/**********************************************************************
 * File:        applybox.cpp  (Formerly applybox.c)
 * Description: Re segment rows according to box file data
 * Author:      Phil Cheatle
 * Created:     Wed Nov 24 09:11:23 GMT 1993
 *
 * (C) Copyright 1993, Hewlett-Packard Ltd.
 ** Licensed under the Apache License, Version 2.0 (the "License");
 ** you may not use this file except in compliance with the License.
 ** You may obtain a copy of the License at
 ** http://www.apache.org/licenses/LICENSE-2.0
 ** Unless required by applicable law or agreed to in writing, software
 ** distributed under the License is distributed on an "AS IS" BASIS,
 ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 ** See the License for the specific language governing permissions and
 ** limitations under the License.
 *
 **********************************************************************/

#ifdef _MSC_VER
#pragma warning(disable:4244)  // Conversion warnings
#endif

#include <ctype.h>
#include <string.h>
#ifdef __UNIX__
#include <assert.h>
#include <errno.h>
#endif
#include "allheaders.h"
#include "boxread.h"
#include "chopper.h"
#include "pageres.h"
#include "unichar.h"
#include "unicharset.h"
#include "tesseractclass.h"
#include "genericvector.h"

// Max number of blobs to classify together in FindSegmentation.
const int kMaxGroupSize = 4;
// Max fraction of median allowed as deviation in xheight before switching
// to median.
const double kMaxXHeightDeviationFraction = 0.125;

/*************************************************************************
 * The box file is assumed to contain box definitions, one per line, of the
 * following format for blob-level boxes:
 *   <UTF8 str> <left> <bottom> <right> <top> <page id>
 * and for word/line-level boxes:
 *   WordStr <left> <bottom> <right> <top> <page id> #<space-delimited word str>
 * NOTES:
 * The boxes use tesseract coordinates, i.e. 0,0 is at BOTTOM-LEFT.
 *
 * <page id> is 0-based, and the page number is used for multipage input (tiff).
 *
 * In the blob-level form, each line represents a recognizable unit, which may
 * be several UTF-8 bytes, but there is a bounding box around each recognizable
 * unit, and no classifier is needed to train in this mode (bootstrapping.)
 *
 * In the word/line-level form, the line begins with the literal "WordStr", and
 * the bounding box bounds either a whole line or a whole word. The recognizable
 * units in the word/line are listed after the # at the end of the line and
 * are space delimited, ignoring any original spaces on the line.
 * Eg.
 * word -> #w o r d
 * multi word line -> #m u l t i w o r d l i n e
 * The recognizable units must be space-delimited in order to allow multiple
 * unicodes to be used for a single recognizable unit, eg Hindi.
 * In this mode, the classifier must have been pre-trained with the desired
 * character set, or it will not be able to find the character segmentations.
 *************************************************************************/

namespace tesseract {

static void clear_any_old_text(BLOCK_LIST *block_list) {
  BLOCK_IT block_it(block_list);
  for (block_it.mark_cycle_pt();
       !block_it.cycled_list(); block_it.forward()) {
    ROW_IT row_it(block_it.data()->row_list());
    for (row_it.mark_cycle_pt(); !row_it.cycled_list(); row_it.forward()) {
      WERD_IT word_it(row_it.data()->word_list());
      for (word_it.mark_cycle_pt();
           !word_it.cycled_list(); word_it.forward()) {
        word_it.data()->set_text("");
      }
    }
  }
}

// Applies the box file based on the image name fname, and resegments
// the words in the block_list (page), with:
// blob-mode: one blob per line in the box file, words as input.
// word/line-mode: one blob per space-delimited unit after the #, and one word
// per line in the box file. (See comment above for box file format.)
// If find_segmentation is true, (word/line mode) then the classifier is used
// to re-segment words/lines to match the space-delimited truth string for
// each box. In this case, the input box may be for a word or even a whole
// text line, and the output words will contain multiple blobs corresponding
// to the space-delimited input string.
// With find_segmentation false, no classifier is needed, but the chopper
// can still be used to correctly segment touching characters with the help
// of the input boxes.
// In the returned PAGE_RES, the WERD_RES are setup as they would be returned
// from normal classification, ie. with a word, chopped_word, rebuild_word,
// seam_array, denorm, box_word, and best_state, but NO best_choice or
// raw_choice, as they would require a UNICHARSET, which we aim to avoid.
// Instead, the correct_text member of WERD_RES is set, and this may be later
// converted to a best_choice using CorrectClassifyWords. CorrectClassifyWords
// is not required before calling ApplyBoxTraining.
PAGE_RES* Tesseract::ApplyBoxes(const STRING& fname,
                                bool find_segmentation,
                                BLOCK_LIST *block_list) {
  int box_count = 0;
  int box_failures = 0;

  FILE* box_file = OpenBoxFile(fname);
  TBOX box;
  GenericVector<TBOX> boxes;
  GenericVector<STRING> texts, full_texts;

  bool found_box = true;
  while (found_box) {
    int line_number = 0;           // Line number of the box file.
    STRING text, full_text;
    found_box = ReadNextBox(applybox_page, &line_number, box_file, &text, &box);
    if (found_box) {
      ++box_count;
      MakeBoxFileStr(text.string(), box, applybox_page, &full_text);
    } else {
      full_text = "";
    }
    boxes.push_back(box);
    texts.push_back(text);
    full_texts.push_back(full_text);
  }

  // In word mode, we use the boxes to make a word for each box, but
  // in blob mode we use the existing words and maximally chop them first.
  PAGE_RES* page_res = find_segmentation ?
      NULL : SetupApplyBoxes(boxes, block_list);
  clear_any_old_text(block_list);

  for (int i = 0; i < boxes.size() - 1; i++) {
    bool foundit = false;
    if (page_res != NULL) {
      if (i == 0) {
        foundit = ResegmentCharBox(page_res, NULL, boxes[i], boxes[i + 1],
                                   full_texts[i].string());
      } else {
        foundit = ResegmentCharBox(page_res, &boxes[i-1], boxes[i],
                                   boxes[i + 1], full_texts[i].string());
      }
    } else {
      foundit = ResegmentWordBox(block_list, boxes[i], boxes[i + 1],
                                 texts[i].string());
    }
    if (!foundit) {
      box_failures++;
      ReportFailedBox(i, boxes[i], texts[i].string(),
                      "FAILURE! Couldn't find a matching blob");
    }
  }

  if (page_res == NULL) {
    // In word/line mode, we now maximally chop all the words and resegment
    // them with the classifier.
    page_res = SetupApplyBoxes(boxes, block_list);
    ReSegmentByClassification(page_res);
  }
  if (applybox_debug > 0) {
    tprintf("APPLY_BOXES:\n");
    tprintf("   Boxes read from boxfile:  %6d\n", box_count);
    if (box_failures > 0)
      tprintf("   Boxes failed resegmentation:  %6d\n", box_failures);
  }
  TidyUp(page_res);
  return page_res;
}

// Helper computes median xheight in the image.
static double MedianXHeight(BLOCK_LIST *block_list) {
  BLOCK_IT block_it(block_list);
  STATS xheights(0, block_it.data()->bounding_box().height());
  for (block_it.mark_cycle_pt();
       !block_it.cycled_list(); block_it.forward()) {
    ROW_IT row_it(block_it.data()->row_list());
    for (row_it.mark_cycle_pt(); !row_it.cycled_list(); row_it.forward()) {
      xheights.add(IntCastRounded(row_it.data()->x_height()), 1);
    }
  }
  return xheights.median();
}

// Any row xheight that is significantly different from the median is set
// to the median.
void Tesseract::PreenXHeights(BLOCK_LIST *block_list) {
  double median_xheight = MedianXHeight(block_list);
  double max_deviation = kMaxXHeightDeviationFraction * median_xheight;
  // Strip all fuzzy space markers to simplify the PAGE_RES.
  BLOCK_IT b_it(block_list);
  for (b_it.mark_cycle_pt(); !b_it.cycled_list(); b_it.forward()) {
    BLOCK* block = b_it.data();
    ROW_IT r_it(block->row_list());
    for (r_it.mark_cycle_pt(); !r_it.cycled_list(); r_it.forward ()) {
      ROW* row = r_it.data();
      float diff = fabs(row->x_height() - median_xheight);
      if (diff > max_deviation) {
        if (applybox_debug) {
          tprintf("row xheight=%g, but median xheight = %g\n",
                  row->x_height(), median_xheight);
        }
        row->set_x_height(static_cast<float>(median_xheight));
      }
    }
  }
}

// Builds a PAGE_RES from the block_list in the way required for ApplyBoxes:
// All fuzzy spaces are removed, and all the words are maximally chopped.
PAGE_RES* Tesseract::SetupApplyBoxes(const GenericVector<TBOX>& boxes,
                                     BLOCK_LIST *block_list) {
  PreenXHeights(block_list);
  // Strip all fuzzy space markers to simplify the PAGE_RES.
  BLOCK_IT b_it(block_list);
  for (b_it.mark_cycle_pt(); !b_it.cycled_list(); b_it.forward()) {
    BLOCK* block = b_it.data();
    ROW_IT r_it(block->row_list());
    for (r_it.mark_cycle_pt(); !r_it.cycled_list(); r_it.forward ()) {
      ROW* row = r_it.data();
      WERD_IT w_it(row->word_list());
      for (w_it.mark_cycle_pt(); !w_it.cycled_list(); w_it.forward()) {
        WERD* word = w_it.data();
        if (word->cblob_list()->empty()) {
          delete w_it.extract();
        } else {
          word->set_flag(W_FUZZY_SP, false);
          word->set_flag(W_FUZZY_NON, false);
        }
      }
    }
  }
  PAGE_RES* page_res = new PAGE_RES(block_list, NULL);
  PAGE_RES_IT pr_it(page_res);
  WERD_RES* word_res;
  while ((word_res = pr_it.word()) != NULL) {
    MaximallyChopWord(boxes, pr_it.block()->block,
                      pr_it.row()->row, word_res);
    pr_it.forward();
  }
  return page_res;
}

// Tests the chopper by exhaustively running chop_one_blob.
// The word_res will contain filled chopped_word, seam_array, denorm,
// box_word and best_state for the maximally chopped word.
void Tesseract::MaximallyChopWord(const GenericVector<TBOX>& boxes,
                                  BLOCK* block, ROW* row,
                                  WERD_RES* word_res) {
  if (!word_res->SetupForRecognition(unicharset, this, BestPix(),
                                     tessedit_ocr_engine_mode, NULL,
                                     classify_bln_numeric_mode,
                                     textord_use_cjk_fp_model,
                                     poly_allow_detailed_fx,
                                     row, block)) {
    word_res->CloneChoppedToRebuild();
    return;
  }
  if (chop_debug) {
    tprintf("Maximally chopping word at:");
    word_res->word->bounding_box().print();
  }
  GenericVector<BLOB_CHOICE*> blob_choices;
  ASSERT_HOST(!word_res->chopped_word->blobs.empty());
  float rating = static_cast<float>(MAX_INT8);
  for (int i = 0; i < word_res->chopped_word->NumBlobs(); ++i) {
    // The rating and certainty are not quite arbitrary. Since
    // select_blob_to_chop uses the worst certainty to choose, they all have
    // to be different, so starting with MAX_INT8, subtract 1/8 for each blob
    // in here, and then divide by e each time they are chopped, which
    // should guarantee a set of unequal values for the whole tree of blobs
    // produced, however much chopping is required. The chops are thus only
    // limited by the ability of the chopper to find suitable chop points,
    // and not by the value of the certainties.
    BLOB_CHOICE* choice =
        new BLOB_CHOICE(0, rating, -rating, -1, -1, 0, 0, 0, 0, BCC_FAKE);
    blob_choices.push_back(choice);
    rating -= 0.125f;
  }
  const double e = exp(1.0);  // The base of natural logs.
  int blob_number;
  int right_chop_index = 0;
  if (!assume_fixed_pitch_char_segment) {
    // We only chop if the language is not fixed pitch like CJK.
    SEAM* seam = NULL;
    while ((seam = chop_one_blob(boxes, blob_choices, word_res,
                                 &blob_number)) != NULL) {
      word_res->InsertSeam(blob_number, seam);
      BLOB_CHOICE* left_choice = blob_choices[blob_number];
      rating = left_choice->rating() / e;
      left_choice->set_rating(rating);
      left_choice->set_certainty(-rating);
      // combine confidence w/ serial #
      BLOB_CHOICE* right_choice = new BLOB_CHOICE(++right_chop_index,
                                                  rating - 0.125f, -rating,
                                                  -1, -1, 0, 0, 0, 0, BCC_FAKE);
      blob_choices.insert(right_choice, blob_number + 1);
    }
  }
  word_res->CloneChoppedToRebuild();
  word_res->FakeClassifyWord(blob_choices.size(), &blob_choices[0]);
}

// Helper to compute the dispute resolution metric.
// Disputed blob resolution. The aim is to give the blob to the most
// appropriate boxfile box. Most of the time it is obvious, but if
// two boxfile boxes overlap significantly it is not. If a small boxfile
// box takes most of the blob, and a large boxfile box does too, then
// we want the small boxfile box to get it, but if the small box
// is much smaller than the blob, we don't want it to get it.
// Details of the disputed blob resolution:
// Given a box with area A, and a blob with area B, with overlap area C,
// then the miss metric is (A-C)(B-C)/(AB) and the box with minimum
// miss metric gets the blob.
static double BoxMissMetric(const TBOX& box1, const TBOX& box2) {
  int overlap_area = box1.intersection(box2).area();
  double miss_metric = box1.area()- overlap_area;
  miss_metric /= box1.area();
  miss_metric *= box2.area() - overlap_area;
  miss_metric /= box2.area();
  return miss_metric;
}

// Gather consecutive blobs that match the given box into the best_state
// and corresponding correct_text.
// Fights over which box owns which blobs are settled by pre-chopping and
// applying the blobs to box or next_box with the least non-overlap.
// Returns false if the box was in error, which can only be caused by
// failing to find an appropriate blob for a box.
// This means that occasionally, blobs may be incorrectly segmented if the
// chopper fails to find a suitable chop point.
bool Tesseract::ResegmentCharBox(PAGE_RES* page_res, const TBOX *prev_box,
                                 const TBOX& box, const TBOX& next_box,
                                 const char* correct_text) {
  if (applybox_debug > 1) {
    tprintf("\nAPPLY_BOX: in ResegmentCharBox() for %s\n", correct_text);
  }
  PAGE_RES_IT page_res_it(page_res);
  WERD_RES* word_res;
  for (word_res = page_res_it.word(); word_res != NULL;
       word_res = page_res_it.forward()) {
    if (!word_res->box_word->bounding_box().major_overlap(box))
      continue;
    if (applybox_debug > 1) {
      tprintf("Checking word box:");
      word_res->box_word->bounding_box().print();
    }
    int word_len = word_res->box_word->length();
    for (int i = 0; i < word_len; ++i) {
      TBOX char_box = TBOX();
      int blob_count = 0;
      for (blob_count = 0; i + blob_count < word_len; ++blob_count) {
        TBOX blob_box = word_res->box_word->BlobBox(i + blob_count);
        if (!blob_box.major_overlap(box))
          break;
        if (word_res->correct_text[i + blob_count].length() > 0)
          break;  // Blob is claimed already.
        double current_box_miss_metric = BoxMissMetric(blob_box, box);
        double next_box_miss_metric = BoxMissMetric(blob_box, next_box);
        if (applybox_debug > 2) {
          tprintf("Checking blob:");
          blob_box.print();
          tprintf("Current miss metric = %g, next = %g\n",
                  current_box_miss_metric, next_box_miss_metric);
        }
        if (current_box_miss_metric > next_box_miss_metric)
          break;  // Blob is a better match for next box.
        char_box += blob_box;
      }
      if (blob_count > 0) {
        if (applybox_debug > 1) {
          tprintf("Index [%d, %d) seem good.\n", i, i + blob_count);
        }
        if (!char_box.almost_equal(box, 3) &&
            (box.x_gap(next_box) < -3 ||
             (prev_box != NULL && prev_box->x_gap(box) < -3))) {
          return false;
        }
        // We refine just the box_word, best_state and correct_text here.
        // The rebuild_word is made in TidyUp.
        // blob_count blobs are put together to match the box. Merge the
        // box_word boxes, save the blob_count in the state and the text.
        word_res->box_word->MergeBoxes(i, i + blob_count);
        word_res->best_state[i] = blob_count;
        word_res->correct_text[i] = correct_text;
        if (applybox_debug > 2) {
          tprintf("%d Blobs match: blob box:", blob_count);
          word_res->box_word->BlobBox(i).print();
          tprintf("Matches box:");
          box.print();
          tprintf("With next box:");
          next_box.print();
        }
        // Eliminated best_state and correct_text entries for the consumed
        // blobs.
        for (int j = 1; j < blob_count; ++j) {
          word_res->best_state.remove(i + 1);
          word_res->correct_text.remove(i + 1);
        }
        // Assume that no box spans multiple source words, so we are done with
        // this box.
        if (applybox_debug > 1) {
          tprintf("Best state = ");
          for (int j = 0; j < word_res->best_state.size(); ++j) {
            tprintf("%d ", word_res->best_state[j]);
          }
          tprintf("\n");
          tprintf("Correct text = [[ ");
          for (int j = 0; j < word_res->correct_text.size(); ++j) {
            tprintf("%s ", word_res->correct_text[j].string());
          }
          tprintf("]]\n");
        }
        return true;
      }
    }
  }
  if (applybox_debug > 0) {
    tprintf("FAIL!\n");
  }
  return false;  // Failure.
}

// Consume all source blobs that strongly overlap the given box,
// putting them into a new word, with the correct_text label.
// Fights over which box owns which blobs are settled by
// applying the blobs to box or next_box with the least non-overlap.
// Returns false if the box was in error, which can only be caused by
// failing to find an overlapping blob for a box.
bool Tesseract::ResegmentWordBox(BLOCK_LIST *block_list,
                                 const TBOX& box, const TBOX& next_box,
                                 const char* correct_text) {
  if (applybox_debug > 1) {
    tprintf("\nAPPLY_BOX: in ResegmentWordBox() for %s\n", correct_text);
  }
  WERD* new_word = NULL;
  BLOCK_IT b_it(block_list);
  for (b_it.mark_cycle_pt(); !b_it.cycled_list(); b_it.forward()) {
    BLOCK* block = b_it.data();
    if (!box.major_overlap(block->bounding_box()))
      continue;
    ROW_IT r_it(block->row_list());
    for (r_it.mark_cycle_pt(); !r_it.cycled_list(); r_it.forward()) {
      ROW* row = r_it.data();
      if (!box.major_overlap(row->bounding_box()))
        continue;
      WERD_IT w_it(row->word_list());
      for (w_it.mark_cycle_pt(); !w_it.cycled_list(); w_it.forward()) {
        WERD* word = w_it.data();
        if (applybox_debug > 2) {
          tprintf("Checking word:");
          word->bounding_box().print();
        }
        if (word->text() != NULL && word->text()[0] != '\0')
          continue;  // Ignore words that are already done.
        if (!box.major_overlap(word->bounding_box()))
          continue;
        C_BLOB_IT blob_it(word->cblob_list());
        for (blob_it.mark_cycle_pt(); !blob_it.cycled_list();
             blob_it.forward()) {
          C_BLOB* blob = blob_it.data();
          TBOX blob_box = blob->bounding_box();
          if (!blob_box.major_overlap(box))
            continue;
          double current_box_miss_metric = BoxMissMetric(blob_box, box);
          double next_box_miss_metric = BoxMissMetric(blob_box, next_box);
          if (applybox_debug > 2) {
            tprintf("Checking blob:");
            blob_box.print();
            tprintf("Current miss metric = %g, next = %g\n",
                    current_box_miss_metric, next_box_miss_metric);
          }
          if (current_box_miss_metric > next_box_miss_metric)
            continue;  // Blob is a better match for next box.
          if (applybox_debug > 2) {
            tprintf("Blob match: blob:");
            blob_box.print();
            tprintf("Matches box:");
            box.print();
            tprintf("With next box:");
            next_box.print();
          }
          if (new_word == NULL) {
            // Make a new word with a single blob.
            new_word = word->shallow_copy();
            new_word->set_text(correct_text);
            w_it.add_to_end(new_word);
          }
          C_BLOB_IT new_blob_it(new_word->cblob_list());
          new_blob_it.add_to_end(blob_it.extract());
        }
      }
    }
  }
  if (new_word == NULL && applybox_debug > 0) tprintf("FAIL!\n");
  return new_word != NULL;
}

// Resegments the words by running the classifier in an attempt to find the
// correct segmentation that produces the required string.
void Tesseract::ReSegmentByClassification(PAGE_RES* page_res) {
  PAGE_RES_IT pr_it(page_res);
  WERD_RES* word_res;
  for (; (word_res = pr_it.word()) != NULL; pr_it.forward()) {
    WERD* word = word_res->word;
    if (word->text() == NULL || word->text()[0] == '\0')
      continue;  // Ignore words that have no text.
    // Convert the correct text to a vector of UNICHAR_ID
    GenericVector<UNICHAR_ID> target_text;
    if (!ConvertStringToUnichars(word->text(), &target_text)) {
      tprintf("APPLY_BOX: FAILURE: can't find class_id for '%s'\n",
              word->text());
      pr_it.DeleteCurrentWord();
      continue;
    }
    if (!FindSegmentation(target_text, word_res)) {
      tprintf("APPLY_BOX: FAILURE: can't find segmentation for '%s'\n",
              word->text());
      pr_it.DeleteCurrentWord();
      continue;
    }
  }
}

// Converts the space-delimited string of utf8 text to a vector of UNICHAR_ID.
// Returns false if an invalid UNICHAR_ID is encountered.
bool Tesseract::ConvertStringToUnichars(const char* utf8,
                                        GenericVector<UNICHAR_ID>* class_ids) {
  for (int step = 0; *utf8 != '\0'; utf8 += step) {
    const char* next_space = strchr(utf8, ' ');
    if (next_space == NULL)
      next_space = utf8 + strlen(utf8);
    step = next_space - utf8;
    UNICHAR_ID class_id = unicharset.unichar_to_id(utf8, step);
    if (class_id == INVALID_UNICHAR_ID) {
      return false;
    }
    while (utf8[step] == ' ')
      ++step;
    class_ids->push_back(class_id);
  }
  return true;
}

// Resegments the word to achieve the target_text from the classifier.
// Returns false if the re-segmentation fails.
// Uses brute-force combination of up to kMaxGroupSize adjacent blobs, and
// applies a full search on the classifier results to find the best classified
// segmentation. As a compromise to obtain better recall, 1-1 ambiguity
// substitutions ARE used.
bool Tesseract::FindSegmentation(const GenericVector<UNICHAR_ID>& target_text,
                                 WERD_RES* word_res) {
  // Classify all required combinations of blobs and save results in choices.
  int word_length = word_res->box_word->length();
  GenericVector<BLOB_CHOICE_LIST*>* choices =
      new GenericVector<BLOB_CHOICE_LIST*>[word_length];
  for (int i = 0; i < word_length; ++i) {
    for (int j = 1; j <= kMaxGroupSize && i + j <= word_length; ++j) {
      BLOB_CHOICE_LIST* match_result = classify_piece(
          word_res->seam_array, i, i + j - 1, "Applybox",
          word_res->chopped_word, word_res->blamer_bundle);
      if (applybox_debug > 2) {
        tprintf("%d+%d:", i, j);
        print_ratings_list("Segment:", match_result, unicharset);
      }
      choices[i].push_back(match_result);
    }
  }
  // Search the segmentation graph for the target text. Must be an exact
  // match. Using wildcards makes it difficult to find the correct
  // segmentation even when it is there.
  word_res->best_state.clear();
  GenericVector<int> search_segmentation;
  float best_rating = 0.0f;
  SearchForText(choices, 0, word_length, target_text, 0, 0.0f,
                &search_segmentation, &best_rating, &word_res->best_state);
  for (int i = 0; i < word_length; ++i)
    choices[i].delete_data_pointers();
  delete [] choices;
  if (word_res->best_state.empty()) {
    // Build the original segmentation and if it is the same length as the
    // truth, assume it will do.
    int blob_count = 1;
    for (int s = 0; s < word_res->seam_array.size(); ++s) {
      SEAM* seam = word_res->seam_array[s];
      if (seam->split1 == NULL) {
        word_res->best_state.push_back(blob_count);
        blob_count = 1;
      } else {
        ++blob_count;
      }
    }
    word_res->best_state.push_back(blob_count);
    if (word_res->best_state.size() != target_text.size()) {
      word_res->best_state.clear();  // No good. Original segmentation bad size.
      return false;
    }
  }
  word_res->correct_text.clear();
  for (int i = 0; i < target_text.size(); ++i) {
    word_res->correct_text.push_back(
        STRING(unicharset.id_to_unichar(target_text[i])));
  }
  return true;
}

// Recursive helper to find a match to the target_text (from text_index
// position) in the choices (from choices_pos position).
// Choices is an array of GenericVectors, of length choices_length, with each
// element representing a starting position in the word, and the
// GenericVector holding classification results for a sequence of consecutive
// blobs, with index 0 being a single blob, index 1 being 2 blobs etc.
void Tesseract::SearchForText(const GenericVector<BLOB_CHOICE_LIST*>* choices,
                              int choices_pos, int choices_length,
                              const GenericVector<UNICHAR_ID>& target_text,
                              int text_index,
                              float rating, GenericVector<int>* segmentation,
                              float* best_rating,
                              GenericVector<int>* best_segmentation) {
  const UnicharAmbigsVector& table = getDict().getUnicharAmbigs().dang_ambigs();
  for (int length = 1; length <= choices[choices_pos].size(); ++length) {
    // Rating of matching choice or worst choice if no match.
    float choice_rating = 0.0f;
    // Find the corresponding best BLOB_CHOICE.
    BLOB_CHOICE_IT choice_it(choices[choices_pos][length - 1]);
    for (choice_it.mark_cycle_pt(); !choice_it.cycled_list();
         choice_it.forward()) {
      BLOB_CHOICE* choice = choice_it.data();
      choice_rating = choice->rating();
      UNICHAR_ID class_id = choice->unichar_id();
      if (class_id == target_text[text_index]) {
        break;
      }
      // Search ambigs table.
      if (class_id < table.size() && table[class_id] != NULL) {
        AmbigSpec_IT spec_it(table[class_id]);
        for (spec_it.mark_cycle_pt(); !spec_it.cycled_list();
             spec_it.forward()) {
          const AmbigSpec *ambig_spec = spec_it.data();
          // We'll only do 1-1.
          if (ambig_spec->wrong_ngram[1] == INVALID_UNICHAR_ID &&
              ambig_spec->correct_ngram_id == target_text[text_index])
            break;
        }
        if (!spec_it.cycled_list())
          break;  // Found an ambig.
      }
    }
    if (choice_it.cycled_list())
      continue;  // No match.
    segmentation->push_back(length);
    if (choices_pos + length == choices_length &&
        text_index + 1 == target_text.size()) {
      // This is a complete match. If the rating is good record a new best.
      if (applybox_debug > 2) {
        tprintf("Complete match, rating = %g, best=%g, seglength=%d, best=%d\n",
                rating + choice_rating, *best_rating, segmentation->size(),
                best_segmentation->size());
      }
      if (best_segmentation->empty() || rating + choice_rating < *best_rating) {
        *best_segmentation = *segmentation;
        *best_rating = rating + choice_rating;
      }
    } else if (choices_pos + length < choices_length &&
               text_index + 1 < target_text.size()) {
      if (applybox_debug > 3) {
        tprintf("Match found for %d=%s:%s, at %d+%d, recursing...\n",
                target_text[text_index],
                unicharset.id_to_unichar(target_text[text_index]),
                choice_it.data()->unichar_id() == target_text[text_index]
                     ? "Match" : "Ambig",
                choices_pos, length);
      }
      SearchForText(choices, choices_pos + length, choices_length, target_text,
                    text_index + 1, rating + choice_rating, segmentation,
                    best_rating, best_segmentation);
      if (applybox_debug > 3) {
        tprintf("End recursion for %d=%s\n", target_text[text_index],
                unicharset.id_to_unichar(target_text[text_index]));
      }
    }
    segmentation->truncate(segmentation->size() - 1);
  }
}

// Counts up the labelled words and the blobs within.
// Deletes all unused or emptied words, counting the unused ones.
// Resets W_BOL and W_EOL flags correctly.
// Builds the rebuild_word and rebuilds the box_word and the best_choice.
void Tesseract::TidyUp(PAGE_RES* page_res) {
  int ok_blob_count = 0;
  int bad_blob_count = 0;
  int ok_word_count = 0;
  int unlabelled_words = 0;
  PAGE_RES_IT pr_it(page_res);
  WERD_RES* word_res;
  for (; (word_res = pr_it.word()) != NULL; pr_it.forward()) {
    int ok_in_word = 0;
    int blob_count = word_res->correct_text.size();
    WERD_CHOICE* word_choice = new WERD_CHOICE(word_res->uch_set, blob_count);
    word_choice->set_permuter(TOP_CHOICE_PERM);
    for (int c = 0; c < blob_count; ++c) {
      if (word_res->correct_text[c].length() > 0) {
        ++ok_in_word;
      }
      // Since we only need a fake word_res->best_choice, the actual
      // unichar_ids do not matter. Which is fortunate, since TidyUp()
      // can be called while training Tesseract, at the stage where
      // unicharset is not meaningful yet.
      word_choice->append_unichar_id_space_allocated(
          INVALID_UNICHAR_ID, word_res->best_state[c], 1.0f, -1.0f);
    }
    if (ok_in_word > 0) {
      ok_blob_count += ok_in_word;
      bad_blob_count += word_res->correct_text.size() - ok_in_word;
      word_res->LogNewRawChoice(word_choice);
      word_res->LogNewCookedChoice(1, false, word_choice);
    } else {
      ++unlabelled_words;
      if (applybox_debug > 0) {
        tprintf("APPLY_BOXES: Unlabelled word at :");
        word_res->word->bounding_box().print();
      }
      pr_it.DeleteCurrentWord();
    }
  }
  pr_it.restart_page();
  for (; (word_res = pr_it.word()) != NULL; pr_it.forward()) {
    // Denormalize back to a BoxWord.
    word_res->RebuildBestState();
    word_res->SetupBoxWord();
    word_res->word->set_flag(W_BOL, pr_it.prev_row() != pr_it.row());
    word_res->word->set_flag(W_EOL, pr_it.next_row() != pr_it.row());
  }
  if (applybox_debug > 0) {
    tprintf("   Found %d good blobs.\n", ok_blob_count);
    if (bad_blob_count > 0) {
      tprintf("   Leaving %d unlabelled blobs in %d words.\n",
              bad_blob_count, ok_word_count);
    }
    if (unlabelled_words > 0)
      tprintf("   %d remaining unlabelled words deleted.\n", unlabelled_words);
  }
}

// Logs a bad box by line in the box file and box coords.
void Tesseract::ReportFailedBox(int boxfile_lineno, TBOX box,
                                const char *box_ch, const char *err_msg) {
  tprintf("APPLY_BOXES: boxfile line %d/%s ((%d,%d),(%d,%d)): %s\n",
          boxfile_lineno + 1, box_ch,
          box.left(), box.bottom(), box.right(), box.top(), err_msg);
}

// Creates a fake best_choice entry in each WERD_RES with the correct text.
void Tesseract::CorrectClassifyWords(PAGE_RES* page_res) {
  PAGE_RES_IT pr_it(page_res);
  for (WERD_RES *word_res = pr_it.word(); word_res != NULL;
       word_res = pr_it.forward()) {
    WERD_CHOICE* choice = new WERD_CHOICE(word_res->uch_set,
                                          word_res->correct_text.size());
    for (int i = 0; i < word_res->correct_text.size(); ++i) {
      // The part before the first space is the real ground truth, and the
      // rest is the bounding box location and page number.
      GenericVector<STRING> tokens;
      word_res->correct_text[i].split(' ', &tokens);
      UNICHAR_ID char_id = unicharset.unichar_to_id(tokens[0].string());
      choice->append_unichar_id_space_allocated(char_id,
                                                word_res->best_state[i],
                                                0.0f, 0.0f);
    }
    word_res->ClearWordChoices();
    word_res->LogNewRawChoice(choice);
    word_res->LogNewCookedChoice(1, false, choice);
  }
}

// Calls LearnWord to extract features for labelled blobs within each word.
// Features are written to the given filename.
void Tesseract::ApplyBoxTraining(const STRING& filename, PAGE_RES* page_res) {
  PAGE_RES_IT pr_it(page_res);
  int word_count = 0;
  for (WERD_RES *word_res = pr_it.word(); word_res != NULL;
       word_res = pr_it.forward()) {
    LearnWord(filename.string(), word_res);
    ++word_count;
  }
  tprintf("Generated training data for %d words\n", word_count);
}


}  // namespace tesseract