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heuristic.cpp
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1 /* -*-C-*-
2 ********************************************************************************
3 *
4 * File: heuristic.c (Formerly heuristic.c)
5 * Description:
6 * Author: Mark Seaman, OCR Technology
7 * Created: Fri Oct 16 14:37:00 1987
8 * Modified: Wed Jul 10 14:15:08 1991 (Mark Seaman) marks@hpgrlt
9 * Language: C
10 * Package: N/A
11 * Status: Reusable Software Component
12 *
13 * (c) Copyright 1987, Hewlett-Packard Company.
14 ** Licensed under the Apache License, Version 2.0 (the "License");
15 ** you may not use this file except in compliance with the License.
16 ** You may obtain a copy of the License at
17 ** http://www.apache.org/licenses/LICENSE-2.0
18 ** Unless required by applicable law or agreed to in writing, software
19 ** distributed under the License is distributed on an "AS IS" BASIS,
20 ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
21 ** See the License for the specific language governing permissions and
22 ** limitations under the License.
23 *
24 *********************************************************************************/
25 /*----------------------------------------------------------------------
26 I n c l u d e s
27 ----------------------------------------------------------------------*/
28 #include <math.h>
29
30 // Note: "heuristic.h" is an empty file and deleted
31 #include "associate.h"
32 #include "bestfirst.h"
33 #include "seam.h"
34 #include "baseline.h"
35 #include "freelist.h"
36 #include "measure.h"
37 #include "ratngs.h"
38 #include "wordrec.h"
39
40 /*----------------------------------------------------------------------
41 M a c r o s
42 ----------------------------------------------------------------------*/
43 #define BAD_RATING 1000.0 /* No valid blob */
44
45
46 namespace tesseract {
47
48 /*----------------------------------------------------------------------
49 // Some static helpers used only in this file
50 ----------------------------------------------------------------------*/
51
52 // Return a character width record corresponding to the character
53 // width that will be generated in this segmentation state.
54 // The calling function need to memfree WIDTH_RECORD when finished.
55 // This is the same as the original function, only cosmetic changes,
56 // except instead of passing chunks back to be freed, it deallocates
57 // internally.
58 WIDTH_RECORD *Wordrec::state_char_widths(WIDTH_RECORD *chunk_widths,
59 STATE *state,
60 int num_joints) {
61 SEARCH_STATE chunks = bin_to_chunks(state, num_joints);
62 int num_chars = chunks[0] + 1;
63
64 // allocate and store (n+1,w0,g0,w1,g1...,wn) in int[2*(n+1)] as a
65 // struct { num_chars, widths[2*n+1]; }
66 WIDTH_RECORD *char_widths = (WIDTH_RECORD*) memalloc(sizeof(int)*num_chars*2);
67 char_widths->num_chars = num_chars;
68
69 int first_blob = 0;
70 int last_blob;
71 for (int i = 1; i <= num_chars; i++) {
72 last_blob = (i > chunks[0]) ? num_joints : first_blob + chunks[i];
73
74 char_widths->widths[2*i-2] =
75 AssociateUtils::GetChunksWidth(chunk_widths, first_blob, last_blob);
76 if (i <= chunks[0]) {
77 char_widths->widths[2*i-1] =
78 AssociateUtils::GetChunksGap(chunk_widths, last_blob);
79 }
80
81 if (segment_adjust_debug > 3)
82 tprintf("width_record[%d]s%d--s%d(%d) %d %d:%d\n",
83 i-1, first_blob, last_blob, chunks[i],
84 char_widths->widths[2*i-2], char_widths->widths[2*i-1],
85 chunk_widths->widths[2*last_blob+1]);
86 first_blob = last_blob + 1;
87 }
88
89 memfree(chunks);
90 return char_widths;
91 }
92
93 // Computes the variance of the char widths normalized to the given height
94 // TODO(dsl): Do this in a later stage and use char choice info to skip
95 // punctuations.
96 FLOAT32 Wordrec::get_width_variance(WIDTH_RECORD *wrec, float norm_height) {
97 MEASUREMENT ws;
98 new_measurement(ws);
99 for (int x = 0; x < wrec->num_chars; x++) {
100 FLOAT32 wh_ratio = wrec->widths[2 * x] * 1.0f / norm_height;
101 if (x == wrec->num_chars - 1 && wh_ratio > 0.3)
102 continue; // exclude trailing punctuation from stats
103 ADD_SAMPLE(ws, wh_ratio);
104 }
105 if (segment_adjust_debug > 2)
106 tprintf("Width Mean=%g Var=%g\n", MEAN(ws), VARIANCE(ws));
107 return VARIANCE(ws);
108 }
109
110 // Computes the variance of char positioning (width + spacing) wrt norm_height
111 FLOAT32 Wordrec::get_gap_variance(WIDTH_RECORD *wrec, float norm_height) {
112 MEASUREMENT ws;
113 new_measurement(ws);
114 for (int x = 0; x < wrec->num_chars - 1; x++) {
115 FLOAT32 gap_ratio = (wrec->widths[2 * x] + wrec->widths[ 2*x + 1])
116 * 1.0 / norm_height;
117 ADD_SAMPLE(ws, gap_ratio);
118 }
119 if (segment_adjust_debug > 2)
120 tprintf("Gap Mean=%g Var=%g\n", MEAN(ws), VARIANCE(ws));
121 return VARIANCE(ws);
122 }
123
124
125 /*----------------------------------------------------------------------
126 Below are the new state prioritization functions, which incorporates
127 segmentation cost and width distribution for fixed pitch languages.
128 They are included as methods in class Wordrec to obtain more context.
129 ----------------------------------------------------------------------*/
130
131 /**********************************************************************
132 * Returns the cost associated with the segmentation state.
133 *
134 * The number of states should be the same as the number of seams.
135 * If state[i] is 1, then seams[i] is present; otherwise, it is hidden.
136 * This function returns the sum of priority for active seams.
137 * TODO(dsl): To keep this clean, this function will just return the
138 * sum of raw "priority" as seam cost. The normalization of this score
139 * relative to other costs will be done in bestfirst.cpp evaluate_seg().
140 **********************************************************************/
141
142 FLOAT32 Wordrec::seamcut_priority(SEAMS seams,
143 STATE *state,
144 int num_joints) {
145 int x;
146 unsigned int mask = (num_joints > 32) ? (1 << (num_joints - 1 - 32))
147 : (1 << (num_joints - 1));
148 float seam_cost = 0.0f;
149 for (x = num_joints - 1; x >= 0; x--) {
150 int i = num_joints - 1 - x;
151 uinT32 value = (x < 32) ? state->part2 : state->part1;
152 bool state_on = value & mask;
153 if (state_on) {
154 SEAM* seam = (SEAM *) array_value(seams, i);
155 seam_cost += seam->priority;
156 }
157 if (mask == 1)
158 mask = 1 << 31;
159 else
160 mask >>= 1;
161 }
162 if (segment_adjust_debug > 2)
163 tprintf("seam_cost: %f\n", seam_cost);
164 return seam_cost;
165 }
166
167 /**********************************************************************
168 * rating_priority
169 *
170 * Assign a segmentation priority based on the ratings of the blobs
171 * (in that segmentation) that have been classified. The average
172 * "goodness" (i.e. rating / weight) for each blob is used to indicate
173 * the segmentation priority. This is the original.
174 **********************************************************************/
175 FLOAT32 Wordrec::rating_priority(CHUNKS_RECORD *chunks_record,
176 STATE *state,
177 int num_joints) {
178 BLOB_CHOICE_LIST *blob_choices;
179 BLOB_CHOICE_IT blob_choice_it;
180 inT16 first_chunk = 0;
181 inT16 last_chunk;
182 inT16 ratings = 0;
183 inT16 weights = 0;
184
185 PIECES_STATE blob_chunks;
186 bin_to_pieces(state, num_joints, blob_chunks);
187
188 for (int x = 0; blob_chunks[x]; x++) {
189 last_chunk = first_chunk + blob_chunks[x];
190
191 blob_choices = chunks_record->ratings->get(first_chunk, last_chunk - 1);
192 if (blob_choices != NOT_CLASSIFIED && blob_choices->length() > 0) {
193 blob_choice_it.set_to_list(blob_choices);
194 ratings += (inT16) blob_choice_it.data()->rating();
195 for (int y = first_chunk; y < last_chunk; y++) {
196 weights += (inT16) (chunks_record->weights[y]);
197 }
198 }
199 first_chunk = last_chunk;
200 }
201 if (weights <= 0)
202 weights = 1;
203 FLOAT32 rating_cost = static_cast<FLOAT32>(ratings) /
204 static_cast<FLOAT32>(weights);
205 if (segment_adjust_debug > 2)
206 tprintf("rating_cost: r%f / w%f = %f\n", ratings, weights, rating_cost);
207 return rating_cost;
208 }
209
210 /**********************************************************************
211 * width_priority
212 *
213 * Return a priority value for this word segmentation based on the
214 * character widths present in the new segmentation.
215 * For variable-pitch fonts, this should do the same thing as before:
216 * ie. penalize only on really wide squatting blobs.
217 * For fixed-pitch fonts, this will include a measure of char & gap
218 * width consistency.
219 * TODO(dsl): generalize this to use a PDF estimate for proportional and
220 * fixed pitch mode.
221 **********************************************************************/
222 FLOAT32 Wordrec::width_priority(CHUNKS_RECORD *chunks_record,
223 STATE *state,
224 int num_joints) {
225 FLOAT32 penalty = 0.0;
226 WIDTH_RECORD *width_rec = state_char_widths(chunks_record->chunk_widths,
227 state, num_joints);
228 // When baseline_enable==True, which is the current default for Tesseract,
229 // a fixed value of 128 (BASELINE_SCALE) is always used.
230 FLOAT32 normalizing_height = BASELINE_SCALE;
231 if (assume_fixed_pitch_char_segment) {
232 // For fixed pitch language like CJK, we use the full text height as the
233 // normalizing factor so we are not dependent on xheight calculation.
234 // In the normalized coord. xheight * scale == BASELINE_SCALE(128),
235 // so add proportionally scaled ascender zone to get full text height.
236 const DENORM& denorm = chunks_record->word_res->denorm;
237 normalizing_height = denorm.y_scale() *
238 (denorm.row()->x_height() + denorm.row()->ascenders());
239 if (segment_adjust_debug > 1)
240 tprintf("WidthPriority: %f %f normalizing height = %f\n",
241 denorm.row()->x_height(), denorm.row()->ascenders(),
242 normalizing_height);
243 // Impose additional segmentation penalties if blob widths or gaps
244 // distribution don't fit a fixed-pitch model.
245 FLOAT32 width_var = get_width_variance(width_rec, normalizing_height);
246 FLOAT32 gap_var = get_gap_variance(width_rec, normalizing_height);
247 penalty += width_var;
248 penalty += gap_var;
249 }
250
251 for (int x = 0; x < width_rec->num_chars; x++) {
252 FLOAT32 squat = width_rec->widths[2*x];
253 FLOAT32 gap = (x < width_rec->num_chars-1) ? width_rec->widths[2*x+1] : 0;
254 squat /= normalizing_height;
255 gap /= normalizing_height;
256 if (assume_fixed_pitch_char_segment) {
257 penalty += AssociateUtils::FixedPitchWidthCost(
258 squat, 0.0f, x == 0 || x == width_rec->num_chars -1,
259 heuristic_max_char_wh_ratio);
260 penalty += AssociateUtils::FixedPitchGapCost(
261 gap, x == width_rec->num_chars - 1);
262 if (width_rec->num_chars == 1 &&
263 squat > AssociateUtils::kMaxFixedPitchCharAspectRatio) {
264 penalty += 10;
265 }
266 } else {
267 // Original equation when
268 // heuristic_max_char_ratio == AssociateUtils::kMaxSquat
269 if (squat > heuristic_max_char_wh_ratio)
270 penalty += squat - heuristic_max_char_wh_ratio;
271 }
272 }
273
274 free_widths(width_rec);
275 return (penalty);
276 }
277
278
279 /**********************************************************************
280 * prioritize_state
281 *
282 * Create a priority for this state. It represents the urgency of
283 * checking this state. The larger the priority value, the worse the
284 * state is (lower priority). The "value" of this priority should be
285 * somewhat consistent with the final word rating.
286 * The question is how to normalize the different scores, and adjust
287 * the relative importance among them.
288 **********************************************************************/
289 FLOAT32 Wordrec::prioritize_state(CHUNKS_RECORD *chunks_record,
290 SEARCH_RECORD *the_search) {
291 FLOAT32 shape_cost;
292 FLOAT32 width_cost;
293 FLOAT32 seam_cost;
294
295 shape_cost = rating_priority(chunks_record,
296 the_search->this_state,
297 the_search->num_joints);
298
299 width_cost = width_priority(chunks_record,
300 the_search->this_state,
301 the_search->num_joints);
302
303 // The rating_priority is the same as the original, and the width_priority
304 // is the same as before if assume_fixed_pitch_char_segment == FALSE.
305 // So this would return the original state priority.
306 if (!use_new_state_cost)
307 return width_cost * 1000 + shape_cost;
308
309 seam_cost = seamcut_priority(chunks_record->splits,
310 the_search->this_state,
311 the_search->num_joints);
312
313 // TODO(dsl): how do we normalize the scores for these separate evidence?
314 // FLOAT32 total_cost = shape_cost + width_cost * 0.01 + seam_cost * 0.001;
315 FLOAT32 total_cost = shape_cost * heuristic_weight_rating +
316 width_cost * heuristic_weight_width +
317 seam_cost * heuristic_weight_seamcut;
318
319 // We don't have an adjustment model for variable pitch segmentation cost
320 // into word rating
321 if (assume_fixed_pitch_char_segment) {
322 float seg_bias = 1.0;
323 if (width_cost < 1) seg_bias *= 0.85;
324 if (width_cost > 3)
325 seg_bias *= pow(heuristic_segcost_rating_base, width_cost/3.0);
326 if (seam_cost > 10)
327 seg_bias *= pow(heuristic_segcost_rating_base, log(seam_cost)/log(10.0));
328 if (shape_cost > 5)
329 seg_bias *= pow(heuristic_segcost_rating_base, shape_cost/5.0);
330 if (segment_adjust_debug) {
331 tprintf("SegCost: %g Weight: %g rating: %g width: %g seam: %g\n",
332 total_cost, seg_bias, shape_cost, width_cost, seam_cost);
333 }
334 the_search->segcost_bias = seg_bias;
335 } else {
336 the_search->segcost_bias = 0;
337 }
338
339 return total_cost;
340 }
341
342 } // namespace tesseract