValidFirstCode(labels[i])) continue;
if (labels[i] != null_char_) {
// Make a fake blob.
TBOX box(label_coords[i], 0, label_coords[i + 1], 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));
++num_blobs;
++blob_run;
}
if (labels[i] == null_char_ || i + 1 == word_end) {
if (blob_run > max_blob_run)
max_blob_run = blob_run;
}
}
if (!use_alternates) max_blob_run = 1;
ASSERT_HOST(label_coords.size() >= word_end);
// Make a fake word from the blobs.
WERD* word = new WERD(&blobs, word_start > 1 ? 1 : 0, NULL);
// Make a WERD_RES from the word.
WERD_RES* word_res = new WERD_RES(word);
word_res->uch_set =
target_unicharset != NULL ? target_unicharset : &GetUnicharset();
word_res->combination = true; // Give it ownership of the word.
word_res->space_certainty = space_certainty;
word_res->ratings = new MATRIX(num_blobs, max_blob_run);
return word_res;
}
// Converts an array of labels to utf-8, whether or not the labels are
// augmented with character boundaries.
STRING LSTMRecognizer::DecodeLabels(const GenericVector<int>& labels) {
STRING result;
int end = 1;
for (int start = 0; start < labels.size(); start = end) {
if (labels[start] == null_char_) {
end = start + 1;
} else {
result += DecodeLabel(labels, start, &end, NULL);
}
}
return result;
}
// Displays the forward results in a window with the characters and
// boundaries as determined by the labels and label_coords.
void LSTMRecognizer::DisplayForward(const NetworkIO& inputs,
const GenericVector<int>& labels,
const GenericVector<int>& label_coords,
const char* window_name,
ScrollView** window) {
#ifndef GRAPHICS_DISABLED // do nothing if there's no graphics
Pix* input_pix = inputs.ToPix();
Network::ClearWindow(false, window_name, pixGetWidth(input_pix),
pixGetHeight(input_pix), window);
int line_height = Network::DisplayImage(input_pix, *window);
DisplayLSTMOutput(labels, label_coords, line_height, *window);
#endif // GRAPHICS_DISABLED
}
// Displays the labels and cuts at the corresponding xcoords.
// Size of labels should match xcoords.
void LSTMRecognizer::DisplayLSTMOutput(const GenericVector<int>& labels,
const GenericVector<int>& xcoords,
int height, ScrollView* window) {
#ifndef GRAPHICS_DISABLED // do nothing if there's no graphics
int x_scale = network_->XScaleFactor();
window->TextAttributes("Arial", height / 4, false, false, false);
int end = 1;
for (int start = 0; start < labels.size(); start = end) {
int xpos = xcoords[start] * x_scale;
if (labels[start] == null_char_) {
end = start + 1;
window->Pen(ScrollView::RED);
} else {
window->Pen(ScrollView::GREEN);
const char* str = DecodeLabel(labels, start, &end, NULL);
if (*str == '\\') str = "\\\\";
xpos = xcoords[(start + end) / 2] * x_scale;
window->Text(xpos, height, str);
}
window->Line(xpos, 0, xpos, height * 3 / 2);
}
window->Update();
#endif // GRAPHICS_DISABLED
}
// Prints debug output detailing the activation path that is implied by the
// label_coords.
void LSTMRecognizer::DebugActivationPath(const NetworkIO& outputs,
const GenericVector<int>& labels,
const GenericVector<int>& xcoords) {
if (xcoords[0] > 0)
DebugActivationRange(outputs, "<null>", null_char_, 0, xcoords[0]);
int end = 1;
for (int start = 0; start < labels.size(); start = end) {
if (labels[start] == null_char_) {
end = start + 1;
DebugActivationRange(outputs, "<null>", null_char_, xcoords[start],
xcoords[end]);
continue;
} else {
int decoded;
const char* label = DecodeLabel(labels, start, &end, &decoded);
DebugActivationRange(outputs, label, labels[start], xcoords[start],
xcoords[start + 1]);
for (int i = start + 1; i < end; ++i) {
DebugActivationRange(outputs, DecodeSingleLabel(labels[i]), labels[i],
xcoords[i], xcoords[i + 1]);
}
}
}
}
// Prints debug output detailing activations and 2nd choice over a range
// of positions.
void LSTMRecognizer::DebugActivationRange(const NetworkIO& outputs,
const char* label, int best_choice,
int x_start, int x_end) {
tprintf("%s=%d On [%d, %d), scores=", label, best_choice, x_start, x_end);
double max_score = 0.0;
double mean_score = 0.0;
int width = x_end - x_start;
for (int x = x_start; x < x_end; ++x) {
const float* line = outputs.f(x);
double score = line[best_choice] * 100.0;
if (score > max_score) max_score = score;
mean_score += score / width;
int best_c = 0;
double best_score = 0.0;
for (int c = 0; c < outputs.NumFeatures(); ++c) {
if (c != best_choice && line[c] > best_score) {
best_c = c;
best_score = line[c];
}
}
tprintf(" %.3g(%s=%d=%.3g)", score, DecodeSingleLabel(best_c), best_c,
best_score * 100.0);
}
tprintf(", Mean=%g, max=%g\n", mean_score, max_score);
}
// Helper returns true if the null_char is the winner at t, and it beats the
// null_threshold, or the next choice is space, in which case we will use the
// null anyway.
static bool NullIsBest(const NetworkIO& output, float null_thr,
int null_char, int t) {
if (output.f(t)[null_char] >= null_thr) return true;
if (output.BestLabel(t, null_char, null_char, NULL) != UNICHAR_SPACE)
return false;
return output.f(t)[null_char] > output.f(t)[UNICHAR_SPACE];
}
// Converts the network output to a sequence of labels. Outputs labels, scores
// and start xcoords of each char, and each null_char_, with an additional
// final xcoord for the end of the output.
// The conversion method is determined by internal state.
void LSTMRecognizer::LabelsFromOutputs(const NetworkIO& outputs, float null_thr,
GenericVector<int>* labels,
GenericVector<int>* xcoords) {
if (SimpleTextOutput()) {
LabelsViaSimpleText(outputs, labels, xcoords);
} else if (IsRecoding()) {
LabelsViaReEncode(outputs, labels, xcoords);
} else if (null_thr <= 0.0) {
LabelsViaCTC(outputs, labels, xcoords);
} else {
LabelsViaThreshold(outputs, null_thr, labels, xcoords);
}
}
// Converts the network output to a sequence of labels, using a threshold
// on the null_char_ to determine character boundaries. Outputs labels, scores
// and start xcoords of each char, and each null_char_, with an additional
// final xcoord for the end of the output.
// The label output is the one with the highest score in the interval between
// null_chars_.
void LSTMRecognizer::LabelsViaThreshold(const NetworkIO& output,
float null_thr,
GenericVector<int>* labels,
GenericVector<int>* xcoords) {
labels->truncate(0);
xcoords->truncate(0);
int width = output.Width();
int t = 0;
// Skip any initial non-char.
while (t < width && NullIsBest(output, null_thr, null_char_, t)) {
++t;
}
while (t < width) {
ASSERT_HOST(!std::isnan(output.f(t)[null_char_]));
int label = output.BestLabel(t, null_char_, null_char_, NULL);
int char_start = t++;
while (t < width && !NullIsBest(output, null_thr, null_char_, t) &&
label == output.BestLabel(t, null_char_, null_char_, NULL)) {
++t;
}
int char_end = t;
labels->push_back(label);
xcoords->push_back(char_start);
// Find the end of the non-char, and compute its score.
while (t < width && NullIsBest(output, null_thr, null_char_, t)) {
++t;
}
if (t > char_end) {
labels->push_back(null_char_);
xcoords->push_back(char_end);
}
}
xcoords->push_back(width);
}
// Converts the network output to a sequence of labels, with scores and
// start x-coords of the character labels. Retains the null_char_ as the
// end x-coord, where already present, otherwise the start of the next
// character is the end.
// The number of labels, scores, and xcoords is always matched, except that
// there is always an additional xcoord for the last end position.
void LSTMRecognizer::LabelsViaCTC(const NetworkIO& output,
GenericVector<int>* labels,
GenericVector<int>* xcoords) {
labels->truncate(0);
xcoords->truncate(0);
int width = output.Width();
int t = 0;
while (t < width) {
float score = 0.0f;
int label = output.BestLabel(t, &score);
labels->push_back(label);
xcoords->push_back(t);
while (++t < width && output.BestLabel(t, NULL) == label) {
}
}
xcoords->push_back(width);
}
// As LabelsViaCTC except that this function constructs the best path that
// contains only legal sequences of subcodes for CJK.
void LSTMRecognizer::LabelsViaReEncode(const NetworkIO& output,
GenericVector<int>* labels,
GenericVector<int>* xcoords) {
if (search_ == NULL) {
search_ =
new RecodeBeamSearch(recoder_, null_char_, SimpleTextOutput(), dict_);
}
search_->Decode(output, 1.0, 0.0, RecodeBeamSearch::kMinCertainty, NULL);
search_->ExtractBestPathAsLabels(labels, xcoords);
}
// Converts the network output to a sequence of labels, with scores, using
// the simple character model (each position is a char, and the null_char_ is
// mainly intended for tail padding.)
void LSTMRecognizer::LabelsViaSimpleText(const NetworkIO& output,
GenericVector<int>* labels,
GenericVector<int>* xcoords) {
labels->truncate(0);
xcoords->truncate(0);
int width = output.Width();
for (int t = 0; t < width; ++t) {
float score = 0.0f;
int label = output.BestLabel(t, &score);
if (label != null_char_) {
labels->push_back(label);
xcoords->push_back(t);
}
}
xcoords->push_back(width);
}
// Helper returns a BLOB_CHOICE_LIST for the choices in a given x-range.
// Handles either LSTM labels or direct unichar-ids.
// Score ratio determines the worst ratio between top choice and remainder.
// If target_unicharset is not NULL, attempts to translate to the target
// unicharset, returning NULL on failure.
BLOB_CHOICE_LIST* LSTMRecognizer::GetBlobChoices(
int col, int row, bool debug, const NetworkIO& output,
const UNICHARSET* target_unicharset, int x_start, int x_end,
float score_ratio) {
float rating = 0.0f, certainty = 0.0f;
int label = output.BestChoiceOverRange(x_start, x_end, UNICHAR_SPACE,
null_char_, &rating, &certainty);
int unichar_id = label == null_char_ ? UNICHAR_SPACE : label;
if (debug) {
tprintf("Best choice over range %d,%d=unichar%d=%s r = %g, cert=%g\n",
x_start, x_end, unichar_id, DecodeSingleLabel(label), rating,
certainty);
}
BLOB_CHOICE_LIST* choices = new BLOB_CHOICE_LIST;
BLOB_CHOICE_IT bc_it(choices);
if (!AddBlobChoices(unichar_id, rating, certainty, col, row,
target_unicharset, &bc_it)) {
delete choices;
return NULL;
}
// Get the other choices.
double best_cert = certainty;
for (int c = 0; c < output.NumFeatures(); ++c) {
if (c == label || c == UNICHAR_SPACE || c == null_char_) continue;
// Compute the score over the range.
output.ScoresOverRange(x_start, x_end, c, null_char_, &rating, &certainty);
int unichar_id = c == null_char_ ? UNICHAR_SPACE : c;
if (certainty >= best_cert - score_ratio &&
!AddBlobChoices(unichar_id, rating, certainty, col, row,
target_unicharset, &bc_it)) {
delete choices;
return NULL;
}
}
choices->sort(&BLOB_CHOICE::SortByRating);
if (bc_it.length() > kMaxChoices) {
bc_it.move_to_first();
for (int i = 0; i < kMaxChoices; ++i)
bc_it.forward();
while (!bc_it.at_first()) {
delete bc_it.extract();
bc_it.forward();
}
}
return choices;
}
// Adds to the given iterator, the blob choices for the target_unicharset
// that correspond to the given LSTM unichar_id.
// Returns false if unicharset translation failed.
bool LSTMRecognizer::AddBlobChoices(int unichar_id, float rating,
float certainty, int col, int row,
const UNICHARSET* target_unicharset,
BLOB_CHOICE_IT* bc_it) {
int target_id = unichar_id;
if (target_unicharset != NULL) {
const char* utf8 = GetUnicharset().id_to_unichar(unichar_id);
if (target_unicharset->contains_unichar(utf8)) {
target_id = target_unicharset->unichar_to_id(utf8);
} else {
return false;
}
}
BLOB_CHOICE* choice = new BLOB_CHOICE(target_id, rating, certainty, -1, 1.0f,
static_cast<float>(MAX_INT16), 0.0f,
BCC_STATIC_CLASSIFIER);
choice->set_matrix_cell(col, row);
bc_it->add_after_then_move(choice);
return true;
}
// Returns a string corresponding to the label starting at start. Sets *end
// to the next start and if non-null, *decoded to the unichar id.
const char* LSTMRecognizer::DecodeLabel(const GenericVector<int>& labels,
int start, int* end, int* decoded) {
*end = start + 1;
if (IsRecoding()) {
// Decode labels via recoder_.
RecodedCharID code;
if (labels[start] == null_char_) {
if (decoded != NULL) {
code.Set(0, null_char_);
*decoded = recoder_.DecodeUnichar(code);
}
return "<null>";
}
int index = start;
while (index < labels.size() &&
code.length() < RecodedCharID::kMaxCodeLen) {
code.Set(code.length(), labels[index++]);
while (index < labels.size() && labels[index] == null_char_) ++index;
int uni_id = recoder_.DecodeUnichar(code);
// If the next label isn't a valid first code, then we need to continue
// extending even if we have a valid uni_id from this prefix.
if (uni_id != INVALID_UNICHAR_ID &&
(index == labels.size() ||
code.length() == RecodedCharID::kMaxCodeLen ||
recoder_.IsValidFirstCode(labels[index]))) {
*end = index;
if (decoded != NULL) *decoded = uni_id;
if (uni_id == UNICHAR_SPACE) return " ";
return GetUnicharset().get_normed_unichar(uni_id);
}
}
return "<Undecodable>";
} else {
if (decoded != NULL) *decoded = labels[start];
if (labels[start] == null_char_) return "<null>";
if (labels[start] == UNICHAR_SPACE) return " ";
return GetUnicharset().get_normed_unichar(labels[start]);
}
}
// Returns a string corresponding to a given single label id, falling back to
// a default of ".." for part of a multi-label unichar-id.
const char* LSTMRecognizer::DecodeSingleLabel(int label) {
if (label == null_char_) return "<null>";
if (IsRecoding()) {
// Decode label via recoder_.
RecodedCharID code;
code.Set(0, label);
label = recoder_.DecodeUnichar(code);
if (label == INVALID_UNICHAR_ID) return ".."; // Part of a bigger code.
}
if (label == UNICHAR_SPACE) return " ";
return GetUnicharset().get_normed_unichar(label);
}
} // namespace tesseract.
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