android-2.0.1_r1/s

/* -*-C-*-
 ********************************************************************************
 *
 * File:        heuristic.c  (Formerly heuristic.c)
 * Description:
 * Author:       Mark Seaman, OCR Technology
 * Created:      Fri Oct 16 14:37:00 1987
 * Modified:     Wed Jul 10 14:15:08 1991 (Mark Seaman) marks@hpgrlt
 * Language:     C
 * Package:      N/A
 * Status:       Reusable Software Component
 *
 * (c) Copyright 1987, Hewlett-Packard Company.
 ** 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.
 *
 *********************************************************************************/
/*----------------------------------------------------------------------
              I n c l u d e s
----------------------------------------------------------------------*/
#include <math.h>

#include "heuristic.h"

#include "baseline.h"
#include "freelist.h"
#include "metrics.h"
#include "measure.h"
#include "ratngs.h"
#include "wordrec.h"

/*----------------------------------------------------------------------
              M a c r o s
----------------------------------------------------------------------*/
#define MAX_SQUAT       2.0      /* Width ratio */
#define BAD_RATING   1000.0      /* No valid blob */

INT_VAR(segment_adjust_debug, 0,
        "Segmentation adjustment debug");
BOOL_VAR(assume_fixed_pitch_char_segment, 0,
         "include fixed-pitch heuristics in char segmentation");
BOOL_VAR(use_new_state_cost, 0,
         "use new state cost heuristics for segmentation state evaluation");
double_VAR(heuristic_segcost_rating_base, 1.25,
           "base factor for adding segmentation cost into word rating."
           "It's a multiplying factor, the larger the value above 1, "
           "the bigger the effect of segmentation cost.");
double_VAR(heuristic_weight_rating, 1,
           "weight associated with char rating in combined cost of state");
double_VAR(heuristic_weight_width, 0,
           "weight associated with width evidence in combined cost of state");
double_VAR(heuristic_weight_seamcut, 0,
           "weight associated with seam cut in combined cost of state");
double_VAR(heuristic_max_char_wh_ratio, MAX_SQUAT,
           "max char width-to-height ratio allowed in segmentation");

namespace tesseract {

/*----------------------------------------------------------------------
// Some static helpers used only in this file
----------------------------------------------------------------------*/

// Return a character width record corresponding to the character
// width that will be generated in this segmentation state.
// The calling function need to memfree WIDTH_RECORD when finished.
// This is the same as the original function, only cosmetic changes,
// except instead of passing chunks back to be freed, it deallocates
// internally.
WIDTH_RECORD *state_char_widths(WIDTH_RECORD *chunk_widths,
                                STATE *state,
                                int num_joints) {
  SEARCH_STATE chunks = bin_to_chunks(state, num_joints);
  int num_chars = chunks[0] + 1;

  // allocate and store (n+1,w0,g0,w1,g1...,wn) in int[2*(n+1)] as a
  // struct { num_chars, widths[2*n+1]; }
  WIDTH_RECORD *char_widths = (WIDTH_RECORD*) memalloc(sizeof(int)*num_chars*2);
  char_widths->num_chars = num_chars;

  int first_blob = 0;
  int last_blob;
  for (int i = 1; i <= num_chars; i++) {
    last_blob = (i > chunks[0]) ? num_joints : first_blob + chunks[i];

    char_widths->widths[2*i-2] = chunks_width(chunk_widths,
                                              first_blob, last_blob);
    if (i <= chunks[0])
      char_widths->widths[2*i-1] = chunks_gap(chunk_widths, last_blob);

    if (segment_adjust_debug > 3)
      tprintf("width_record[%d]s%d--s%d(%d) %d %d:%d\n",
              i-1, first_blob, last_blob, chunks[i],
              char_widths->widths[2*i-2], char_widths->widths[2*i-1],
              chunk_widths->widths[2*last_blob+1]);
    first_blob = last_blob + 1;
  }

  memfree(chunks);
  return char_widths;
}

// Computes the variance of the char widths normalized to the given height
// TODO(dsl): Do this in a later stage and use char choice info to skip
// punctuations.
FLOAT32 get_width_variance(WIDTH_RECORD *wrec, float norm_height) {
  MEASUREMENT ws;
  new_measurement(ws);
  for (int x = 0; x < wrec->num_chars; x++) {
    FLOAT32 wh_ratio = wrec->widths[2 * x] * 1.0f / norm_height;
    if (x == wrec->num_chars - 1 && wh_ratio > 0.3)
      continue;   // exclude trailing punctuation from stats
    ADD_SAMPLE(ws, wh_ratio);
  }
  if (segment_adjust_debug > 2)
    tprintf("Width Mean=%g Var=%g\n", MEAN(ws), VARIANCE(ws));
  return VARIANCE(ws);
}

// Computes the variance of char positioning (width + spacing) wrt norm_height
FLOAT32 get_gap_variance(WIDTH_RECORD *wrec, float norm_height) {
  MEASUREMENT ws;
  new_measurement(ws);
  for (int x = 0; x < wrec->num_chars - 1; x++) {
    FLOAT32 gap_ratio = (wrec->widths[2 * x] + wrec->widths[ 2*x + 1])
                        * 1.0 / norm_height;
    ADD_SAMPLE(ws, gap_ratio);
  }
  if (segment_adjust_debug > 2)
    tprintf("Gap Mean=%g Var=%g\n", MEAN(ws), VARIANCE(ws));
  return VARIANCE(ws);
}


/*----------------------------------------------------------------------
 Below are the new state prioritization functions, which incorporates
 segmentation cost and width distribution for fixed pitch languages.
 They are included as methods in class Wordrec to obtain more context.
 ----------------------------------------------------------------------*/

/**********************************************************************
 * Returns the cost associated with the segmentation state.
 *
 * The number of states should be the same as the number of seams.
 * If state[i] is 1, then seams[i] is present; otherwise, it is hidden.
 * This function returns the sum of priority for active seams.
 * TODO(dsl): To keep this clean, this function will just return the
 * sum of raw "priority" as seam cost.  The normalization of this score
 * relative to other costs will be done in bestfirst.cpp evaluate_seg().
 **********************************************************************/

FLOAT32 Wordrec::seamcut_priority(SEAMS seams,
                                  STATE *state,
                                  int num_joints) {
  int x;
  unsigned int mask = (num_joints > 32) ? (1 << (num_joints - 1 - 32))
                                        : (1 << (num_joints - 1));
  float seam_cost = 0.0f;
  for (x = num_joints - 1; x >= 0; x--) {
    int i = num_joints - 1 - x;
    uinT32 value = (x < 32) ? state->part2 : state->part1;
    bool state_on = value & mask;
    if (state_on) {
      SEAM* seam = (SEAM *) array_value(seams, i);
      seam_cost += seam->priority;
    }
    if (mask == 1)
      mask = 1 << 31;
    else
      mask >>= 1;
  }
  if (segment_adjust_debug > 2)
    tprintf("seam_cost: %f\n", seam_cost);
  return seam_cost;
}

/**********************************************************************
 * rating_priority
 *
 * Assign a segmentation priority based on the ratings of the blobs
 * (in that segmentation) that have been classified.  The average
 * "goodness" (i.e. rating / weight) for each blob is used to indicate
 * the segmentation priority.  This is the original.
 **********************************************************************/
FLOAT32 Wordrec::rating_priority(CHUNKS_RECORD *chunks_record,
                        STATE *state,
                        int num_joints) {
  BLOB_CHOICE_LIST *blob_choices;
  BLOB_CHOICE_IT blob_choice_it;
  inT16 first_chunk = 0;
  inT16 last_chunk;
  inT16 ratings = 0;
  inT16 weights = 0;

  PIECES_STATE blob_chunks;
  bin_to_pieces(state, num_joints, blob_chunks);

  for (int x = 0; blob_chunks[x]; x++) {
    last_chunk = first_chunk + blob_chunks[x];

    blob_choices = chunks_record->ratings->get(first_chunk, last_chunk - 1);
    if (blob_choices != NOT_CLASSIFIED && blob_choices->length() > 0) {
      blob_choice_it.set_to_list(blob_choices);
      ratings += (inT16) blob_choice_it.data()->rating();
      for (int y = first_chunk; y < last_chunk; y++) {
        weights += (inT16) (chunks_record->weights[y]);
      }
    }
    first_chunk = last_chunk;
  }
  if (weights <= 0)
    weights = 1;
  FLOAT32 rating_cost = static_cast<FLOAT32>(ratings) /
                        static_cast<FLOAT32>(weights);
  if (segment_adjust_debug > 2)
    tprintf("rating_cost: r%f / w%f = %f\n", ratings, weights, rating_cost);
  return rating_cost;
}

// Returns the cost, eg. -log(p), of a given value in char width distribution.
FLOAT32 fp_width_cost(float norm_width, bool end_pos) {
  bool use_old_hack = true;
  if (use_old_hack) {
    float cost = 0;
    if (norm_width > heuristic_max_char_wh_ratio)
      cost += norm_width;
    if (norm_width > MAX_SQUAT)  // extra penalty for merging two CJK chars
      cost += norm_width * norm_width;
    // penalize skinny blobs, except for punctuation in the last position
    if (norm_width < 0.5 && !end_pos)
      cost += 1 - norm_width;
    return cost;
}

  // otherwise, approximate with our not-so-normal distribution
  float s = fabs((norm_width - 0.85) / 0.35);
  if (s < 1)   // clip penalty to zero for anything within 1 std
    return 0.0f;
  // Allow smaller chars at begin or end position for punctuations
  if (end_pos && norm_width < 0.3)
    return 0.0f;
  if (segment_adjust_debug > 2)
    tprintf("fp_width_cost(%f) = %f**2 = %f\n", norm_width, s, s*s);
  return s*s;
}

FLOAT32 fp_gap_cost(float norm_gap, bool end_pos) {
  bool use_old_hack = true;
  if (use_old_hack) {
    if (norm_gap < 0.05 && !end_pos)
      return 5;   // penalize vertically overlapping components
    else
      return 0;
  }
  float s = fabs((norm_gap - 0.1) / 0.02);
  if (s > -1) return 0.0f;  // no penalty for wider gaps
  if (segment_adjust_debug > 2)
    tprintf("fp_gap_cost(%f) = %f**2 = %f\n", norm_gap, s, s*s);
  return s*s;
}

/**********************************************************************
 * width_priority
 *
 * Return a priority value for this word segmentation based on the
 * character widths present in the new segmentation.
 * For variable-pitch fonts, this should do the same thing as before:
 * ie. penalize only on really wide squatting blobs.
 * For fixed-pitch fonts, this will include a measure of char & gap
 * width consistency.
 * TODO(dsl): generalize this to use a PDF estimate for proportional and
 * fixed pitch mode.
 **********************************************************************/
FLOAT32 Wordrec::width_priority(CHUNKS_RECORD *chunks_record,
                                STATE *state,
                                int num_joints) {
  FLOAT32 penalty = 0.0;
  WIDTH_RECORD *width_rec = state_char_widths(chunks_record->chunk_widths,
                                              state, num_joints);
  // When baseline_enable==True, which is the current default for Tesseract,
  // a fixed value of 128 (BASELINE_SCALE) is always used.
  FLOAT32 normalizing_height = BASELINE_SCALE;
  if (!classify_baseline_normalized)   // this doesn't work and is never invoked
    normalizing_height = chunks_record->row->lineheight;
  if (assume_fixed_pitch_char_segment) {
    // For fixed pitch language like CJK, we use the full text height as the
    // normalizing factor so we are not dependent on xheight calculation.
    // In the normalized coord. xheight * scale == BASELINE_SCALE(128),
    // so add proportionally scaled ascender zone to get full text height.
    normalizing_height = tess_denorm->scale() *
        (tess_denorm->row()->x_height() + tess_denorm->row()->ascenders());
    if (segment_adjust_debug > 1)
      tprintf("WidthPriority: %f %f normalizing height = %f\n",
              tess_denorm->row()->x_height(), tess_denorm->row()->ascenders(),
              normalizing_height);
    // Impose additional segmentation penalties if blob widths or gaps
    // distribution don't fit a fixed-pitch model.
    FLOAT32 width_var = get_width_variance(width_rec, normalizing_height);
    FLOAT32 gap_var = get_gap_variance(width_rec, normalizing_height);
    penalty += width_var;
    penalty += gap_var;
  }

  for (int x = 0; x < width_rec->num_chars; x++) {
    FLOAT32 squat = width_rec->widths[2*x];
    FLOAT32 gap = (x < width_rec->num_chars-1) ? width_rec->widths[2*x+1] : 0;
    squat /= normalizing_height;
    gap /= normalizing_height;
    if (assume_fixed_pitch_char_segment) {
      penalty += fp_width_cost(squat, x == 0 || x == width_rec->num_chars -1);
      penalty += fp_gap_cost(gap, x == width_rec->num_chars - 1);
      if (width_rec->num_chars == 1 && squat > MAX_SQUAT)
        penalty += 10;
    } else {
      // original equation when heuristic_max_char_ratio == MAX_SQUAT
      if (squat > heuristic_max_char_wh_ratio)
        penalty += squat - heuristic_max_char_wh_ratio;
    }
  }

  free_widths(width_rec);
  return (penalty);
}


/**********************************************************************
 * prioritize_state
 *
 * Create a priority for this state.  It represents the urgency of
 * checking this state.  The larger the priority value, the worse the
 * state is (lower priority).  The "value" of this priority should be
 * somewhat consistent with the final word rating.
 * The question is how to normalize the different scores, and adjust
 * the relative importance among them.
 **********************************************************************/
FLOAT32 Wordrec::prioritize_state(CHUNKS_RECORD *chunks_record,
                                  SEARCH_RECORD *the_search) {
  FLOAT32 shape_cost;
  FLOAT32 width_cost;
  FLOAT32 seam_cost;

  shape_cost = rating_priority(chunks_record,
                               the_search->this_state,
                               the_search->num_joints);

  width_cost = width_priority(chunks_record,
                              the_search->this_state,
                              the_search->num_joints);

  // The rating_priority is the same as the original, and the width_priority
  // is the same as before if assume_fixed_pitch_char_segment == FALSE.
  // So this would return the original state priority.
  if (!use_new_state_cost)
    return width_cost * 1000 + shape_cost;

  seam_cost = seamcut_priority(chunks_record->splits,
                               the_search->this_state,
                               the_search->num_joints);
  record_priorities(the_search, shape_cost, width_cost);

  // TODO(dsl): how do we normalize the scores for these separate evidence?
  // FLOAT32 total_cost = shape_cost + width_cost * 0.01 + seam_cost * 0.001;
  FLOAT32 total_cost = shape_cost * heuristic_weight_rating +
                       width_cost * heuristic_weight_width +
                       seam_cost * heuristic_weight_seamcut;

  // We don't have an adjustment model for variable pitch segmentation cost
  // into word rating
  if (assume_fixed_pitch_char_segment) {
    float seg_bias = 1.0;
    if (width_cost < 1) seg_bias *= 0.85;
    if (width_cost > 3)
      seg_bias *= pow(heuristic_segcost_rating_base, width_cost/3.0);
    if (seam_cost > 10)
      seg_bias *= pow(heuristic_segcost_rating_base, log(seam_cost)/log(10.0));
    if (shape_cost > 5)
      seg_bias *= pow(heuristic_segcost_rating_base, shape_cost/5.0);
    if (segment_adjust_debug) {
      tprintf("SegCost: %g Weight: %g rating: %g  width: %g  seam: %g\n",
               total_cost, seg_bias, shape_cost, width_cost, seam_cost);
    }
    the_search->segcost_bias = seg_bias;
  } else {
    the_search->segcost_bias = 0;
  }

  return total_cost;
}


/*----------------------------------------------------------------------
              F u n c t i o n s

 Below are the original state prioritization functions for reference.
 Since they work well for Latin, we need to keep them around until the
 new path is verified to do no worse than before.

//  Assign a segmentation priority based on the ratings of the blobs
//  (in that segmentation) that have been classified.  The average
//  "goodness" (i.e. rating / weight) for each blob is used to indicate
//  the segmentation priority.
FLOAT32 rating_priority(CHUNKS_RECORD *chunks_record,
                        STATE *state,
                        STATE *old_state,
                        int num_joints) {
  PIECES_STATE blob_chunks;
  inT16 x;
  inT16 y;
  BLOB_CHOICE_LIST *blob_choices;
  BLOB_CHOICE_IT blob_choice_it;
  inT16 first_chunk = 0;
  inT16 last_chunk;
  inT16 ratings = 0;
  inT16 weights = 0;

  bin_to_pieces(state, num_joints, blob_chunks);

  for (x = 0; blob_chunks[x]; x++) {
                                 // Iterate each blob
    last_chunk = first_chunk + blob_chunks[x] - 1;

    blob_choices = chunks_record->ratings->get(first_chunk, last_chunk);

    if (blob_choices != NOT_CLASSIFIED) {
      blob_choice_it.set_to_list(blob_choices);
      ratings += (inT16) blob_choice_it.data()->rating();
      for (y = first_chunk; y <= last_chunk; y++) {
        weights += (inT16) (chunks_record->weights[y]);
      }
    }
    first_chunk += blob_chunks[x];
  }
  if (weights <= 0)
    weights = 1;
  return ((FLOAT32) ratings / weights);
}

// Return a priority value for this word segmentation based on the
// character widths present in the new segmentation.
FLOAT32 width_priority(CHUNKS_RECORD *chunks_record,
                       STATE *state,
                       int num_joints) {
  FLOAT32 result = 0.0;
  WIDTH_RECORD *width_record;
  FLOAT32 squat;
  int x;

  width_record = state_char_widths (chunks_record->chunk_widths,
    state, num_joints);
  for (x = 0; x < width_record->num_chars; x++) {

    squat = width_record->widths[2 * x];
    if (!classify_baseline_normalized) {
      squat /= chunks_record->row->lineheight;
    }
    else {
      squat /= BASELINE_SCALE;
    }

    if (squat > MAX_SQUAT)
      result += squat - MAX_SQUAT;

  }

  free_widths(width_record);

  return (result);
}

//  Create a priority for this state.  It represents the urgency of
//  checking this state.
FLOAT32 prioritize_state(CHUNKS_RECORD *chunks_record,
                         SEARCH_RECORD *the_search,
                         STATE *old_state) {
  FLOAT32 width_pri;
  FLOAT32 match_pri;

  match_pri = rating_priority (chunks_record, the_search->this_state,
    old_state, the_search->num_joints);

  width_pri = width_priority (chunks_record, the_search->this_state,
    the_search->num_joints) * 1000.0;

  record_priorities(the_search, old_state, match_pri, width_pri);

  return (width_pri + match_pri);
}
------------------   Original Rating Functions   -----------------*/

}  // namespace tesseract