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https://github.com/tesseract-ocr/tesseract.git
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d8d9b390d1
git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@961 d0cd1f9f-072b-0410-8dd7-cf729c803f20
2578 lines
93 KiB
C++
2578 lines
93 KiB
C++
/**********************************************************************
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* File: paragraphs.cpp
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* Description: Paragraph detection for tesseract.
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* Author: David Eger
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* Created: 25 February 2011
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*
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* (C) Copyright 2011, Google Inc.
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** Licensed under the Apache License, Version 2.0 (the "License");
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** you may not use this file except in compliance with the License.
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** You may obtain a copy of the License at
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** http://www.apache.org/licenses/LICENSE-2.0
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** Unless required by applicable law or agreed to in writing, software
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** distributed under the License is distributed on an "AS IS" BASIS,
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** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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** See the License for the specific language governing permissions and
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** limitations under the License.
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*
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**********************************************************************/
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#ifdef _MSC_VER
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#define __func__ __FUNCTION__
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#endif
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#include <ctype.h>
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#include "genericvector.h"
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#include "helpers.h"
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#include "mutableiterator.h"
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#include "ocrpara.h"
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#include "pageres.h"
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#include "paragraphs.h"
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#include "paragraphs_internal.h"
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#include "publictypes.h"
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#include "ratngs.h"
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#include "rect.h"
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#include "statistc.h"
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#include "strngs.h"
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#include "tprintf.h"
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#include "unicharset.h"
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#include "unicodes.h"
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namespace tesseract {
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// Special "weak" ParagraphModels.
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const ParagraphModel *kCrownLeft
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= reinterpret_cast<ParagraphModel *>(0xDEAD111F);
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const ParagraphModel *kCrownRight
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= reinterpret_cast<ParagraphModel *>(0xDEAD888F);
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// Given the width of a typical space between words, what is the threshold
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// by which by which we think left and right alignments for paragraphs
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// can vary and still be aligned.
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static int Epsilon(int space_pix) {
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return space_pix * 4 / 5;
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}
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static bool AcceptableRowArgs(
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int debug_level, int min_num_rows, const char *function_name,
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const GenericVector<RowScratchRegisters> *rows,
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int row_start, int row_end) {
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if (row_start < 0 || row_end > rows->size() || row_start > row_end) {
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tprintf("Invalid arguments rows[%d, %d) while rows is of size %d.\n",
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row_start, row_end, rows->size());
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return false;
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}
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if (row_end - row_start < min_num_rows) {
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if (debug_level > 1) {
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tprintf("# Too few rows[%d, %d) for %s.\n",
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row_start, row_end, function_name);
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}
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return false;
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}
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return true;
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}
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// =============================== Debug Code ================================
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// Convert an integer to a decimal string.
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static STRING StrOf(int num) {
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char buffer[30];
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snprintf(buffer, sizeof(buffer), "%d", num);
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return STRING(buffer);
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}
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// Given a row-major matrix of unicode text and a column separator, print
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// a formatted table. For ASCII, we get good column alignment.
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static void PrintTable(const GenericVector<GenericVector<STRING> > &rows,
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const STRING &colsep) {
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GenericVector<int> max_col_widths;
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for (int r = 0; r < rows.size(); r++) {
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int num_columns = rows[r].size();
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for (int c = 0; c < num_columns; c++) {
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int num_unicodes = 0;
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for (int i = 0; i < rows[r][c].size(); i++) {
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if ((rows[r][c][i] & 0xC0) != 0x80) num_unicodes++;
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}
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if (c >= max_col_widths.size()) {
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max_col_widths.push_back(num_unicodes);
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} else {
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if (num_unicodes > max_col_widths[c])
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max_col_widths[c] = num_unicodes;
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}
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}
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}
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GenericVector<STRING> col_width_patterns;
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for (int c = 0; c < max_col_widths.size(); c++) {
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col_width_patterns.push_back(
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STRING("%-") + StrOf(max_col_widths[c]) + "s");
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}
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for (int r = 0; r < rows.size(); r++) {
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for (int c = 0; c < rows[r].size(); c++) {
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if (c > 0)
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tprintf("%s", colsep.string());
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tprintf(col_width_patterns[c].string(), rows[r][c].string());
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}
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tprintf("\n");
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}
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}
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STRING RtlEmbed(const STRING &word, bool rtlify) {
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if (rtlify)
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return STRING(kRLE) + word + STRING(kPDF);
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return word;
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}
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// Print the current thoughts of the paragraph detector.
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static void PrintDetectorState(const ParagraphTheory &theory,
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const GenericVector<RowScratchRegisters> &rows) {
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GenericVector<GenericVector<STRING> > output;
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output.push_back(GenericVector<STRING>());
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output.back().push_back("#row");
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output.back().push_back("space");
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output.back().push_back("..");
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output.back().push_back("lword[widthSEL]");
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output.back().push_back("rword[widthSEL]");
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RowScratchRegisters::AppendDebugHeaderFields(&output.back());
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output.back().push_back("text");
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for (int i = 0; i < rows.size(); i++) {
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output.push_back(GenericVector<STRING>());
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GenericVector<STRING> &row = output.back();
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const RowInfo& ri = *rows[i].ri_;
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row.push_back(StrOf(i));
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row.push_back(StrOf(ri.average_interword_space));
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row.push_back(ri.has_leaders ? ".." : " ");
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row.push_back(RtlEmbed(ri.lword_text, !ri.ltr) +
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"[" + StrOf(ri.lword_box.width()) +
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(ri.lword_likely_starts_idea ? "S" : "s") +
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(ri.lword_likely_ends_idea ? "E" : "e") +
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(ri.lword_indicates_list_item ? "L" : "l") +
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"]");
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row.push_back(RtlEmbed(ri.rword_text, !ri.ltr) +
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"[" + StrOf(ri.rword_box.width()) +
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(ri.rword_likely_starts_idea ? "S" : "s") +
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(ri.rword_likely_ends_idea ? "E" : "e") +
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(ri.rword_indicates_list_item ? "L" : "l") +
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"]");
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rows[i].AppendDebugInfo(theory, &row);
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row.push_back(RtlEmbed(ri.text, !ri.ltr));
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}
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PrintTable(output, " ");
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tprintf("Active Paragraph Models:\n");
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for (int m = 0; m < theory.models().size(); m++) {
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tprintf(" %d: %s\n", m + 1, theory.models()[m]->ToString().string());
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}
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}
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static void DebugDump(
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bool should_print,
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const STRING &phase,
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const ParagraphTheory &theory,
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const GenericVector<RowScratchRegisters> &rows) {
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if (!should_print)
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return;
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tprintf("# %s\n", phase.string());
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PrintDetectorState(theory, rows);
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}
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// Print out the text for rows[row_start, row_end)
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static void PrintRowRange(const GenericVector<RowScratchRegisters> &rows,
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int row_start, int row_end) {
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tprintf("======================================\n");
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for (int row = row_start; row < row_end; row++) {
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tprintf("%s\n", rows[row].ri_->text.string());
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}
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tprintf("======================================\n");
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}
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// ============= Brain Dead Language Model (ASCII Version) ===================
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bool IsLatinLetter(int ch) {
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return (ch >= 'a' && ch <= 'z') || (ch >= 'A' && ch <= 'Z');
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}
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bool IsDigitLike(int ch) {
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return ch == 'o' || ch == 'O' || ch == 'l' || ch == 'I';
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}
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bool IsOpeningPunct(int ch) {
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return strchr("'\"({[", ch) != NULL;
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}
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bool IsTerminalPunct(int ch) {
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return strchr(":'\".?!]})", ch) != NULL;
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}
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// Return a pointer after consuming as much text as qualifies as roman numeral.
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const char *SkipChars(const char *str, const char *toskip) {
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while (*str != '\0' && strchr(toskip, *str)) { str++; }
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return str;
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}
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const char *SkipChars(const char *str, bool (*skip)(int)) {
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while (*str != '\0' && skip(*str)) { str++; }
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return str;
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}
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const char *SkipOne(const char *str, const char *toskip) {
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if (*str != '\0' && strchr(toskip, *str)) return str + 1;
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return str;
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}
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// Return whether it is very likely that this is a numeral marker that could
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// start a list item. Some examples include:
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// A I iii. VI (2) 3.5. [C-4]
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bool LikelyListNumeral(const STRING &word) {
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const char *kRomans = "ivxlmdIVXLMD";
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const char *kDigits = "012345789";
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const char *kOpen = "[{(";
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const char *kSep = ":;-.,";
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const char *kClose = "]})";
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int num_segments = 0;
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const char *pos = word.string();
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while (*pos != '\0' && num_segments < 3) {
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// skip up to two open parens.
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const char *numeral_start = SkipOne(SkipOne(pos, kOpen), kOpen);
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const char *numeral_end = SkipChars(numeral_start, kRomans);
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if (numeral_end != numeral_start) {
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// Got Roman Numeral. Great.
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} else {
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numeral_end = SkipChars(numeral_start, kDigits);
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if (numeral_end == numeral_start) {
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// If there's a single latin letter, we can use that.
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numeral_end = SkipChars(numeral_start, IsLatinLetter);
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if (numeral_end - numeral_start != 1)
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break;
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}
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}
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// We got some sort of numeral.
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num_segments++;
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// Skip any trailing parens or punctuation.
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pos = SkipChars(SkipChars(numeral_end, kClose), kSep);
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if (pos == numeral_end)
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break;
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}
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return *pos == '\0';
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}
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bool LikelyListMark(const STRING &word) {
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const char *kListMarks = "0Oo*.,+.";
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return word.size() == 1 && strchr(kListMarks, word[0]) != NULL;
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}
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bool AsciiLikelyListItem(const STRING &word) {
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return LikelyListMark(word) || LikelyListNumeral(word);
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}
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// ========== Brain Dead Language Model (Tesseract Version) ================
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// Return the first Unicode Codepoint from werd[pos].
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int UnicodeFor(const UNICHARSET *u, const WERD_CHOICE *werd, int pos) {
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if (!u || !werd || pos > werd->length())
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return 0;
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return UNICHAR(u->id_to_unichar(werd->unichar_id(pos)), -1).first_uni();
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}
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// A useful helper class for finding the first j >= i so that word[j]
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// does not have given character type.
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class UnicodeSpanSkipper {
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public:
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UnicodeSpanSkipper(const UNICHARSET *unicharset, const WERD_CHOICE *word)
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: u_(unicharset), word_(word) { wordlen_ = word->length(); }
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// Given an input position, return the first position >= pos not punc.
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int SkipPunc(int pos);
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// Given an input position, return the first position >= pos not digit.
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int SkipDigits(int pos);
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// Given an input position, return the first position >= pos not roman.
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int SkipRomans(int pos);
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// Given an input position, return the first position >= pos not alpha.
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int SkipAlpha(int pos);
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private:
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const UNICHARSET *u_;
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const WERD_CHOICE *word_;
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int wordlen_;
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};
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int UnicodeSpanSkipper::SkipPunc(int pos) {
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while (pos < wordlen_ && u_->get_ispunctuation(word_->unichar_id(pos))) pos++;
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return pos;
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}
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int UnicodeSpanSkipper::SkipDigits(int pos) {
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while (pos < wordlen_ && (u_->get_isdigit(word_->unichar_id(pos)) ||
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IsDigitLike(UnicodeFor(u_, word_, pos)))) pos++;
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return pos;
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}
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int UnicodeSpanSkipper::SkipRomans(int pos) {
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const char *kRomans = "ivxlmdIVXLMD";
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while (pos < wordlen_) {
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int ch = UnicodeFor(u_, word_, pos);
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if (ch >= 0xF0 || strchr(kRomans, ch) == 0) break;
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pos++;
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}
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return pos;
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}
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int UnicodeSpanSkipper::SkipAlpha(int pos) {
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while (pos < wordlen_ && u_->get_isalpha(word_->unichar_id(pos))) pos++;
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return pos;
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}
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bool LikelyListMarkUnicode(int ch) {
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if (ch < 0x80) {
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STRING single_ch;
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single_ch += ch;
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return LikelyListMark(single_ch);
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}
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switch (ch) {
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// TODO(eger) expand this list of unicodes as needed.
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case 0x00B0: // degree sign
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case 0x2022: // bullet
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case 0x25E6: // white bullet
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case 0x00B7: // middle dot
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case 0x25A1: // white square
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case 0x25A0: // black square
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case 0x25AA: // black small square
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case 0x2B1D: // black very small square
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case 0x25BA: // black right-pointing pointer
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case 0x25CF: // black circle
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case 0x25CB: // white circle
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return true;
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default:
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break; // fall through
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}
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return false;
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}
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// Return whether it is very likely that this is a numeral marker that could
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// start a list item. Some examples include:
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// A I iii. VI (2) 3.5. [C-4]
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bool UniLikelyListItem(const UNICHARSET *u, const WERD_CHOICE *werd) {
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if (werd->length() == 1 && LikelyListMarkUnicode(UnicodeFor(u, werd, 0)))
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return true;
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UnicodeSpanSkipper m(u, werd);
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int num_segments = 0;
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int pos = 0;
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while (pos < werd->length() && num_segments < 3) {
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int numeral_start = m.SkipPunc(pos);
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if (numeral_start > pos + 1) break;
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int numeral_end = m.SkipRomans(numeral_start);
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if (numeral_end == numeral_start) {
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numeral_end = m.SkipDigits(numeral_start);
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if (numeral_end == numeral_start) {
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// If there's a single latin letter, we can use that.
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numeral_end = m.SkipAlpha(numeral_start);
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if (numeral_end - numeral_start != 1)
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break;
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}
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}
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// We got some sort of numeral.
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num_segments++;
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// Skip any trailing punctuation.
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pos = m.SkipPunc(numeral_end);
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if (pos == numeral_end)
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break;
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}
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return pos == werd->length();
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}
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// ========= Brain Dead Language Model (combined entry points) ================
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// Given the leftmost word of a line either as a Tesseract unicharset + werd
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// or a utf8 string, set the following attributes for it:
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// is_list - this word might be a list number or bullet.
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// starts_idea - this word is likely to start a sentence.
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// ends_idea - this word is likely to end a sentence.
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void LeftWordAttributes(const UNICHARSET *unicharset, const WERD_CHOICE *werd,
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const STRING &utf8,
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bool *is_list, bool *starts_idea, bool *ends_idea) {
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*is_list = false;
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*starts_idea = false;
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*ends_idea = false;
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if (utf8.size() == 0 || (werd != NULL && werd->length() == 0)) { // Empty
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*ends_idea = true;
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return;
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}
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if (unicharset && werd) { // We have a proper werd and unicharset so use it.
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if (UniLikelyListItem(unicharset, werd)) {
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*is_list = true;
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*starts_idea = true;
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*ends_idea = true;
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}
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if (unicharset->get_isupper(werd->unichar_id(0))) {
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*starts_idea = true;
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}
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if (unicharset->get_ispunctuation(werd->unichar_id(0))) {
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*starts_idea = true;
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*ends_idea = true;
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}
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} else { // Assume utf8 is mostly ASCII
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if (AsciiLikelyListItem(utf8)) {
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*is_list = true;
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*starts_idea = true;
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}
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int start_letter = utf8[0];
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if (IsOpeningPunct(start_letter)) {
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*starts_idea = true;
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}
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if (IsTerminalPunct(start_letter)) {
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*ends_idea = true;
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}
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if (start_letter >= 'A' && start_letter <= 'Z') {
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*starts_idea = true;
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}
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}
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}
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// Given the rightmost word of a line either as a Tesseract unicharset + werd
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// or a utf8 string, set the following attributes for it:
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// is_list - this word might be a list number or bullet.
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// starts_idea - this word is likely to start a sentence.
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// ends_idea - this word is likely to end a sentence.
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void RightWordAttributes(const UNICHARSET *unicharset, const WERD_CHOICE *werd,
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const STRING &utf8,
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bool *is_list, bool *starts_idea, bool *ends_idea) {
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*is_list = false;
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*starts_idea = false;
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*ends_idea = false;
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if (utf8.size() == 0 || (werd != NULL && werd->length() == 0)) { // Empty
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*ends_idea = true;
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return;
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}
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if (unicharset && werd) { // We have a proper werd and unicharset so use it.
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if (UniLikelyListItem(unicharset, werd)) {
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*is_list = true;
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*starts_idea = true;
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}
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UNICHAR_ID last_letter = werd->unichar_id(werd->length() - 1);
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if (unicharset->get_ispunctuation(last_letter)) {
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*ends_idea = true;
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}
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} else { // Assume utf8 is mostly ASCII
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if (AsciiLikelyListItem(utf8)) {
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|
*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 == NULL)
|
|
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, NULL));
|
|
}
|
|
}
|
|
|
|
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, NULL));
|
|
}
|
|
}
|
|
|
|
void RowScratchRegisters::AddStartLine(const ParagraphModel *model) {
|
|
hypotheses_.push_back_new(LineHypothesis(LT_START, model));
|
|
int old_idx = hypotheses_.get_index(LineHypothesis(LT_START, NULL));
|
|
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, NULL));
|
|
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 != NULL)
|
|
models->push_back_new(hypotheses_[h].model);
|
|
}
|
|
}
|
|
|
|
const ParagraphModel *RowScratchRegisters::UniqueStartHypothesis() const {
|
|
if (hypotheses_.size() != 1 || hypotheses_[0].ty != LT_START)
|
|
return NULL;
|
|
return hypotheses_[0].model;
|
|
}
|
|
|
|
const ParagraphModel *RowScratchRegisters::UniqueBodyHypothesis() const {
|
|
if (hypotheses_.size() != 1 || hypotheses_[0].ty != LT_BODY)
|
|
return NULL;
|
|
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.
|
|
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.
|
|
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.
|
|
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
|
|
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.
|
|
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];
|
|
}
|
|
ParagraphModel *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 NULL.
|
|
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 NULL;
|
|
}
|
|
|
|
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.
|
|
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.
|
|
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 = NULL;
|
|
while (end > 0 &&
|
|
(model = (*rows)[end - 1].UniqueBodyHypothesis()) == NULL) {
|
|
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 alignemnt.
|
|
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;
|
|
}
|
|
|
|
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;
|
|
}
|
|
}
|
|
|
|
bool LikelyParagraphStart(const RowScratchRegisters &before,
|
|
const RowScratchRegisters &after) {
|
|
return before.ri_->num_words == 0 ||
|
|
(FirstWordWouldHaveFit(before, after) &&
|
|
TextSupportsBreak(before, after));
|
|
}
|
|
|
|
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.
|
|
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.
|
|
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.
|
|
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.
|
|
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 = NULL;
|
|
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.
|
|
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();
|
|
}
|
|
|
|
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.
|
|
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 = NULL;
|
|
// TODO(eger): Be smarter about dealing with multiple hypotheses.
|
|
bool single_line_paragraph = false;
|
|
SetOfModels models;
|
|
rows[start].NonNullHypotheses(&models);
|
|
if (models.size() > 0) {
|
|
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 = NULL;
|
|
}
|
|
}
|
|
if (model == NULL) {
|
|
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] != NULL) {
|
|
tprintf("Memory leak! ConvertHypothesizeModelRunsToParagraphs() called "
|
|
"more than once!\n");
|
|
}
|
|
(*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.
|
|
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.
|
|
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);
|
|
if (models.empty() && models_w_crowns.size() > 0) {
|
|
// 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);
|
|
if (end_models.size() == 0) {
|
|
needs_fixing = true;
|
|
break;
|
|
} else if (strong_end_models.size() > 0) {
|
|
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 NULL (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 = NULL;
|
|
for (int i = 0; i < rows.size(); i++) {
|
|
if (rows[i] == NULL) {
|
|
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 NULL pointers.
|
|
row_owners->init_to_size(row_infos->size(), NULL);
|
|
|
|
// 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 NULL row paragraph parents.
|
|
CanonicalizeDetectionResults(row_owners, paragraphs);
|
|
}
|
|
|
|
// ============ Code interfacing with the rest of Tesseract ==================
|
|
|
|
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 = NULL;
|
|
WERD_RES *rword = NULL;
|
|
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);
|
|
info->lword_box = lword->word->bounding_box();
|
|
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.
|
|
void InitializeRowInfo(bool after_recognition,
|
|
const MutableIterator &it,
|
|
RowInfo *info) {
|
|
if (it.PageResIt()->row() != NULL) {
|
|
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() : MAX(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 = 1;
|
|
|
|
if (!after_recognition) {
|
|
InitializeTextAndBoxesPreRecognition(it, info);
|
|
return;
|
|
}
|
|
info->text = "";
|
|
char *text = it.GetUTF8Text(RIL_TEXTLINE);
|
|
int trailing_ws_idx = strlen(text); // strip trailing space
|
|
while (trailing_ws_idx > 0 &&
|
|
// isspace() only takes ASCII
|
|
((text[trailing_ws_idx - 1] & 0x80) == 0) &&
|
|
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];
|
|
}
|
|
delete []text;
|
|
|
|
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();
|
|
if (werds.size() > 0) {
|
|
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->poly_block() && !block->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(NULL);
|
|
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.
|
|
if (row_infos.size() > 0) {
|
|
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(), NULL);
|
|
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
|