tesseract/src/ccmain/paragraphs.cpp

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/**********************************************************************
* File: paragraphs.cpp
* Description: Paragraph detection for tesseract.
* Author: David Eger
* Created: 25 February 2011
*
* (C) Copyright 2011, 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 "paragraphs.h"
#include <cctype> // for isspace
#include <cmath> // for abs
#include <cstdio> // for snprintf
#include <cstdlib> // for abs
#include <cstring> // for strchr, strlen
#include <algorithm> // for max
#include <memory> // for unique_ptr
#include "genericvector.h" // for GenericVector, GenericVectorEqEq
#include "helpers.h" // for UpdateRange, ClipToRange
#include "host.h" // for NearlyEqual
#include "mutableiterator.h" // for MutableIterator
#include "ocrblock.h" // for BLOCK
#include "ocrpara.h" // for ParagraphModel, PARA, PARA_IT, PARA...
#include "ocrrow.h" // for ROW
#include "pageiterator.h" // for PageIterator
#include "pageres.h" // for PAGE_RES_IT, WERD_RES, ROW_RES, BLO...
#include "paragraphs_internal.h" // for RowScratchRegisters, SetOfModels
#include "pdblock.h" // for PDBLK
#include "polyblk.h" // for POLY_BLOCK
#include "publictypes.h" // for JUSTIFICATION_LEFT, JUSTIFICATION_R...
#include "ratngs.h" // for WERD_CHOICE
#include "rect.h" // for TBOX
#include "statistc.h" // for STATS
#include "strngs.h" // for STRING
#include "tprintf.h" // for tprintf
#include "unichar.h" // for UNICHAR, UNICHAR_ID
#include "unicharset.h" // for UNICHARSET
#include "unicodes.h" // for kPDF, kRLE
#include "werd.h" // for WERD, W_REP_CHAR
namespace tesseract {
// Special "weak" ParagraphModels.
const ParagraphModel *kCrownLeft
= reinterpret_cast<ParagraphModel *>(0xDEAD111F);
const ParagraphModel *kCrownRight
= reinterpret_cast<ParagraphModel *>(0xDEAD888F);
// Do the text and geometry of two rows support a paragraph break between them?
static bool LikelyParagraphStart(const RowScratchRegisters &before,
const RowScratchRegisters &after,
tesseract::ParagraphJustification j);
// Given the width of a typical space between words, what is the threshold
// by which by which we think left and right alignments for paragraphs
// can vary and still be aligned.
static int Epsilon(int space_pix) {
return space_pix * 4 / 5;
}
static bool AcceptableRowArgs(
int debug_level, int min_num_rows, const char *function_name,
const GenericVector<RowScratchRegisters> *rows,
int row_start, int row_end) {
if (row_start < 0 || row_end > rows->size() || row_start > row_end) {
tprintf("Invalid arguments rows[%d, %d) while rows is of size %d.\n",
row_start, row_end, rows->size());
return false;
}
if (row_end - row_start < min_num_rows) {
if (debug_level > 1) {
tprintf("# Too few rows[%d, %d) for %s.\n",
row_start, row_end, function_name);
}
return false;
}
return true;
}
// =============================== Debug Code ================================
// Convert an integer to a decimal string.
static STRING StrOf(int num) {
char buffer[30];
snprintf(buffer, sizeof(buffer), "%d", num);
return STRING(buffer);
}
// Given a row-major matrix of unicode text and a column separator, print
// a formatted table. For ASCII, we get good column alignment.
static void PrintTable(const GenericVector<GenericVector<STRING> > &rows,
const STRING &colsep) {
GenericVector<int> max_col_widths;
for (int r = 0; r < rows.size(); r++) {
int num_columns = rows[r].size();
for (int c = 0; c < num_columns; c++) {
int num_unicodes = 0;
for (int i = 0; i < rows[r][c].size(); i++) {
if ((rows[r][c][i] & 0xC0) != 0x80) num_unicodes++;
}
if (c >= max_col_widths.size()) {
max_col_widths.push_back(num_unicodes);
} else {
if (num_unicodes > max_col_widths[c])
max_col_widths[c] = num_unicodes;
}
}
}
GenericVector<STRING> col_width_patterns;
for (int c = 0; c < max_col_widths.size(); c++) {
col_width_patterns.push_back(
STRING("%-") + StrOf(max_col_widths[c]) + "s");
}
for (int r = 0; r < rows.size(); r++) {
for (int c = 0; c < rows[r].size(); c++) {
if (c > 0)
tprintf("%s", colsep.string());
tprintf(col_width_patterns[c].string(), rows[r][c].string());
}
tprintf("\n");
}
}
static STRING RtlEmbed(const STRING &word, bool rtlify) {
if (rtlify)
return STRING(kRLE) + word + STRING(kPDF);
return word;
}
// Print the current thoughts of the paragraph detector.
static void PrintDetectorState(const ParagraphTheory &theory,
const GenericVector<RowScratchRegisters> &rows) {
GenericVector<GenericVector<STRING> > output;
output.push_back(GenericVector<STRING>());
output.back().push_back("#row");
output.back().push_back("space");
output.back().push_back("..");
output.back().push_back("lword[widthSEL]");
output.back().push_back("rword[widthSEL]");
RowScratchRegisters::AppendDebugHeaderFields(&output.back());
output.back().push_back("text");
for (int i = 0; i < rows.size(); i++) {
output.push_back(GenericVector<STRING>());
GenericVector<STRING> &row = output.back();
const RowInfo& ri = *rows[i].ri_;
row.push_back(StrOf(i));
row.push_back(StrOf(ri.average_interword_space));
row.push_back(ri.has_leaders ? ".." : " ");
row.push_back(RtlEmbed(ri.lword_text, !ri.ltr) +
"[" + StrOf(ri.lword_box.width()) +
(ri.lword_likely_starts_idea ? "S" : "s") +
(ri.lword_likely_ends_idea ? "E" : "e") +
(ri.lword_indicates_list_item ? "L" : "l") +
"]");
row.push_back(RtlEmbed(ri.rword_text, !ri.ltr) +
"[" + StrOf(ri.rword_box.width()) +
(ri.rword_likely_starts_idea ? "S" : "s") +
(ri.rword_likely_ends_idea ? "E" : "e") +
(ri.rword_indicates_list_item ? "L" : "l") +
"]");
rows[i].AppendDebugInfo(theory, &row);
row.push_back(RtlEmbed(ri.text, !ri.ltr));
}
PrintTable(output, " ");
tprintf("Active Paragraph Models:\n");
for (int m = 0; m < theory.models().size(); m++) {
tprintf(" %d: %s\n", m + 1, theory.models()[m]->ToString().string());
}
}
static void DebugDump(
bool should_print,
const STRING &phase,
const ParagraphTheory &theory,
const GenericVector<RowScratchRegisters> &rows) {
if (!should_print)
return;
tprintf("# %s\n", phase.string());
PrintDetectorState(theory, rows);
}
// Print out the text for rows[row_start, row_end)
static void PrintRowRange(const GenericVector<RowScratchRegisters> &rows,
int row_start, int row_end) {
tprintf("======================================\n");
for (int row = row_start; row < row_end; row++) {
tprintf("%s\n", rows[row].ri_->text.string());
}
tprintf("======================================\n");
}
// ============= Brain Dead Language Model (ASCII Version) ===================
static bool IsLatinLetter(int ch) {
return (ch >= 'a' && ch <= 'z') || (ch >= 'A' && ch <= 'Z');
}
static bool IsDigitLike(int ch) {
return ch == 'o' || ch == 'O' || ch == 'l' || ch == 'I';
}
static bool IsOpeningPunct(int ch) {
return strchr("'\"({[", ch) != nullptr;
}
static bool IsTerminalPunct(int ch) {
return strchr(":'\".?!]})", ch) != nullptr;
}
// Return a pointer after consuming as much text as qualifies as roman numeral.
static const char *SkipChars(const char *str, const char *toskip) {
while (*str != '\0' && strchr(toskip, *str)) { str++; }
return str;
}
static const char *SkipChars(const char *str, bool (*skip)(int)) {
while (*str != '\0' && skip(*str)) { str++; }
return str;
}
static const char *SkipOne(const char *str, const char *toskip) {
if (*str != '\0' && strchr(toskip, *str)) return str + 1;
return str;
}
// Return whether it is very likely that this is a numeral marker that could
// start a list item. Some examples include:
// A I iii. VI (2) 3.5. [C-4]
static bool LikelyListNumeral(const STRING &word) {
const char *kRomans = "ivxlmdIVXLMD";
const char *kDigits = "012345789";
const char *kOpen = "[{(";
const char *kSep = ":;-.,";
const char *kClose = "]})";
int num_segments = 0;
const char *pos = word.string();
while (*pos != '\0' && num_segments < 3) {
// skip up to two open parens.
const char *numeral_start = SkipOne(SkipOne(pos, kOpen), kOpen);
const char *numeral_end = SkipChars(numeral_start, kRomans);
if (numeral_end != numeral_start) {
// Got Roman Numeral. Great.
} else {
numeral_end = SkipChars(numeral_start, kDigits);
if (numeral_end == numeral_start) {
// If there's a single latin letter, we can use that.
numeral_end = SkipChars(numeral_start, IsLatinLetter);
if (numeral_end - numeral_start != 1)
break;
}
}
// We got some sort of numeral.
num_segments++;
// Skip any trailing parens or punctuation.
pos = SkipChars(SkipChars(numeral_end, kClose), kSep);
if (pos == numeral_end)
break;
}
return *pos == '\0';
}
static bool LikelyListMark(const STRING &word) {
const char *kListMarks = "0Oo*.,+.";
return word.size() == 1 && strchr(kListMarks, word[0]) != nullptr;
}
bool AsciiLikelyListItem(const STRING &word) {
return LikelyListMark(word) || LikelyListNumeral(word);
}
// ========== Brain Dead Language Model (Tesseract Version) ================
// Return the first Unicode Codepoint from werd[pos].
int UnicodeFor(const UNICHARSET *u, const WERD_CHOICE *werd, int pos) {
if (!u || !werd || pos > werd->length())
return 0;
return UNICHAR(u->id_to_unichar(werd->unichar_id(pos)), -1).first_uni();
}
// A useful helper class for finding the first j >= i so that word[j]
// does not have given character type.
class UnicodeSpanSkipper {
public:
UnicodeSpanSkipper(const UNICHARSET *unicharset, const WERD_CHOICE *word)
: u_(unicharset), word_(word) { wordlen_ = word->length(); }
// Given an input position, return the first position >= pos not punc.
int SkipPunc(int pos);
// Given an input position, return the first position >= pos not digit.
int SkipDigits(int pos);
// Given an input position, return the first position >= pos not roman.
int SkipRomans(int pos);
// Given an input position, return the first position >= pos not alpha.
int SkipAlpha(int pos);
private:
const UNICHARSET *u_;
const WERD_CHOICE *word_;
int wordlen_;
};
int UnicodeSpanSkipper::SkipPunc(int pos) {
while (pos < wordlen_ && u_->get_ispunctuation(word_->unichar_id(pos))) pos++;
return pos;
}
int UnicodeSpanSkipper::SkipDigits(int pos) {
while (pos < wordlen_ && (u_->get_isdigit(word_->unichar_id(pos)) ||
IsDigitLike(UnicodeFor(u_, word_, pos)))) pos++;
return pos;
}
int UnicodeSpanSkipper::SkipRomans(int pos) {
const char *kRomans = "ivxlmdIVXLMD";
while (pos < wordlen_) {
int ch = UnicodeFor(u_, word_, pos);
if (ch >= 0xF0 || strchr(kRomans, ch) == nullptr) break;
pos++;
}
return pos;
}
int UnicodeSpanSkipper::SkipAlpha(int pos) {
while (pos < wordlen_ && u_->get_isalpha(word_->unichar_id(pos))) pos++;
return pos;
}
static bool LikelyListMarkUnicode(int ch) {
if (ch < 0x80) {
STRING single_ch;
single_ch += ch;
return LikelyListMark(single_ch);
}
switch (ch) {
// TODO(eger) expand this list of unicodes as needed.
case 0x00B0: // degree sign
case 0x2022: // bullet
case 0x25E6: // white bullet
case 0x00B7: // middle dot
case 0x25A1: // white square
case 0x25A0: // black square
case 0x25AA: // black small square
case 0x2B1D: // black very small square
case 0x25BA: // black right-pointing pointer
case 0x25CF: // black circle
case 0x25CB: // white circle
return true;
default:
break; // fall through
}
return false;
}
// Return whether it is very likely that this is a numeral marker that could
// start a list item. Some examples include:
// A I iii. VI (2) 3.5. [C-4]
static bool UniLikelyListItem(const UNICHARSET *u, const WERD_CHOICE *werd) {
if (werd->length() == 1 && LikelyListMarkUnicode(UnicodeFor(u, werd, 0)))
return true;
UnicodeSpanSkipper m(u, werd);
int num_segments = 0;
int pos = 0;
while (pos < werd->length() && num_segments < 3) {
int numeral_start = m.SkipPunc(pos);
if (numeral_start > pos + 1) break;
int numeral_end = m.SkipRomans(numeral_start);
if (numeral_end == numeral_start) {
numeral_end = m.SkipDigits(numeral_start);
if (numeral_end == numeral_start) {
// If there's a single latin letter, we can use that.
numeral_end = m.SkipAlpha(numeral_start);
if (numeral_end - numeral_start != 1)
break;
}
}
// We got some sort of numeral.
num_segments++;
// Skip any trailing punctuation.
pos = m.SkipPunc(numeral_end);
if (pos == numeral_end)
break;
}
return pos == werd->length();
}
// ========= Brain Dead Language Model (combined entry points) ================
// Given the leftmost word of a line either as a Tesseract unicharset + werd
// or a utf8 string, set the following attributes for it:
// is_list - this word might be a list number or bullet.
// starts_idea - this word is likely to start a sentence.
// ends_idea - this word is likely to end a sentence.
void LeftWordAttributes(const UNICHARSET *unicharset, const WERD_CHOICE *werd,
const STRING &utf8,
bool *is_list, bool *starts_idea, bool *ends_idea) {
*is_list = false;
*starts_idea = false;
*ends_idea = false;
if (utf8.size() == 0 || (werd != nullptr && werd->length() == 0)) { // Empty
*ends_idea = true;
return;
}
if (unicharset && werd) { // We have a proper werd and unicharset so use it.
if (UniLikelyListItem(unicharset, werd)) {
*is_list = true;
*starts_idea = true;
*ends_idea = true;
}
if (unicharset->get_isupper(werd->unichar_id(0))) {
*starts_idea = true;
}
if (unicharset->get_ispunctuation(werd->unichar_id(0))) {
*starts_idea = true;
*ends_idea = true;
}
} else { // Assume utf8 is mostly ASCII
if (AsciiLikelyListItem(utf8)) {
*is_list = true;
*starts_idea = true;
}
int start_letter = utf8[0];
if (IsOpeningPunct(start_letter)) {
*starts_idea = true;
}
if (IsTerminalPunct(start_letter)) {
*ends_idea = true;
}
if (start_letter >= 'A' && start_letter <= 'Z') {
*starts_idea = true;
}
}
}
// Given the rightmost word of a line either as a Tesseract unicharset + werd
// or a utf8 string, set the following attributes for it:
// is_list - this word might be a list number or bullet.
// starts_idea - this word is likely to start a sentence.
// ends_idea - this word is likely to end a sentence.
void RightWordAttributes(const UNICHARSET *unicharset, const WERD_CHOICE *werd,
const STRING &utf8,
bool *is_list, bool *starts_idea, bool *ends_idea) {
*is_list = false;
*starts_idea = false;
*ends_idea = false;
if (utf8.size() == 0 || (werd != nullptr && werd->length() == 0)) { // Empty
*ends_idea = true;
return;
}
if (unicharset && werd) { // We have a proper werd and unicharset so use it.
if (UniLikelyListItem(unicharset, werd)) {
*is_list = true;
*starts_idea = true;
}
UNICHAR_ID last_letter = werd->unichar_id(werd->length() - 1);
if (unicharset->get_ispunctuation(last_letter)) {
*ends_idea = true;
}
} else { // Assume utf8 is mostly ASCII
if (AsciiLikelyListItem(utf8)) {
*is_list = true;
*starts_idea = true;
}
int last_letter = utf8[utf8.size() - 1];
if (IsOpeningPunct(last_letter) || IsTerminalPunct(last_letter)) {
*ends_idea = true;
}
}
}
// =============== Implementation of RowScratchRegisters =====================
/* static */
void RowScratchRegisters::AppendDebugHeaderFields(
GenericVector<STRING> *header) {
header->push_back("[lmarg,lind;rind,rmarg]");
header->push_back("model");
}
void RowScratchRegisters::AppendDebugInfo(const ParagraphTheory &theory,
GenericVector<STRING> *dbg) const {
char s[30];
snprintf(s, sizeof(s), "[%3d,%3d;%3d,%3d]",
lmargin_, lindent_, rindent_, rmargin_);
dbg->push_back(s);
STRING model_string;
model_string += static_cast<char>(GetLineType());
model_string += ":";
int model_numbers = 0;
for (int h = 0; h < hypotheses_.size(); h++) {
if (hypotheses_[h].model == nullptr)
continue;
if (model_numbers > 0)
model_string += ",";
if (StrongModel(hypotheses_[h].model)) {
model_string += StrOf(1 + theory.IndexOf(hypotheses_[h].model));
} else if (hypotheses_[h].model == kCrownLeft) {
model_string += "CrL";
} else if (hypotheses_[h].model == kCrownRight) {
model_string += "CrR";
}
model_numbers++;
}
if (model_numbers == 0)
model_string += "0";
dbg->push_back(model_string);
}
void RowScratchRegisters::Init(const RowInfo &row) {
ri_ = &row;
lmargin_ = 0;
lindent_ = row.pix_ldistance;
rmargin_ = 0;
rindent_ = row.pix_rdistance;
}
LineType RowScratchRegisters::GetLineType() const {
if (hypotheses_.empty())
return LT_UNKNOWN;
bool has_start = false;
bool has_body = false;
for (int i = 0; i < hypotheses_.size(); i++) {
switch (hypotheses_[i].ty) {
case LT_START: has_start = true; break;
case LT_BODY: has_body = true; break;
default:
tprintf("Encountered bad value in hypothesis list: %c\n",
hypotheses_[i].ty);
break;
}
}
if (has_start && has_body)
return LT_MULTIPLE;
return has_start ? LT_START : LT_BODY;
}
LineType RowScratchRegisters::GetLineType(const ParagraphModel *model) const {
if (hypotheses_.empty())
return LT_UNKNOWN;
bool has_start = false;
bool has_body = false;
for (int i = 0; i < hypotheses_.size(); i++) {
if (hypotheses_[i].model != model)
continue;
switch (hypotheses_[i].ty) {
case LT_START: has_start = true; break;
case LT_BODY: has_body = true; break;
default:
tprintf("Encountered bad value in hypothesis list: %c\n",
hypotheses_[i].ty);
break;
}
}
if (has_start && has_body)
return LT_MULTIPLE;
return has_start ? LT_START : LT_BODY;
}
void RowScratchRegisters::SetStartLine() {
LineType current_lt = GetLineType();
if (current_lt != LT_UNKNOWN && current_lt != LT_START) {
tprintf("Trying to set a line to be START when it's already BODY.\n");
}
if (current_lt == LT_UNKNOWN || current_lt == LT_BODY) {
hypotheses_.push_back_new(LineHypothesis(LT_START, nullptr));
}
}
void RowScratchRegisters::SetBodyLine() {
LineType current_lt = GetLineType();
if (current_lt != LT_UNKNOWN && current_lt != LT_BODY) {
tprintf("Trying to set a line to be BODY when it's already START.\n");
}
if (current_lt == LT_UNKNOWN || current_lt == LT_START) {
hypotheses_.push_back_new(LineHypothesis(LT_BODY, nullptr));
}
}
void RowScratchRegisters::AddStartLine(const ParagraphModel *model) {
hypotheses_.push_back_new(LineHypothesis(LT_START, model));
int old_idx = hypotheses_.get_index(LineHypothesis(LT_START, nullptr));
if (old_idx >= 0)
hypotheses_.remove(old_idx);
}
void RowScratchRegisters::AddBodyLine(const ParagraphModel *model) {
hypotheses_.push_back_new(LineHypothesis(LT_BODY, model));
int old_idx = hypotheses_.get_index(LineHypothesis(LT_BODY, nullptr));
if (old_idx >= 0)
hypotheses_.remove(old_idx);
}
void RowScratchRegisters::StartHypotheses(SetOfModels *models) const {
for (int h = 0; h < hypotheses_.size(); h++) {
if (hypotheses_[h].ty == LT_START && StrongModel(hypotheses_[h].model))
models->push_back_new(hypotheses_[h].model);
}
}
void RowScratchRegisters::StrongHypotheses(SetOfModels *models) const {
for (int h = 0; h < hypotheses_.size(); h++) {
if (StrongModel(hypotheses_[h].model))
models->push_back_new(hypotheses_[h].model);
}
}
void RowScratchRegisters::NonNullHypotheses(SetOfModels *models) const {
for (int h = 0; h < hypotheses_.size(); h++) {
if (hypotheses_[h].model != nullptr)
models->push_back_new(hypotheses_[h].model);
}
}
const ParagraphModel *RowScratchRegisters::UniqueStartHypothesis() const {
if (hypotheses_.size() != 1 || hypotheses_[0].ty != LT_START)
return nullptr;
return hypotheses_[0].model;
}
const ParagraphModel *RowScratchRegisters::UniqueBodyHypothesis() const {
if (hypotheses_.size() != 1 || hypotheses_[0].ty != LT_BODY)
return nullptr;
return hypotheses_[0].model;
}
// Discard any hypotheses whose model is not in the given list.
void RowScratchRegisters::DiscardNonMatchingHypotheses(
const SetOfModels &models) {
if (models.empty())
return;
for (int h = hypotheses_.size() - 1; h >= 0; h--) {
if (!models.contains(hypotheses_[h].model)) {
hypotheses_.remove(h);
}
}
}
// ============ Geometry based Paragraph Detection Algorithm =================
struct Cluster {
Cluster() : center(0), count(0) {}
Cluster(int cen, int num) : center(cen), count(num) {}
int center; // The center of the cluster.
int count; // The number of entries within the cluster.
};
class SimpleClusterer {
public:
explicit SimpleClusterer(int max_cluster_width)
: max_cluster_width_(max_cluster_width) {}
void Add(int value) { values_.push_back(value); }
int size() const { return values_.size(); }
void GetClusters(GenericVector<Cluster> *clusters);
private:
int max_cluster_width_;
GenericVectorEqEq<int> values_;
};
// Return the index of the cluster closest to value.
static int ClosestCluster(const GenericVector<Cluster> &clusters, int value) {
int best_index = 0;
for (int i = 0; i < clusters.size(); i++) {
if (abs(value - clusters[i].center) <
abs(value - clusters[best_index].center))
best_index = i;
}
return best_index;
}
void SimpleClusterer::GetClusters(GenericVector<Cluster> *clusters) {
clusters->clear();
values_.sort();
for (int i = 0; i < values_.size();) {
int orig_i = i;
int lo = values_[i];
int hi = lo;
while (++i < values_.size() && values_[i] <= lo + max_cluster_width_) {
hi = values_[i];
}
clusters->push_back(Cluster((hi + lo) / 2, i - orig_i));
}
}
// Calculate left- and right-indent tab stop values seen in
// rows[row_start, row_end) given a tolerance of tolerance.
static void CalculateTabStops(GenericVector<RowScratchRegisters> *rows,
int row_start, int row_end, int tolerance,
GenericVector<Cluster> *left_tabs,
GenericVector<Cluster> *right_tabs) {
if (!AcceptableRowArgs(0, 1, __func__, rows, row_start, row_end))
return;
// First pass: toss all left and right indents into clusterers.
SimpleClusterer initial_lefts(tolerance);
SimpleClusterer initial_rights(tolerance);
GenericVector<Cluster> initial_left_tabs;
GenericVector<Cluster> initial_right_tabs;
for (int i = row_start; i < row_end; i++) {
initial_lefts.Add((*rows)[i].lindent_);
initial_rights.Add((*rows)[i].rindent_);
}
initial_lefts.GetClusters(&initial_left_tabs);
initial_rights.GetClusters(&initial_right_tabs);
// Second pass: cluster only lines that are not "stray"
// An example of a stray line is a page number -- a line whose start
// and end tab-stops are far outside the typical start and end tab-stops
// for the block.
// Put another way, we only cluster data from lines whose start or end
// tab stop is frequent.
SimpleClusterer lefts(tolerance);
SimpleClusterer rights(tolerance);
// Outlier elimination. We might want to switch this to test outlier-ness
// based on how strange a position an outlier is in instead of or in addition
// to how rare it is. These outliers get re-added if we end up having too
// few tab stops, to work with, however.
int infrequent_enough_to_ignore = 0;
if (row_end - row_start >= 8) infrequent_enough_to_ignore = 1;
if (row_end - row_start >= 20) infrequent_enough_to_ignore = 2;
for (int i = row_start; i < row_end; i++) {
int lidx = ClosestCluster(initial_left_tabs, (*rows)[i].lindent_);
int ridx = ClosestCluster(initial_right_tabs, (*rows)[i].rindent_);
if (initial_left_tabs[lidx].count > infrequent_enough_to_ignore ||
initial_right_tabs[ridx].count > infrequent_enough_to_ignore) {
lefts.Add((*rows)[i].lindent_);
rights.Add((*rows)[i].rindent_);
}
}
lefts.GetClusters(left_tabs);
rights.GetClusters(right_tabs);
if ((left_tabs->size() == 1 && right_tabs->size() >= 4) ||
(right_tabs->size() == 1 && left_tabs->size() >= 4)) {
// One side is really ragged, and the other only has one tab stop,
// so those "insignificant outliers" are probably important, actually.
// This often happens on a page of an index. Add back in the ones
// we omitted in the first pass.
for (int i = row_start; i < row_end; i++) {
int lidx = ClosestCluster(initial_left_tabs, (*rows)[i].lindent_);
int ridx = ClosestCluster(initial_right_tabs, (*rows)[i].rindent_);
if (!(initial_left_tabs[lidx].count > infrequent_enough_to_ignore ||
initial_right_tabs[ridx].count > infrequent_enough_to_ignore)) {
lefts.Add((*rows)[i].lindent_);
rights.Add((*rows)[i].rindent_);
}
}
}
lefts.GetClusters(left_tabs);
rights.GetClusters(right_tabs);
// If one side is almost a two-indent aligned side, and the other clearly
// isn't, try to prune out the least frequent tab stop from that side.
if (left_tabs->size() == 3 && right_tabs->size() >= 4) {
int to_prune = -1;
for (int i = left_tabs->size() - 1; i >= 0; i--) {
if (to_prune < 0 ||
(*left_tabs)[i].count < (*left_tabs)[to_prune].count) {
to_prune = i;
}
}
if (to_prune >= 0 &&
(*left_tabs)[to_prune].count <= infrequent_enough_to_ignore) {
left_tabs->remove(to_prune);
}
}
if (right_tabs->size() == 3 && left_tabs->size() >= 4) {
int to_prune = -1;
for (int i = right_tabs->size() - 1; i >= 0; i--) {
if (to_prune < 0 ||
(*right_tabs)[i].count < (*right_tabs)[to_prune].count) {
to_prune = i;
}
}
if (to_prune >= 0 &&
(*right_tabs)[to_prune].count <= infrequent_enough_to_ignore) {
right_tabs->remove(to_prune);
}
}
}
// Given a paragraph model mark rows[row_start, row_end) as said model
// start or body lines.
//
// Case 1: model->first_indent_ != model->body_indent_
// Differentiating the paragraph start lines from the paragraph body lines in
// this case is easy, we just see how far each line is indented.
//
// Case 2: model->first_indent_ == model->body_indent_
// Here, we find end-of-paragraph lines by looking for "short lines."
// What constitutes a "short line" changes depending on whether the text
// ragged-right[left] or fully justified (aligned left and right).
//
// Case 2a: Ragged Right (or Left) text. (eop_threshold == 0)
// We have a new paragraph it the first word would have at the end
// of the previous line.
//
// Case 2b: Fully Justified. (eop_threshold > 0)
// We mark a line as short (end of paragraph) if the offside indent
// is greater than eop_threshold.
static void MarkRowsWithModel(GenericVector<RowScratchRegisters> *rows,
int row_start, int row_end,
const ParagraphModel *model,
bool ltr, int eop_threshold) {
if (!AcceptableRowArgs(0, 0, __func__, rows, row_start, row_end))
return;
for (int row = row_start; row < row_end; row++) {
bool valid_first = ValidFirstLine(rows, row, model);
bool valid_body = ValidBodyLine(rows, row, model);
if (valid_first && !valid_body) {
(*rows)[row].AddStartLine(model);
} else if (valid_body && !valid_first) {
(*rows)[row].AddBodyLine(model);
} else if (valid_body && valid_first) {
bool after_eop = (row == row_start);
if (row > row_start) {
if (eop_threshold > 0) {
if (model->justification() == JUSTIFICATION_LEFT) {
after_eop = (*rows)[row - 1].rindent_ > eop_threshold;
} else {
after_eop = (*rows)[row - 1].lindent_ > eop_threshold;
}
} else {
after_eop = FirstWordWouldHaveFit((*rows)[row - 1], (*rows)[row],
model->justification());
}
}
if (after_eop) {
(*rows)[row].AddStartLine(model);
} else {
(*rows)[row].AddBodyLine(model);
}
} else {
// Do nothing. Stray row.
}
}
}
// GeometricClassifierState holds all of the information we'll use while
// trying to determine a paragraph model for the text lines in a block of
// text:
// + the rows under consideration [row_start, row_end)
// + the common left- and right-indent tab stops
// + does the block start out left-to-right or right-to-left
// Further, this struct holds the data we amass for the (single) ParagraphModel
// we'll assign to the text lines (assuming we get that far).
struct GeometricClassifierState {
GeometricClassifierState(int dbg_level,
GenericVector<RowScratchRegisters> *r,
int r_start, int r_end)
: debug_level(dbg_level), rows(r), row_start(r_start), row_end(r_end),
margin(0) {
tolerance = InterwordSpace(*r, r_start, r_end);
CalculateTabStops(r, r_start, r_end, tolerance,
&left_tabs, &right_tabs);
if (debug_level >= 3) {
tprintf("Geometry: TabStop cluster tolerance = %d; "
"%d left tabs; %d right tabs\n",
tolerance, left_tabs.size(), right_tabs.size());
}
ltr = (*r)[r_start].ri_->ltr;
}
void AssumeLeftJustification() {
just = tesseract::JUSTIFICATION_LEFT;
margin = (*rows)[row_start].lmargin_;
}
void AssumeRightJustification() {
just = tesseract::JUSTIFICATION_RIGHT;
margin = (*rows)[row_start].rmargin_;
}
// Align tabs are the tab stops the text is aligned to.
const GenericVector<Cluster> &AlignTabs() const {
if (just == tesseract::JUSTIFICATION_RIGHT) return right_tabs;
return left_tabs;
}
// Offside tabs are the tab stops opposite the tabs used to align the text.
//
// Note that for a left-to-right text which is aligned to the right such as
// this function comment, the offside tabs are the horizontal tab stops
// marking the beginning of ("Note", "this" and "marking").
const GenericVector<Cluster> &OffsideTabs() const {
if (just == tesseract::JUSTIFICATION_RIGHT) return left_tabs;
return right_tabs;
}
// Return whether the i'th row extends from the leftmost left tab stop
// to the right most right tab stop.
bool IsFullRow(int i) const {
return ClosestCluster(left_tabs, (*rows)[i].lindent_) == 0 &&
ClosestCluster(right_tabs, (*rows)[i].rindent_) == 0;
}
int AlignsideTabIndex(int row_idx) const {
return ClosestCluster(AlignTabs(), (*rows)[row_idx].AlignsideIndent(just));
}
// Given what we know about the paragraph justification (just), would the
// first word of row_b have fit at the end of row_a?
bool FirstWordWouldHaveFit(int row_a, int row_b) {
return ::tesseract::FirstWordWouldHaveFit(
(*rows)[row_a], (*rows)[row_b], just);
}
void PrintRows() const { PrintRowRange(*rows, row_start, row_end); }
void Fail(int min_debug_level, const char *why) const {
if (debug_level < min_debug_level) return;
tprintf("# %s\n", why);
PrintRows();
}
ParagraphModel Model() const {
return ParagraphModel(just, margin, first_indent, body_indent, tolerance);
}
// We print out messages with a debug level at least as great as debug_level.
int debug_level;
// The Geometric Classifier was asked to find a single paragraph model
// to fit the text rows (*rows)[row_start, row_end)
GenericVector<RowScratchRegisters> *rows;
int row_start;
int row_end;
// The amount by which we expect the text edge can vary and still be aligned.
int tolerance;
// Is the script in this text block left-to-right?
// HORRIBLE ROUGH APPROXIMATION. TODO(eger): Improve
bool ltr;
// These left and right tab stops were determined to be the common tab
// stops for the given text.
GenericVector<Cluster> left_tabs;
GenericVector<Cluster> right_tabs;
// These are parameters we must determine to create a ParagraphModel.
tesseract::ParagraphJustification just;
int margin;
int first_indent;
int body_indent;
// eop_threshold > 0 if the text is fully justified. See MarkRowsWithModel()
int eop_threshold;
};
// Given a section of text where strong textual clues did not help identifying
// paragraph breaks, and for which the left and right indents have exactly
// three tab stops between them, attempt to find the paragraph breaks based
// solely on the outline of the text and whether the script is left-to-right.
//
// Algorithm Detail:
// The selected rows are in the form of a rectangle except
// for some number of "short lines" of the same length:
//
// (A1) xxxxxxxxxxxxx (B1) xxxxxxxxxxxx
// xxxxxxxxxxx xxxxxxxxxx # A "short" line.
// xxxxxxxxxxxxx xxxxxxxxxxxx
// xxxxxxxxxxxxx xxxxxxxxxxxx
//
// We have a slightly different situation if the only short
// line is at the end of the excerpt.
//
// (A2) xxxxxxxxxxxxx (B2) xxxxxxxxxxxx
// xxxxxxxxxxxxx xxxxxxxxxxxx
// xxxxxxxxxxxxx xxxxxxxxxxxx
// xxxxxxxxxxx xxxxxxxxxx # A "short" line.
//
// We'll interpret these as follows based on the reasoning in the comment for
// GeometricClassify():
// [script direction: first indent, body indent]
// (A1) LtR: 2,0 RtL: 0,0 (B1) LtR: 0,0 RtL: 2,0
// (A2) LtR: 2,0 RtL: CrR (B2) LtR: CrL RtL: 2,0
static void GeometricClassifyThreeTabStopTextBlock(
int debug_level,
GeometricClassifierState &s,
ParagraphTheory *theory) {
int num_rows = s.row_end - s.row_start;
int num_full_rows = 0;
int last_row_full = 0;
for (int i = s.row_start; i < s.row_end; i++) {
if (s.IsFullRow(i)) {
num_full_rows++;
if (i == s.row_end - 1) last_row_full++;
}
}
if (num_full_rows < 0.7 * num_rows) {
s.Fail(1, "Not enough full lines to know which lines start paras.");
return;
}
// eop_threshold gets set if we're fully justified; see MarkRowsWithModel()
s.eop_threshold = 0;
if (s.ltr) {
s.AssumeLeftJustification();
} else {
s.AssumeRightJustification();
}
if (debug_level > 0) {
tprintf("# Not enough variety for clear outline classification. "
"Guessing these are %s aligned based on script.\n",
s.ltr ? "left" : "right");
s.PrintRows();
}
if (s.AlignTabs().size() == 2) { // case A1 or A2
s.first_indent = s.AlignTabs()[1].center;
s.body_indent = s.AlignTabs()[0].center;
} else { // case B1 or B2
if (num_rows - 1 == num_full_rows - last_row_full) {
// case B2
const ParagraphModel *model = s.ltr ? kCrownLeft : kCrownRight;
(*s.rows)[s.row_start].AddStartLine(model);
for (int i = s.row_start + 1; i < s.row_end; i++) {
(*s.rows)[i].AddBodyLine(model);
}
return;
} else {
// case B1
s.first_indent = s.body_indent = s.AlignTabs()[0].center;
s.eop_threshold = (s.OffsideTabs()[0].center +
s.OffsideTabs()[1].center) / 2;
}
}
const ParagraphModel *model = theory->AddModel(s.Model());
MarkRowsWithModel(s.rows, s.row_start, s.row_end, model,
s.ltr, s.eop_threshold);
return;
}
// This function is called if strong textual clues were not available, but
// the caller hopes that the paragraph breaks will be super obvious just
// by the outline of the text.
//
// The particularly difficult case is figuring out what's going on if you
// don't have enough short paragraph end lines to tell us what's going on.
//
// For instance, let's say you have the following outline:
//
// (A1) xxxxxxxxxxxxxxxxxxxxxx
// xxxxxxxxxxxxxxxxxxxx
// xxxxxxxxxxxxxxxxxxxxxx
// xxxxxxxxxxxxxxxxxxxxxx
//
// Even if we know that the text is left-to-right and so will probably be
// left-aligned, both of the following are possible texts:
//
// (A1a) 1. Here our list item
// with two full lines.
// 2. Here a second item.
// 3. Here our third one.
//
// (A1b) so ends paragraph one.
// Here starts another
// paragraph we want to
// read. This continues
//
// These examples are obvious from the text and should have been caught
// by the StrongEvidenceClassify pass. However, for languages where we don't
// have capital letters to go on (e.g. Hebrew, Arabic, Hindi, Chinese),
// it's worth guessing that (A1b) is the correct interpretation if there are
// far more "full" lines than "short" lines.
static void GeometricClassify(int debug_level,
GenericVector<RowScratchRegisters> *rows,
int row_start, int row_end,
ParagraphTheory *theory) {
if (!AcceptableRowArgs(debug_level, 4, __func__, rows, row_start, row_end))
return;
if (debug_level > 1) {
tprintf("###############################################\n");
tprintf("##### GeometricClassify( rows[%d:%d) ) ####\n",
row_start, row_end);
tprintf("###############################################\n");
}
RecomputeMarginsAndClearHypotheses(rows, row_start, row_end, 10);
GeometricClassifierState s(debug_level, rows, row_start, row_end);
if (s.left_tabs.size() > 2 && s.right_tabs.size() > 2) {
s.Fail(2, "Too much variety for simple outline classification.");
return;
}
if (s.left_tabs.size() <= 1 && s.right_tabs.size() <= 1) {
s.Fail(1, "Not enough variety for simple outline classification.");
return;
}
if (s.left_tabs.size() + s.right_tabs.size() == 3) {
GeometricClassifyThreeTabStopTextBlock(debug_level, s, theory);
return;
}
// At this point, we know that one side has at least two tab stops, and the
// other side has one or two tab stops.
// Left to determine:
// (1) Which is the body indent and which is the first line indent?
// (2) Is the text fully justified?
// If one side happens to have three or more tab stops, assume that side
// is opposite of the aligned side.
if (s.right_tabs.size() > 2) {
s.AssumeLeftJustification();
} else if (s.left_tabs.size() > 2) {
s.AssumeRightJustification();
} else if (s.ltr) { // guess based on script direction
s.AssumeLeftJustification();
} else {
s.AssumeRightJustification();
}
if (s.AlignTabs().size() == 2) {
// For each tab stop on the aligned side, how many of them appear
// to be paragraph start lines? [first lines]
int firsts[2] = {0, 0};
// Count the first line as a likely paragraph start line.
firsts[s.AlignsideTabIndex(s.row_start)]++;
// For each line, if the first word would have fit on the previous
// line count it as a likely paragraph start line.
bool jam_packed = true;
for (int i = s.row_start + 1; i < s.row_end; i++) {
if (s.FirstWordWouldHaveFit(i - 1, i)) {
firsts[s.AlignsideTabIndex(i)]++;
jam_packed = false;
}
}
// Make an extra accounting for the last line of the paragraph just
// in case it's the only short line in the block. That is, take its
// first word as typical and see if this looks like the *last* line
// of a paragraph. If so, mark the *other* indent as probably a first.
if (jam_packed && s.FirstWordWouldHaveFit(s.row_end - 1, s.row_end - 1)) {
firsts[1 - s.AlignsideTabIndex(s.row_end - 1)]++;
}
int percent0firsts, percent1firsts;
percent0firsts = (100 * firsts[0]) / s.AlignTabs()[0].count;
percent1firsts = (100 * firsts[1]) / s.AlignTabs()[1].count;
// TODO(eger): Tune these constants if necessary.
if ((percent0firsts < 20 && 30 < percent1firsts) ||
percent0firsts + 30 < percent1firsts) {
s.first_indent = s.AlignTabs()[1].center;
s.body_indent = s.AlignTabs()[0].center;
} else if ((percent1firsts < 20 && 30 < percent0firsts) ||
percent1firsts + 30 < percent0firsts) {
s.first_indent = s.AlignTabs()[0].center;
s.body_indent = s.AlignTabs()[1].center;
} else {
// Ambiguous! Probably lineated (poetry)
if (debug_level > 1) {
tprintf("# Cannot determine %s indent likely to start paragraphs.\n",
s.just == tesseract::JUSTIFICATION_LEFT ? "left" : "right");
tprintf("# Indent of %d looks like a first line %d%% of the time.\n",
s.AlignTabs()[0].center, percent0firsts);
tprintf("# Indent of %d looks like a first line %d%% of the time.\n",
s.AlignTabs()[1].center, percent1firsts);
s.PrintRows();
}
return;
}
} else {
// There's only one tab stop for the "aligned to" side.
s.first_indent = s.body_indent = s.AlignTabs()[0].center;
}
// At this point, we have our model.
const ParagraphModel *model = theory->AddModel(s.Model());
// Now all we have to do is figure out if the text is fully justified or not.
// eop_threshold: default to fully justified unless we see evidence below.
// See description on MarkRowsWithModel()
s.eop_threshold =
(s.OffsideTabs()[0].center + s.OffsideTabs()[1].center) / 2;
// If the text is not fully justified, re-set the eop_threshold to 0.
if (s.AlignTabs().size() == 2) {
// Paragraphs with a paragraph-start indent.
for (int i = s.row_start; i < s.row_end - 1; i++) {
if (ValidFirstLine(s.rows, i + 1, model) &&
!NearlyEqual(s.OffsideTabs()[0].center,
(*s.rows)[i].OffsideIndent(s.just), s.tolerance)) {
// We found a non-end-of-paragraph short line: not fully justified.
s.eop_threshold = 0;
break;
}
}
} else {
// Paragraphs with no paragraph-start indent.
for (int i = s.row_start; i < s.row_end - 1; i++) {
if (!s.FirstWordWouldHaveFit(i, i + 1) &&
!NearlyEqual(s.OffsideTabs()[0].center,
(*s.rows)[i].OffsideIndent(s.just), s.tolerance)) {
// We found a non-end-of-paragraph short line: not fully justified.
s.eop_threshold = 0;
break;
}
}
}
MarkRowsWithModel(rows, row_start, row_end, model, s.ltr, s.eop_threshold);
}
// =============== Implementation of ParagraphTheory =====================
const ParagraphModel *ParagraphTheory::AddModel(const ParagraphModel &model) {
for (int i = 0; i < models_->size(); i++) {
if ((*models_)[i]->Comparable(model))
return (*models_)[i];
}
auto *m = new ParagraphModel(model);
models_->push_back(m);
models_we_added_.push_back_new(m);
return m;
}
void ParagraphTheory::DiscardUnusedModels(const SetOfModels &used_models) {
for (int i = models_->size() - 1; i >= 0; i--) {
ParagraphModel *m = (*models_)[i];
if (!used_models.contains(m) && models_we_added_.contains(m)) {
models_->remove(i);
models_we_added_.remove(models_we_added_.get_index(m));
delete m;
}
}
}
// Examine rows[start, end) and try to determine if an existing non-centered
// paragraph model would fit them perfectly. If so, return a pointer to it.
// If not, return nullptr.
const ParagraphModel *ParagraphTheory::Fits(
const GenericVector<RowScratchRegisters> *rows, int start, int end) const {
for (int m = 0; m < models_->size(); m++) {
const ParagraphModel *model = (*models_)[m];
if (model->justification() != JUSTIFICATION_CENTER &&
RowsFitModel(rows, start, end, model))
return model;
}
return nullptr;
}
void ParagraphTheory::NonCenteredModels(SetOfModels *models) {
for (int m = 0; m < models_->size(); m++) {
const ParagraphModel *model = (*models_)[m];
if (model->justification() != JUSTIFICATION_CENTER)
models->push_back_new(model);
}
}
int ParagraphTheory::IndexOf(const ParagraphModel *model) const {
for (int i = 0; i < models_->size(); i++) {
if ((*models_)[i] == model)
return i;
}
return -1;
}
bool ValidFirstLine(const GenericVector<RowScratchRegisters> *rows,
int row, const ParagraphModel *model) {
if (!StrongModel(model)) {
tprintf("ValidFirstLine() should only be called with strong models!\n");
}
return StrongModel(model) &&
model->ValidFirstLine(
(*rows)[row].lmargin_, (*rows)[row].lindent_,
(*rows)[row].rindent_, (*rows)[row].rmargin_);
}
bool ValidBodyLine(const GenericVector<RowScratchRegisters> *rows,
int row, const ParagraphModel *model) {
if (!StrongModel(model)) {
tprintf("ValidBodyLine() should only be called with strong models!\n");
}
return StrongModel(model) &&
model->ValidBodyLine(
(*rows)[row].lmargin_, (*rows)[row].lindent_,
(*rows)[row].rindent_, (*rows)[row].rmargin_);
}
bool CrownCompatible(const GenericVector<RowScratchRegisters> *rows,
int a, int b, const ParagraphModel *model) {
if (model != kCrownRight && model != kCrownLeft) {
tprintf("CrownCompatible() should only be called with crown models!\n");
return false;
}
RowScratchRegisters &row_a = (*rows)[a];
RowScratchRegisters &row_b = (*rows)[b];
if (model == kCrownRight) {
return NearlyEqual(row_a.rindent_ + row_a.rmargin_,
row_b.rindent_ + row_b.rmargin_,
Epsilon(row_a.ri_->average_interword_space));
}
return NearlyEqual(row_a.lindent_ + row_a.lmargin_,
row_b.lindent_ + row_b.lmargin_,
Epsilon(row_a.ri_->average_interword_space));
}
// =============== Implementation of ParagraphModelSmearer ====================
ParagraphModelSmearer::ParagraphModelSmearer(
GenericVector<RowScratchRegisters> *rows,
int row_start, int row_end, ParagraphTheory *theory)
: theory_(theory), rows_(rows), row_start_(row_start),
row_end_(row_end) {
if (!AcceptableRowArgs(0, 0, __func__, rows, row_start, row_end)) {
row_start_ = 0;
row_end_ = 0;
return;
}
SetOfModels no_models;
for (int row = row_start - 1; row <= row_end; row++) {
open_models_.push_back(no_models);
}
}
// see paragraphs_internal.h
void ParagraphModelSmearer::CalculateOpenModels(int row_start, int row_end) {
SetOfModels no_models;
if (row_start < row_start_) row_start = row_start_;
if (row_end > row_end_) row_end = row_end_;
for (int row = (row_start > 0) ? row_start - 1 : row_start; row < row_end;
row++) {
if ((*rows_)[row].ri_->num_words == 0) {
OpenModels(row + 1) = no_models;
} else {
SetOfModels &opened = OpenModels(row);
(*rows_)[row].StartHypotheses(&opened);
// Which models survive the transition from row to row + 1?
SetOfModels still_open;
for (int m = 0; m < opened.size(); m++) {
if (ValidFirstLine(rows_, row, opened[m]) ||
ValidBodyLine(rows_, row, opened[m])) {
// This is basic filtering; we check likely paragraph starty-ness down
// below in Smear() -- you know, whether the first word would have fit
// and such.
still_open.push_back_new(opened[m]);
}
}
OpenModels(row + 1) = still_open;
}
}
}
// see paragraphs_internal.h
void ParagraphModelSmearer::Smear() {
CalculateOpenModels(row_start_, row_end_);
// For each row which we're unsure about (that is, it is LT_UNKNOWN or
// we have multiple LT_START hypotheses), see if there's a model that
// was recently used (an "open" model) which might model it well.
for (int i = row_start_; i < row_end_; i++) {
RowScratchRegisters &row = (*rows_)[i];
if (row.ri_->num_words == 0)
continue;
// Step One:
// Figure out if there are "open" models which are left-alined or
// right-aligned. This is important for determining whether the
// "first" word in a row would fit at the "end" of the previous row.
bool left_align_open = false;
bool right_align_open = false;
for (int m = 0; m < OpenModels(i).size(); m++) {
switch (OpenModels(i)[m]->justification()) {
case JUSTIFICATION_LEFT: left_align_open = true; break;
case JUSTIFICATION_RIGHT: right_align_open = true; break;
default: left_align_open = right_align_open = true;
}
}
// Step Two:
// Use that knowledge to figure out if this row is likely to
// start a paragraph.
bool likely_start;
if (i == 0) {
likely_start = true;
} else {
if ((left_align_open && right_align_open) ||
(!left_align_open && !right_align_open)) {
likely_start = LikelyParagraphStart((*rows_)[i - 1], row,
JUSTIFICATION_LEFT) ||
LikelyParagraphStart((*rows_)[i - 1], row,
JUSTIFICATION_RIGHT);
} else if (left_align_open) {
likely_start = LikelyParagraphStart((*rows_)[i - 1], row,
JUSTIFICATION_LEFT);
} else {
likely_start = LikelyParagraphStart((*rows_)[i - 1], row,
JUSTIFICATION_RIGHT);
}
}
// Step Three:
// If this text line seems like an obvious first line of an
// open model, or an obvious continuation of an existing
// modelled paragraph, mark it up.
if (likely_start) {
// Add Start Hypotheses for all Open models that fit.
for (int m = 0; m < OpenModels(i).size(); m++) {
if (ValidFirstLine(rows_, i, OpenModels(i)[m])) {
row.AddStartLine(OpenModels(i)[m]);
}
}
} else {
// Add relevant body line hypotheses.
SetOfModels last_line_models;
if (i > 0) {
(*rows_)[i - 1].StrongHypotheses(&last_line_models);
} else {
theory_->NonCenteredModels(&last_line_models);
}
for (int m = 0; m < last_line_models.size(); m++) {
const ParagraphModel *model = last_line_models[m];
if (ValidBodyLine(rows_, i, model))
row.AddBodyLine(model);
}
}
// Step Four:
// If we're still quite unsure about this line, go through all
// models in our theory and see if this row could be the start
// of any of our models.
if (row.GetLineType() == LT_UNKNOWN ||
(row.GetLineType() == LT_START && !row.UniqueStartHypothesis())) {
SetOfModels all_models;
theory_->NonCenteredModels(&all_models);
for (int m = 0; m < all_models.size(); m++) {
if (ValidFirstLine(rows_, i, all_models[m])) {
row.AddStartLine(all_models[m]);
}
}
}
// Step Five:
// Since we may have updated the hypotheses about this row, we need
// to recalculate the Open models for the rest of rows[i + 1, row_end)
if (row.GetLineType() != LT_UNKNOWN) {
CalculateOpenModels(i + 1, row_end_);
}
}
}
// ================ Main Paragraph Detection Algorithm =======================
// Find out what ParagraphModels are actually used, and discard any
// that are not.
static void DiscardUnusedModels(const GenericVector<RowScratchRegisters> &rows,
ParagraphTheory *theory) {
SetOfModels used_models;
for (int i = 0; i < rows.size(); i++) {
rows[i].StrongHypotheses(&used_models);
}
theory->DiscardUnusedModels(used_models);
}
// DowngradeWeakestToCrowns:
// Forget any flush-{left, right} models unless we see two or more
// of them in sequence.
//
// In pass 3, we start to classify even flush-left paragraphs (paragraphs
// where the first line and body indent are the same) as having proper Models.
// This is generally dangerous, since if you start imagining that flush-left
// is a typical paragraph model when it is not, it will lead you to chop normal
// indented paragraphs in the middle whenever a sentence happens to start on a
// new line (see "This" above). What to do?
// What we do is to take any paragraph which is flush left and is not
// preceded by another paragraph of the same model and convert it to a "Crown"
// paragraph. This is a weak pseudo-ParagraphModel which is a placeholder
// for later. It means that the paragraph is flush, but it would be desirable
// to mark it as the same model as following text if it fits. This downgrade
// FlushLeft -> CrownLeft -> Model of following paragraph. Means that we
// avoid making flush left Paragraph Models whenever we see a top-of-the-page
// half-of-a-paragraph. and instead we mark it the same as normal body text.
//
// Implementation:
//
// Comb backwards through the row scratch registers, and turn any
// sequences of body lines of equivalent type abutted against the beginning
// or a body or start line of a different type into a crown paragraph.
static void DowngradeWeakestToCrowns(int debug_level, ParagraphTheory *theory,
GenericVector<RowScratchRegisters> *rows) {
int start;
for (int end = rows->size(); end > 0; end = start) {
// Search back for a body line of a unique type.
const ParagraphModel *model = nullptr;
while (end > 0 &&
(model = (*rows)[end - 1].UniqueBodyHypothesis()) == nullptr) {
end--;
}
if (end == 0) break;
start = end - 1;
while (start >= 0 && (*rows)[start].UniqueBodyHypothesis() == model) {
start--; // walk back to the first line that is not the same body type.
}
if (start >= 0 && (*rows)[start].UniqueStartHypothesis() == model &&
StrongModel(model) &&
NearlyEqual(model->first_indent(), model->body_indent(),
model->tolerance())) {
start--;
}
start++;
// Now rows[start, end) is a sequence of unique body hypotheses of model.
if (StrongModel(model) && model->justification() == JUSTIFICATION_CENTER)
continue;
if (!StrongModel(model)) {
while (start > 0 &&
CrownCompatible(rows, start - 1, start, model))
start--;
}
if (start == 0 ||
(!StrongModel(model)) ||
(StrongModel(model) && !ValidFirstLine(rows, start - 1, model))) {
// crownify rows[start, end)
const ParagraphModel *crown_model = model;
if (StrongModel(model)) {
if (model->justification() == JUSTIFICATION_LEFT)
crown_model = kCrownLeft;
else
crown_model = kCrownRight;
}
(*rows)[start].SetUnknown();
(*rows)[start].AddStartLine(crown_model);
for (int row = start + 1; row < end; row++) {
(*rows)[row].SetUnknown();
(*rows)[row].AddBodyLine(crown_model);
}
}
}
DiscardUnusedModels(*rows, theory);
}
// Clear all hypotheses about lines [start, end) and reset margins.
//
// The empty space between the left of a row and the block boundary (and
// similarly for the right) is split into two pieces: margin and indent.
// In initial processing, we assume the block is tight and the margin for
// all lines is set to zero. However, if our first pass does not yield
// models for everything, it may be due to an inset paragraph like a
// block-quote. In that case, we make a second pass over that unmarked
// section of the page and reset the "margin" portion of the empty space
// to the common amount of space at the ends of the lines under consid-
// eration. This would be equivalent to percentile set to 0. However,
// sometimes we have a single character sticking out in the right margin
// of a text block (like the 'r' in 'for' on line 3 above), and we can
// really just ignore it as an outlier. To express this, we allow the
// user to specify the percentile (0..100) of indent values to use as
// the common margin for each row in the run of rows[start, end).
void RecomputeMarginsAndClearHypotheses(
GenericVector<RowScratchRegisters> *rows, int start, int end,
int percentile) {
if (!AcceptableRowArgs(0, 0, __func__, rows, start, end))
return;
int lmin, lmax, rmin, rmax;
lmin = lmax = (*rows)[start].lmargin_ + (*rows)[start].lindent_;
rmin = rmax = (*rows)[start].rmargin_ + (*rows)[start].rindent_;
for (int i = start; i < end; i++) {
RowScratchRegisters &sr = (*rows)[i];
sr.SetUnknown();
if (sr.ri_->num_words == 0)
continue;
UpdateRange(sr.lmargin_ + sr.lindent_, &lmin, &lmax);
UpdateRange(sr.rmargin_ + sr.rindent_, &rmin, &rmax);
}
STATS lefts(lmin, lmax + 1);
STATS rights(rmin, rmax + 1);
for (int i = start; i < end; i++) {
RowScratchRegisters &sr = (*rows)[i];
if (sr.ri_->num_words == 0)
continue;
lefts.add(sr.lmargin_ + sr.lindent_, 1);
rights.add(sr.rmargin_ + sr.rindent_, 1);
}
int ignorable_left = lefts.ile(ClipToRange(percentile, 0, 100) / 100.0);
int ignorable_right = rights.ile(ClipToRange(percentile, 0, 100) / 100.0);
for (int i = start; i < end; i++) {
RowScratchRegisters &sr = (*rows)[i];
int ldelta = ignorable_left - sr.lmargin_;
sr.lmargin_ += ldelta;
sr.lindent_ -= ldelta;
int rdelta = ignorable_right - sr.rmargin_;
sr.rmargin_ += rdelta;
sr.rindent_ -= rdelta;
}
}
// Return the median inter-word space in rows[row_start, row_end).
int InterwordSpace(const GenericVector<RowScratchRegisters> &rows,
int row_start, int row_end) {
if (row_end < row_start + 1) return 1;
int word_height = (rows[row_start].ri_->lword_box.height() +
rows[row_end - 1].ri_->lword_box.height()) / 2;
int word_width = (rows[row_start].ri_->lword_box.width() +
rows[row_end - 1].ri_->lword_box.width()) / 2;
STATS spacing_widths(0, 5 + word_width);
for (int i = row_start; i < row_end; i++) {
if (rows[i].ri_->num_words > 1) {
spacing_widths.add(rows[i].ri_->average_interword_space, 1);
}
}
int minimum_reasonable_space = word_height / 3;
if (minimum_reasonable_space < 2)
minimum_reasonable_space = 2;
int median = spacing_widths.median();
return (median > minimum_reasonable_space)
? median : minimum_reasonable_space;
}
// Return whether the first word on the after line can fit in the space at
// the end of the before line (knowing which way the text is aligned and read).
bool FirstWordWouldHaveFit(const RowScratchRegisters &before,
const RowScratchRegisters &after,
tesseract::ParagraphJustification justification) {
if (before.ri_->num_words == 0 || after.ri_->num_words == 0)
return true;
if (justification == JUSTIFICATION_UNKNOWN) {
tprintf("Don't call FirstWordWouldHaveFit(r, s, JUSTIFICATION_UNKNOWN).\n");
}
int available_space;
if (justification == JUSTIFICATION_CENTER) {
available_space = before.lindent_ + before.rindent_;
} else {
available_space = before.OffsideIndent(justification);
}
available_space -= before.ri_->average_interword_space;
if (before.ri_->ltr)
return after.ri_->lword_box.width() < available_space;
return after.ri_->rword_box.width() < available_space;
}
// Return whether the first word on the after line can fit in the space at
// the end of the before line (not knowing which way the text goes) in a left
// or right alignment.
bool FirstWordWouldHaveFit(const RowScratchRegisters &before,
const RowScratchRegisters &after) {
if (before.ri_->num_words == 0 || after.ri_->num_words == 0)
return true;
int available_space = before.lindent_;
if (before.rindent_ > available_space)
available_space = before.rindent_;
available_space -= before.ri_->average_interword_space;
if (before.ri_->ltr)
return after.ri_->lword_box.width() < available_space;
return after.ri_->rword_box.width() < available_space;
}
static bool TextSupportsBreak(const RowScratchRegisters &before,
const RowScratchRegisters &after) {
if (before.ri_->ltr) {
return before.ri_->rword_likely_ends_idea &&
after.ri_->lword_likely_starts_idea;
} else {
return before.ri_->lword_likely_ends_idea &&
after.ri_->rword_likely_starts_idea;
}
}
static bool LikelyParagraphStart(const RowScratchRegisters &before,
const RowScratchRegisters &after,
tesseract::ParagraphJustification j) {
return before.ri_->num_words == 0 ||
(FirstWordWouldHaveFit(before, after, j) &&
TextSupportsBreak(before, after));
}
// Examine rows[start, end) and try to determine what sort of ParagraphModel
// would fit them as a single paragraph.
// If we can't produce a unique model justification_ = JUSTIFICATION_UNKNOWN.
// If the rows given could be a consistent start to a paragraph, set *consistent
// true.
static ParagraphModel InternalParagraphModelByOutline(
const GenericVector<RowScratchRegisters> *rows,
int start, int end, int tolerance, bool *consistent) {
int ltr_line_count = 0;
for (int i = start; i < end; i++) {
ltr_line_count += static_cast<int>((*rows)[i].ri_->ltr);
}
bool ltr = (ltr_line_count >= (end - start) / 2);
*consistent = true;
if (!AcceptableRowArgs(0, 2, __func__, rows, start, end))
return ParagraphModel();
// Ensure the caller only passed us a region with a common rmargin and
// lmargin.
int lmargin = (*rows)[start].lmargin_;
int rmargin = (*rows)[start].rmargin_;
int lmin, lmax, rmin, rmax, cmin, cmax;
lmin = lmax = (*rows)[start + 1].lindent_;
rmin = rmax = (*rows)[start + 1].rindent_;
cmin = cmax = 0;
for (int i = start + 1; i < end; i++) {
if ((*rows)[i].lmargin_ != lmargin || (*rows)[i].rmargin_ != rmargin) {
tprintf("Margins don't match! Software error.\n");
*consistent = false;
return ParagraphModel();
}
UpdateRange((*rows)[i].lindent_, &lmin, &lmax);
UpdateRange((*rows)[i].rindent_, &rmin, &rmax);
UpdateRange((*rows)[i].rindent_ - (*rows)[i].lindent_, &cmin, &cmax);
}
int ldiff = lmax - lmin;
int rdiff = rmax - rmin;
int cdiff = cmax - cmin;
if (rdiff > tolerance && ldiff > tolerance) {
if (cdiff < tolerance * 2) {
if (end - start < 3)
return ParagraphModel();
return ParagraphModel(JUSTIFICATION_CENTER, 0, 0, 0, tolerance);
}
*consistent = false;
return ParagraphModel();
}
if (end - start < 3) // Don't return a model for two line paras.
return ParagraphModel();
// These booleans keep us from saying something is aligned left when the body
// left variance is too large.
bool body_admits_left_alignment = ldiff < tolerance;
bool body_admits_right_alignment = rdiff < tolerance;
ParagraphModel left_model =
ParagraphModel(JUSTIFICATION_LEFT, lmargin, (*rows)[start].lindent_,
(lmin + lmax) / 2, tolerance);
ParagraphModel right_model =
ParagraphModel(JUSTIFICATION_RIGHT, rmargin, (*rows)[start].rindent_,
(rmin + rmax) / 2, tolerance);
// These booleans keep us from having an indent on the "wrong side" for the
// first line.
bool text_admits_left_alignment = ltr || left_model.is_flush();
bool text_admits_right_alignment = !ltr || right_model.is_flush();
// At least one of the edges is less than tolerance in variance.
// If the other is obviously ragged, it can't be the one aligned to.
// [Note the last line is included in this raggedness.]
if (tolerance < rdiff) {
if (body_admits_left_alignment && text_admits_left_alignment)
return left_model;
*consistent = false;
return ParagraphModel();
}
if (tolerance < ldiff) {
if (body_admits_right_alignment && text_admits_right_alignment)
return right_model;
*consistent = false;
return ParagraphModel();
}
// At this point, we know the body text doesn't vary much on either side.
// If the first line juts out oddly in one direction or the other,
// that likely indicates the side aligned to.
int first_left = (*rows)[start].lindent_;
int first_right = (*rows)[start].rindent_;
if (ltr && body_admits_left_alignment &&
(first_left < lmin || first_left > lmax))
return left_model;
if (!ltr && body_admits_right_alignment &&
(first_right < rmin || first_right > rmax))
return right_model;
*consistent = false;
return ParagraphModel();
}
// Examine rows[start, end) and try to determine what sort of ParagraphModel
// would fit them as a single paragraph. If nothing fits,
// justification_ = JUSTIFICATION_UNKNOWN and print the paragraph to debug
// output if we're debugging.
static ParagraphModel ParagraphModelByOutline(
int debug_level,
const GenericVector<RowScratchRegisters> *rows,
int start, int end, int tolerance) {
bool unused_consistent;
ParagraphModel retval = InternalParagraphModelByOutline(
rows, start, end, tolerance, &unused_consistent);
if (debug_level >= 2 && retval.justification() == JUSTIFICATION_UNKNOWN) {
tprintf("Could not determine a model for this paragraph:\n");
PrintRowRange(*rows, start, end);
}
return retval;
}
// Do rows[start, end) form a single instance of the given paragraph model?
bool RowsFitModel(const GenericVector<RowScratchRegisters> *rows,
int start, int end, const ParagraphModel *model) {
if (!AcceptableRowArgs(0, 1, __func__, rows, start, end))
return false;
if (!ValidFirstLine(rows, start, model)) return false;
for (int i = start + 1 ; i < end; i++) {
if (!ValidBodyLine(rows, i, model)) return false;
}
return true;
}
// Examine rows[row_start, row_end) as an independent section of text,
// and mark rows that are exceptionally clear as start-of-paragraph
// and paragraph-body lines.
//
// We presume that any lines surrounding rows[row_start, row_end) may
// have wildly different paragraph models, so we don't key any data off
// of those lines.
//
// We only take the very strongest signals, as we don't want to get
// confused and marking up centered text, poetry, or source code as
// clearly part of a typical paragraph.
static void MarkStrongEvidence(GenericVector<RowScratchRegisters> *rows,
int row_start, int row_end) {
// Record patently obvious body text.
for (int i = row_start + 1; i < row_end; i++) {
const RowScratchRegisters &prev = (*rows)[i - 1];
RowScratchRegisters &curr = (*rows)[i];
tesseract::ParagraphJustification typical_justification =
prev.ri_->ltr ? JUSTIFICATION_LEFT : JUSTIFICATION_RIGHT;
if (!curr.ri_->rword_likely_starts_idea &&
!curr.ri_->lword_likely_starts_idea &&
!FirstWordWouldHaveFit(prev, curr, typical_justification)) {
curr.SetBodyLine();
}
}
// Record patently obvious start paragraph lines.
//
// It's an extremely good signal of the start of a paragraph that
// the first word would have fit on the end of the previous line.
// However, applying just that signal would have us mark random
// start lines of lineated text (poetry and source code) and some
// centered headings as paragraph start lines. Therefore, we use
// a second qualification for a paragraph start: Not only should
// the first word of this line have fit on the previous line,
// but also, this line should go full to the right of the block,
// disallowing a subsequent word from having fit on this line.
// First row:
{
RowScratchRegisters &curr = (*rows)[row_start];
RowScratchRegisters &next = (*rows)[row_start + 1];
tesseract::ParagraphJustification j =
curr.ri_->ltr ? JUSTIFICATION_LEFT : JUSTIFICATION_RIGHT;
if (curr.GetLineType() == LT_UNKNOWN &&
!FirstWordWouldHaveFit(curr, next, j) &&
(curr.ri_->lword_likely_starts_idea ||
curr.ri_->rword_likely_starts_idea)) {
curr.SetStartLine();
}
}
// Middle rows
for (int i = row_start + 1; i < row_end - 1; i++) {
RowScratchRegisters &prev = (*rows)[i - 1];
RowScratchRegisters &curr = (*rows)[i];
RowScratchRegisters &next = (*rows)[i + 1];
tesseract::ParagraphJustification j =
curr.ri_->ltr ? JUSTIFICATION_LEFT : JUSTIFICATION_RIGHT;
if (curr.GetLineType() == LT_UNKNOWN &&
!FirstWordWouldHaveFit(curr, next, j) &&
LikelyParagraphStart(prev, curr, j)) {
curr.SetStartLine();
}
}
// Last row
{ // the short circuit at the top means we have at least two lines.
RowScratchRegisters &prev = (*rows)[row_end - 2];
RowScratchRegisters &curr = (*rows)[row_end - 1];
tesseract::ParagraphJustification j =
curr.ri_->ltr ? JUSTIFICATION_LEFT : JUSTIFICATION_RIGHT;
if (curr.GetLineType() == LT_UNKNOWN &&
!FirstWordWouldHaveFit(curr, curr, j) &&
LikelyParagraphStart(prev, curr, j)) {
curr.SetStartLine();
}
}
}
// Look for sequences of a start line followed by some body lines in
// rows[row_start, row_end) and create ParagraphModels for them if
// they seem coherent.
static void ModelStrongEvidence(int debug_level,
GenericVector<RowScratchRegisters> *rows,
int row_start, int row_end,
bool allow_flush_models,
ParagraphTheory *theory) {
if (!AcceptableRowArgs(debug_level, 2, __func__, rows, row_start, row_end))
return;
int start = row_start;
while (start < row_end) {
while (start < row_end && (*rows)[start].GetLineType() != LT_START)
start++;
if (start >= row_end - 1)
break;
int tolerance = Epsilon((*rows)[start + 1].ri_->average_interword_space);
int end = start;
ParagraphModel last_model;
bool next_consistent;
do {
++end;
// rows[row, end) was consistent.
// If rows[row, end + 1) is not consistent,
// just model rows[row, end)
if (end < row_end - 1) {
RowScratchRegisters &next = (*rows)[end];
LineType lt = next.GetLineType();
next_consistent = lt == LT_BODY ||
(lt == LT_UNKNOWN &&
!FirstWordWouldHaveFit((*rows)[end - 1], (*rows)[end]));
} else {
next_consistent = false;
}
if (next_consistent) {
ParagraphModel next_model = InternalParagraphModelByOutline(
rows, start, end + 1, tolerance, &next_consistent);
if (((*rows)[start].ri_->ltr &&
last_model.justification() == JUSTIFICATION_LEFT &&
next_model.justification() != JUSTIFICATION_LEFT) ||
(!(*rows)[start].ri_->ltr &&
last_model.justification() == JUSTIFICATION_RIGHT &&
next_model.justification() != JUSTIFICATION_RIGHT)) {
next_consistent = false;
}
last_model = next_model;
} else {
next_consistent = false;
}
} while (next_consistent && end < row_end);
// At this point, rows[start, end) looked like it could have been a
// single paragraph. If we can make a good ParagraphModel for it,
// do so and mark this sequence with that model.
if (end > start + 1) {
// emit a new paragraph if we have more than one line.
const ParagraphModel *model = nullptr;
ParagraphModel new_model = ParagraphModelByOutline(
debug_level, rows, start, end,
Epsilon(InterwordSpace(*rows, start, end)));
if (new_model.justification() == JUSTIFICATION_UNKNOWN) {
// couldn't create a good model, oh well.
} else if (new_model.is_flush()) {
if (end == start + 2) {
// It's very likely we just got two paragraph starts in a row.
end = start + 1;
} else if (start == row_start) {
// Mark this as a Crown.
if (new_model.justification() == JUSTIFICATION_LEFT) {
model = kCrownLeft;
} else {
model = kCrownRight;
}
} else if (allow_flush_models) {
model = theory->AddModel(new_model);
}
} else {
model = theory->AddModel(new_model);
}
if (model) {
(*rows)[start].AddStartLine(model);
for (int i = start + 1; i < end; i++) {
(*rows)[i].AddBodyLine(model);
}
}
}
start = end;
}
}
// We examine rows[row_start, row_end) and do the following:
// (1) Clear all existing hypotheses for the rows being considered.
// (2) Mark up any rows as exceptionally likely to be paragraph starts
// or paragraph body lines as such using both geometric and textual
// clues.
// (3) Form models for any sequence of start + continuation lines.
// (4) Smear the paragraph models to cover surrounding text.
static void StrongEvidenceClassify(int debug_level,
GenericVector<RowScratchRegisters> *rows,
int row_start, int row_end,
ParagraphTheory *theory) {
if (!AcceptableRowArgs(debug_level, 2, __func__, rows, row_start, row_end))
return;
if (debug_level > 1) {
tprintf("#############################################\n");
tprintf("# StrongEvidenceClassify( rows[%d:%d) )\n", row_start, row_end);
tprintf("#############################################\n");
}
RecomputeMarginsAndClearHypotheses(rows, row_start, row_end, 10);
MarkStrongEvidence(rows, row_start, row_end);
DebugDump(debug_level > 2, "Initial strong signals.", *theory, *rows);
// Create paragraph models.
ModelStrongEvidence(debug_level, rows, row_start, row_end, false, theory);
DebugDump(debug_level > 2, "Unsmeared hypotheses.s.", *theory, *rows);
// At this point, some rows are marked up as paragraphs with model numbers,
// and some rows are marked up as either LT_START or LT_BODY. Now let's
// smear any good paragraph hypotheses forward and backward.
ParagraphModelSmearer smearer(rows, row_start, row_end, theory);
smearer.Smear();
}
static void SeparateSimpleLeaderLines(GenericVector<RowScratchRegisters> *rows,
int row_start, int row_end,
ParagraphTheory *theory) {
for (int i = row_start + 1; i < row_end - 1; i++) {
if ((*rows)[i - 1].ri_->has_leaders &&
(*rows)[i].ri_->has_leaders &&
(*rows)[i + 1].ri_->has_leaders) {
const ParagraphModel *model = theory->AddModel(
ParagraphModel(JUSTIFICATION_UNKNOWN, 0, 0, 0, 0));
(*rows)[i].AddStartLine(model);
}
}
}
// Collect sequences of unique hypotheses in row registers and create proper
// paragraphs for them, referencing the paragraphs in row_owners.
static void ConvertHypothesizedModelRunsToParagraphs(
int debug_level,
const GenericVector<RowScratchRegisters> &rows,
GenericVector<PARA *> *row_owners,
ParagraphTheory *theory) {
int end = rows.size();
int start;
for (; end > 0; end = start) {
start = end - 1;
const ParagraphModel *model = nullptr;
// TODO(eger): Be smarter about dealing with multiple hypotheses.
bool single_line_paragraph = false;
SetOfModels models;
rows[start].NonNullHypotheses(&models);
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if (!models.empty()) {
model = models[0];
if (rows[start].GetLineType(model) != LT_BODY)
single_line_paragraph = true;
}
if (model && !single_line_paragraph) {
// walk back looking for more body lines and then a start line.
while (--start > 0 && rows[start].GetLineType(model) == LT_BODY) {
// do nothing
}
if (start < 0 || rows[start].GetLineType(model) != LT_START) {
model = nullptr;
}
}
if (model == nullptr) {
continue;
}
// rows[start, end) should be a paragraph.
PARA *p = new PARA();
if (model == kCrownLeft || model == kCrownRight) {
p->is_very_first_or_continuation = true;
// Crown paragraph.
// If we can find an existing ParagraphModel that fits, use it,
// else create a new one.
for (int row = end; row < rows.size(); row++) {
if ((*row_owners)[row] &&
(ValidBodyLine(&rows, start, (*row_owners)[row]->model) &&
(start == 0 ||
ValidFirstLine(&rows, start, (*row_owners)[row]->model)))) {
model = (*row_owners)[row]->model;
break;
}
}
if (model == kCrownLeft) {
// No subsequent model fits, so cons one up.
model = theory->AddModel(ParagraphModel(
JUSTIFICATION_LEFT, rows[start].lmargin_ + rows[start].lindent_,
0, 0, Epsilon(rows[start].ri_->average_interword_space)));
} else if (model == kCrownRight) {
// No subsequent model fits, so cons one up.
model = theory->AddModel(ParagraphModel(
JUSTIFICATION_RIGHT, rows[start].rmargin_ + rows[start].rmargin_,
0, 0, Epsilon(rows[start].ri_->average_interword_space)));
}
}
rows[start].SetUnknown();
rows[start].AddStartLine(model);
for (int i = start + 1; i < end; i++) {
rows[i].SetUnknown();
rows[i].AddBodyLine(model);
}
p->model = model;
p->has_drop_cap = rows[start].ri_->has_drop_cap;
p->is_list_item =
model->justification() == JUSTIFICATION_RIGHT
? rows[start].ri_->rword_indicates_list_item
: rows[start].ri_->lword_indicates_list_item;
for (int row = start; row < end; row++) {
if ((*row_owners)[row] != nullptr) {
tprintf("Memory leak! ConvertHypothesizeModelRunsToParagraphs() called "
"more than once!\n");
delete (*row_owners)[row];
}
(*row_owners)[row] = p;
}
}
}
struct Interval {
Interval() : begin(0), end(0) {}
Interval(int b, int e) : begin(b), end(e) {}
int begin;
int end;
};
// Return whether rows[row] appears to be stranded, meaning that the evidence
// for this row is very weak due to context. For instance, two lines of source
// code may happen to be indented at the same tab vector as body text starts,
// leading us to think they are two start-of-paragraph lines. This is not
// optimal. However, we also don't want to mark a sequence of short dialog
// as "weak," so our heuristic is:
// (1) If a line is surrounded by lines of unknown type, it's weak.
// (2) If two lines in a row are start lines for a given paragraph type, but
// after that the same paragraph type does not continue, they're weak.
static bool RowIsStranded(const GenericVector<RowScratchRegisters> &rows,
int row) {
SetOfModels row_models;
rows[row].StrongHypotheses(&row_models);
for (int m = 0; m < row_models.size(); m++) {
bool all_starts = rows[row].GetLineType();
int run_length = 1;
bool continues = true;
for (int i = row - 1; i >= 0 && continues; i--) {
SetOfModels models;
rows[i].NonNullHypotheses(&models);
switch (rows[i].GetLineType(row_models[m])) {
case LT_START: run_length++; break;
case LT_MULTIPLE: // explicit fall-through
case LT_BODY: run_length++; all_starts = false; break;
case LT_UNKNOWN: // explicit fall-through
default: continues = false;
}
}
continues = true;
for (int i = row + 1; i < rows.size() && continues; i++) {
SetOfModels models;
rows[i].NonNullHypotheses(&models);
switch (rows[i].GetLineType(row_models[m])) {
case LT_START: run_length++; break;
case LT_MULTIPLE: // explicit fall-through
case LT_BODY: run_length++; all_starts = false; break;
case LT_UNKNOWN: // explicit fall-through
default: continues = false;
}
}
if (run_length > 2 || (!all_starts && run_length > 1)) return false;
}
return true;
}
// Go through rows[row_start, row_end) and gather up sequences that need better
// classification.
// + Sequences of non-empty rows without hypotheses.
// + Crown paragraphs not immediately followed by a strongly modeled line.
// + Single line paragraphs surrounded by text that doesn't match the
// model.
static void LeftoverSegments(const GenericVector<RowScratchRegisters> &rows,
GenericVector<Interval> *to_fix,
int row_start, int row_end) {
to_fix->clear();
for (int i = row_start; i < row_end; i++) {
bool needs_fixing = false;
SetOfModels models;
SetOfModels models_w_crowns;
rows[i].StrongHypotheses(&models);
rows[i].NonNullHypotheses(&models_w_crowns);
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if (models.empty() && !models_w_crowns.empty()) {
// Crown paragraph. Is it followed by a modeled line?
for (int end = i + 1; end < rows.size(); end++) {
SetOfModels end_models;
SetOfModels strong_end_models;
rows[end].NonNullHypotheses(&end_models);
rows[end].StrongHypotheses(&strong_end_models);
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if (end_models.empty()) {
needs_fixing = true;
break;
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} else if (!strong_end_models.empty()) {
needs_fixing = false;
break;
}
}
} else if (models.empty() && rows[i].ri_->num_words > 0) {
// No models at all.
needs_fixing = true;
}
if (!needs_fixing && !models.empty()) {
needs_fixing = RowIsStranded(rows, i);
}
if (needs_fixing) {
if (!to_fix->empty() && to_fix->back().end == i - 1)
to_fix->back().end = i;
else
to_fix->push_back(Interval(i, i));
}
}
// Convert inclusive intervals to half-open intervals.
for (int i = 0; i < to_fix->size(); i++) {
(*to_fix)[i].end = (*to_fix)[i].end + 1;
}
}
// Given a set of row_owners pointing to PARAs or nullptr (no paragraph known),
// normalize each row_owner to point to an actual PARA, and output the
// paragraphs in order onto paragraphs.
void CanonicalizeDetectionResults(
GenericVector<PARA *> *row_owners,
PARA_LIST *paragraphs) {
GenericVector<PARA *> &rows = *row_owners;
paragraphs->clear();
PARA_IT out(paragraphs);
PARA *formerly_null = nullptr;
for (int i = 0; i < rows.size(); i++) {
if (rows[i] == nullptr) {
if (i == 0 || rows[i - 1] != formerly_null) {
rows[i] = formerly_null = new PARA();
} else {
rows[i] = formerly_null;
continue;
}
} else if (i > 0 && rows[i - 1] == rows[i]) {
continue;
}
out.add_after_then_move(rows[i]);
}
}
// Main entry point for Paragraph Detection Algorithm.
//
// Given a set of equally spaced textlines (described by row_infos),
// Split them into paragraphs.
//
// Output:
// row_owners - one pointer for each row, to the paragraph it belongs to.
// paragraphs - this is the actual list of PARA objects.
// models - the list of paragraph models referenced by the PARA objects.
// caller is responsible for deleting the models.
void DetectParagraphs(int debug_level,
GenericVector<RowInfo> *row_infos,
GenericVector<PARA *> *row_owners,
PARA_LIST *paragraphs,
GenericVector<ParagraphModel *> *models) {
GenericVector<RowScratchRegisters> rows;
ParagraphTheory theory(models);
// Initialize row_owners to be a bunch of nullptr pointers.
row_owners->init_to_size(row_infos->size(), nullptr);
// Set up row scratch registers for the main algorithm.
rows.init_to_size(row_infos->size(), RowScratchRegisters());
for (int i = 0; i < row_infos->size(); i++) {
rows[i].Init((*row_infos)[i]);
}
// Pass 1:
// Detect sequences of lines that all contain leader dots (.....)
// These are likely Tables of Contents. If there are three text lines in
// a row with leader dots, it's pretty safe to say the middle one should
// be a paragraph of its own.
SeparateSimpleLeaderLines(&rows, 0, rows.size(), &theory);
DebugDump(debug_level > 1, "End of Pass 1", theory, rows);
GenericVector<Interval> leftovers;
LeftoverSegments(rows, &leftovers, 0, rows.size());
for (int i = 0; i < leftovers.size(); i++) {
// Pass 2a:
// Find any strongly evidenced start-of-paragraph lines. If they're
// followed by two lines that look like body lines, make a paragraph
// model for that and see if that model applies throughout the text
// (that is, "smear" it).
StrongEvidenceClassify(debug_level, &rows,
leftovers[i].begin, leftovers[i].end, &theory);
// Pass 2b:
// If we had any luck in pass 2a, we got part of the page and didn't
// know how to classify a few runs of rows. Take the segments that
// didn't find a model and reprocess them individually.
GenericVector<Interval> leftovers2;
LeftoverSegments(rows, &leftovers2, leftovers[i].begin, leftovers[i].end);
bool pass2a_was_useful = leftovers2.size() > 1 ||
(leftovers2.size() == 1 &&
(leftovers2[0].begin != 0 || leftovers2[0].end != rows.size()));
if (pass2a_was_useful) {
for (int j = 0; j < leftovers2.size(); j++) {
StrongEvidenceClassify(debug_level, &rows,
leftovers2[j].begin, leftovers2[j].end,
&theory);
}
}
}
DebugDump(debug_level > 1, "End of Pass 2", theory, rows);
// Pass 3:
// These are the dregs for which we didn't have enough strong textual
// and geometric clues to form matching models for. Let's see if
// the geometric clues are simple enough that we could just use those.
LeftoverSegments(rows, &leftovers, 0, rows.size());
for (int i = 0; i < leftovers.size(); i++) {
GeometricClassify(debug_level, &rows,
leftovers[i].begin, leftovers[i].end, &theory);
}
// Undo any flush models for which there's little evidence.
DowngradeWeakestToCrowns(debug_level, &theory, &rows);
DebugDump(debug_level > 1, "End of Pass 3", theory, rows);
// Pass 4:
// Take everything that's still not marked up well and clear all markings.
LeftoverSegments(rows, &leftovers, 0, rows.size());
for (int i = 0; i < leftovers.size(); i++) {
for (int j = leftovers[i].begin; j < leftovers[i].end; j++) {
rows[j].SetUnknown();
}
}
DebugDump(debug_level > 1, "End of Pass 4", theory, rows);
// Convert all of the unique hypothesis runs to PARAs.
ConvertHypothesizedModelRunsToParagraphs(debug_level, rows, row_owners,
&theory);
DebugDump(debug_level > 0, "Final Paragraph Segmentation", theory, rows);
// Finally, clean up any dangling nullptr row paragraph parents.
CanonicalizeDetectionResults(row_owners, paragraphs);
}
// ============ Code interfacing with the rest of Tesseract ==================
static void InitializeTextAndBoxesPreRecognition(const MutableIterator &it,
RowInfo *info) {
// Set up text, lword_text, and rword_text (mostly for debug printing).
STRING fake_text;
PageIterator pit(static_cast<const PageIterator&>(it));
bool first_word = true;
if (!pit.Empty(RIL_WORD)) {
do {
fake_text += "x";
if (first_word) info->lword_text += "x";
info->rword_text += "x";
if (pit.IsAtFinalElement(RIL_WORD, RIL_SYMBOL) &&
!pit.IsAtFinalElement(RIL_TEXTLINE, RIL_SYMBOL)) {
fake_text += " ";
info->rword_text = "";
first_word = false;
}
} while (!pit.IsAtFinalElement(RIL_TEXTLINE, RIL_SYMBOL) &&
pit.Next(RIL_SYMBOL));
}
if (fake_text.size() == 0) return;
int lspaces = info->pix_ldistance / info->average_interword_space;
for (int i = 0; i < lspaces; i++) {
info->text += ' ';
}
info->text += fake_text;
// Set up lword_box, rword_box, and num_words.
PAGE_RES_IT page_res_it = *it.PageResIt();
WERD_RES *word_res = page_res_it.restart_row();
ROW_RES *this_row = page_res_it.row();
WERD_RES *lword = nullptr;
WERD_RES *rword = nullptr;
info->num_words = 0;
do {
if (word_res) {
if (!lword) lword = word_res;
if (rword != word_res) info->num_words++;
rword = word_res;
}
word_res = page_res_it.forward();
} while (page_res_it.row() == this_row);
if (lword) info->lword_box = lword->word->bounding_box();
if (rword) info->rword_box = rword->word->bounding_box();
}
// Given a Tesseract Iterator pointing to a text line, fill in the paragraph
// detector RowInfo with all relevant information from the row.
static void InitializeRowInfo(bool after_recognition,
const MutableIterator &it, RowInfo *info) {
if (it.PageResIt()->row() != nullptr) {
ROW *row = it.PageResIt()->row()->row;
info->pix_ldistance = row->lmargin();
info->pix_rdistance = row->rmargin();
info->average_interword_space =
row->space() > 0 ? row->space() : std::max(static_cast<int>(row->x_height()), 1);
info->pix_xheight = row->x_height();
info->has_leaders = false;
info->has_drop_cap = row->has_drop_cap();
info->ltr = true; // set below depending on word scripts
} else {
info->pix_ldistance = info->pix_rdistance = 0;
info->average_interword_space = 1;
info->pix_xheight = 1.0;
info->has_leaders = false;
info->has_drop_cap = false;
info->ltr = true;
}
info->num_words = 0;
info->lword_indicates_list_item = false;
info->lword_likely_starts_idea = false;
info->lword_likely_ends_idea = false;
info->rword_indicates_list_item = false;
info->rword_likely_starts_idea = false;
info->rword_likely_ends_idea = false;
info->has_leaders = false;
info->ltr = true;
if (!after_recognition) {
InitializeTextAndBoxesPreRecognition(it, info);
return;
}
info->text = "";
const std::unique_ptr<const char[]> text(it.GetUTF8Text(RIL_TEXTLINE));
int trailing_ws_idx = strlen(text.get()); // strip trailing space
while (trailing_ws_idx > 0 &&
// isspace() only takes ASCII
isascii(text[trailing_ws_idx - 1]) &&
isspace(text[trailing_ws_idx - 1]))
trailing_ws_idx--;
if (trailing_ws_idx > 0) {
int lspaces = info->pix_ldistance / info->average_interword_space;
for (int i = 0; i < lspaces; i++)
info->text += ' ';
for (int i = 0; i < trailing_ws_idx; i++)
info->text += text[i];
}
if (info->text.size() == 0) {
return;
}
PAGE_RES_IT page_res_it = *it.PageResIt();
GenericVector<WERD_RES *> werds;
WERD_RES *word_res = page_res_it.restart_row();
ROW_RES *this_row = page_res_it.row();
int num_leaders = 0;
int ltr = 0;
int rtl = 0;
do {
if (word_res && word_res->best_choice->unichar_string().length() > 0) {
werds.push_back(word_res);
ltr += word_res->AnyLtrCharsInWord() ? 1 : 0;
rtl += word_res->AnyRtlCharsInWord() ? 1 : 0;
if (word_res->word->flag(W_REP_CHAR)) num_leaders++;
}
word_res = page_res_it.forward();
} while (page_res_it.row() == this_row);
info->ltr = ltr >= rtl;
info->has_leaders = num_leaders > 3;
info->num_words = werds.size();
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if (!werds.empty()) {
WERD_RES *lword = werds[0], *rword = werds[werds.size() - 1];
info->lword_text = lword->best_choice->unichar_string().string();
info->rword_text = rword->best_choice->unichar_string().string();
info->lword_box = lword->word->bounding_box();
info->rword_box = rword->word->bounding_box();
LeftWordAttributes(lword->uch_set, lword->best_choice,
info->lword_text,
&info->lword_indicates_list_item,
&info->lword_likely_starts_idea,
&info->lword_likely_ends_idea);
RightWordAttributes(rword->uch_set, rword->best_choice,
info->rword_text,
&info->rword_indicates_list_item,
&info->rword_likely_starts_idea,
&info->rword_likely_ends_idea);
}
}
// This is called after rows have been identified and words are recognized.
// Much of this could be implemented before word recognition, but text helps
// to identify bulleted lists and gives good signals for sentence boundaries.
void DetectParagraphs(int debug_level,
bool after_text_recognition,
const MutableIterator *block_start,
GenericVector<ParagraphModel *> *models) {
// Clear out any preconceived notions.
if (block_start->Empty(RIL_TEXTLINE)) {
return;
}
BLOCK *block = block_start->PageResIt()->block()->block;
block->para_list()->clear();
bool is_image_block = block->pdblk.poly_block() && !block->pdblk.poly_block()->IsText();
// Convert the Tesseract structures to RowInfos
// for the paragraph detection algorithm.
MutableIterator row(*block_start);
if (row.Empty(RIL_TEXTLINE))
return; // end of input already.
GenericVector<RowInfo> row_infos;
do {
if (!row.PageResIt()->row())
continue; // empty row.
row.PageResIt()->row()->row->set_para(nullptr);
row_infos.push_back(RowInfo());
RowInfo &ri = row_infos.back();
InitializeRowInfo(after_text_recognition, row, &ri);
} while (!row.IsAtFinalElement(RIL_BLOCK, RIL_TEXTLINE) &&
row.Next(RIL_TEXTLINE));
// If we're called before text recognition, we might not have
// tight block bounding boxes, so trim by the minimum on each side.
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if (!row_infos.empty()) {
int min_lmargin = row_infos[0].pix_ldistance;
int min_rmargin = row_infos[0].pix_rdistance;
for (int i = 1; i < row_infos.size(); i++) {
if (row_infos[i].pix_ldistance < min_lmargin)
min_lmargin = row_infos[i].pix_ldistance;
if (row_infos[i].pix_rdistance < min_rmargin)
min_rmargin = row_infos[i].pix_rdistance;
}
if (min_lmargin > 0 || min_rmargin > 0) {
for (int i = 0; i < row_infos.size(); i++) {
row_infos[i].pix_ldistance -= min_lmargin;
row_infos[i].pix_rdistance -= min_rmargin;
}
}
}
// Run the paragraph detection algorithm.
GenericVector<PARA *> row_owners;
GenericVector<PARA *> the_paragraphs;
if (!is_image_block) {
DetectParagraphs(debug_level, &row_infos, &row_owners, block->para_list(),
models);
} else {
row_owners.init_to_size(row_infos.size(), nullptr);
CanonicalizeDetectionResults(&row_owners, block->para_list());
}
// Now stitch in the row_owners into the rows.
row = *block_start;
for (int i = 0; i < row_owners.size(); i++) {
while (!row.PageResIt()->row())
row.Next(RIL_TEXTLINE);
row.PageResIt()->row()->row->set_para(row_owners[i]);
row.Next(RIL_TEXTLINE);
}
}
} // namespace