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Improved sub/superscript treatment

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git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@872 d0cd1f9f-072b-0410-8dd7-cf729c803f20
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theraysmith@gmail.com 2013-09-20 19:49:47 +00:00
parent 96c662ed6e
commit 2aafc9df24
3 changed files with 872 additions and 53 deletions
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ccmain/superscript.cpp Normal file
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@ -0,0 +1,610 @@
/******************************************************************
* File: superscript.cpp
* Description: Correction pass to fix superscripts and subscripts.
* Author: David Eger
* Created: Mon Mar 12 14:05:00 PDT 2012
*
* (C) Copyright 2012, Google, Inc.
** Licensed under the Apache License, Version 2.0 (the "License");
** you may not use this file except in compliance with the License.
** You may obtain a copy of the License at
** http://www.apache.org/licenses/LICENSE-2.0
** Unless required by applicable law or agreed to in writing, software
** distributed under the License is distributed on an "AS IS" BASIS,
** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
** See the License for the specific language governing permissions and
** limitations under the License.
*
**********************************************************************/
#include "normalis.h"
#include "tesseractclass.h"
static int LeadingUnicharsToChopped(WERD_RES *word, int num_unichars) {
int num_chopped = 0;
for (int i = 0; i < num_unichars; i++)
num_chopped += word->best_state[i];
return num_chopped;
}
static int TrailingUnicharsToChopped(WERD_RES *word, int num_unichars) {
int num_chopped = 0;
for (int i = 0; i < num_unichars; i++)
num_chopped += word->best_state[word->best_state.size() - 1 - i];
return num_chopped;
}
namespace tesseract {
/**
* Given a recognized blob, see if a contiguous collection of sub-pieces
* (chopped blobs) starting at its left might qualify as being a subscript
* or superscript letter based only on y position. Also do this for the
* right side.
*/
void YOutlierPieces(WERD_RES *word, int rebuilt_blob_index,
int super_y_bottom, int sub_y_top,
ScriptPos *leading_pos, int *num_leading_outliers,
ScriptPos *trailing_pos, int *num_trailing_outliers) {
ScriptPos sp_unused1, sp_unused2;
int unused1, unused2;
if (!leading_pos) leading_pos = &sp_unused1;
if (!num_leading_outliers) num_leading_outliers = &unused1;
if (!trailing_pos) trailing_pos = &sp_unused2;
if (!num_trailing_outliers) num_trailing_outliers = &unused2;
*num_leading_outliers = *num_trailing_outliers = 0;
*leading_pos = *trailing_pos = SP_NORMAL;
int chopped_start = LeadingUnicharsToChopped(word, rebuilt_blob_index);
int num_chopped_pieces = word->best_state[rebuilt_blob_index];
ScriptPos last_pos = SP_NORMAL;
int trailing_outliers = 0;
for (int i = 0; i < num_chopped_pieces; i++) {
TBOX box = word->chopped_word->blobs[chopped_start + i]->bounding_box();
ScriptPos pos = SP_NORMAL;
if (box.bottom() >= super_y_bottom) {
pos = SP_SUPERSCRIPT;
} else if (box.top() <= sub_y_top) {
pos = SP_SUBSCRIPT;
}
if (pos == SP_NORMAL) {
if (trailing_outliers == i) {
*num_leading_outliers = trailing_outliers;
*leading_pos = last_pos;
}
trailing_outliers = 0;
} else {
if (pos == last_pos) {
trailing_outliers++;
} else {
trailing_outliers = 1;
}
}
last_pos = pos;
}
*num_trailing_outliers = trailing_outliers;
*trailing_pos = last_pos;
}
/**
* Attempt to split off any high (or low) bits at the ends of the word with poor
* certainty and recognize them separately. If the certainty gets much better
* and other sanity checks pass, acccept.
*
* This superscript fix is meant to be called in the second pass of recognition
* when we have tried once and already have a preliminary answer for word.
*
* @return Whether we modified the given word.
*/
bool Tesseract::SubAndSuperscriptFix(WERD_RES *word) {
if (word->tess_failed || word->word->flag(W_REP_CHAR) ||
!word->best_choice) {
return false;
}
int num_leading, num_trailing;
ScriptPos sp_leading, sp_trailing;
float leading_certainty, trailing_certainty;
float avg_certainty, unlikely_threshold;
// Calculate the number of whole suspicious characters at the edges.
GetSubAndSuperscriptCandidates(
word, &num_leading, &sp_leading, &leading_certainty,
&num_trailing, &sp_trailing, &trailing_certainty,
&avg_certainty, &unlikely_threshold);
const char *leading_pos = sp_leading == SP_SUBSCRIPT ? "sub" : "super";
const char *trailing_pos = sp_trailing == SP_SUBSCRIPT ? "sub" : "super";
int num_blobs = word->best_choice->length();
// Calculate the remainder (partial characters) at the edges.
// This accounts for us having classified the best version of
// a word as [speaker?'] when it was instead [speaker.^{21}]
// (that is we accidentally thought the 2 was attached to the period).
int num_remainder_leading = 0, num_remainder_trailing = 0;
if (num_leading + num_trailing < num_blobs && unlikely_threshold < 0.0) {
int super_y_bottom =
kBlnBaselineOffset + kBlnXHeight * superscript_min_y_bottom;
int sub_y_top =
kBlnBaselineOffset + kBlnXHeight * subscript_max_y_top;
int last_word_char = num_blobs - 1 - num_trailing;
float last_char_certainty = word->best_choice->certainty(last_word_char);
if (word->best_choice->unichar_id(last_word_char) != 0 &&
last_char_certainty <= unlikely_threshold) {
ScriptPos rpos;
YOutlierPieces(word, last_word_char, super_y_bottom, sub_y_top,
NULL, NULL, &rpos, &num_remainder_trailing);
if (num_trailing > 0 && rpos != sp_trailing) num_remainder_trailing = 0;
if (num_remainder_trailing > 0 &&
last_char_certainty < trailing_certainty) {
trailing_certainty = last_char_certainty;
}
}
bool another_blob_available = (num_remainder_trailing == 0) ||
num_leading + num_trailing + 1 < num_blobs;
int first_char_certainty = word->best_choice->certainty(num_leading);
if (another_blob_available &&
word->best_choice->unichar_id(num_leading) != 0 &&
first_char_certainty <= unlikely_threshold) {
ScriptPos lpos;
YOutlierPieces(word, num_leading, super_y_bottom, sub_y_top,
&lpos, &num_remainder_leading, NULL, NULL);
if (num_leading > 0 && lpos != sp_leading) num_remainder_leading = 0;
if (num_remainder_leading > 0 &&
first_char_certainty < leading_certainty) {
leading_certainty = first_char_certainty;
}
}
}
// If nothing to do, bail now.
if (num_leading + num_trailing +
num_remainder_leading + num_remainder_trailing == 0) {
return false;
}
if (superscript_debug >= 1) {
tprintf("Candidate for superscript detection: %s (",
word->best_choice->unichar_string().string());
if (num_leading || num_remainder_leading) {
tprintf("%d.%d %s-leading ", num_leading, num_remainder_leading,
leading_pos);
}
if (num_trailing || num_remainder_trailing) {
tprintf("%d.%d %s-trailing ", num_trailing, num_remainder_trailing,
trailing_pos);
}
tprintf(")\n");
}
if (superscript_debug >= 3) {
word->best_choice->print();
}
if (superscript_debug >= 2) {
tprintf(" Certainties -- Average: %.2f Unlikely thresh: %.2f ",
avg_certainty, unlikely_threshold);
if (num_leading)
tprintf("Orig. leading (min): %.2f ", leading_certainty);
if (num_trailing)
tprintf("Orig. trailing (min): %.2f ", trailing_certainty);
tprintf("\n");
}
// We've now calculated the number of rebuilt blobs we want to carve off.
// However, split_word() works from TBLOBs in chopped_word, so we need to
// convert to those.
int num_chopped_leading =
LeadingUnicharsToChopped(word, num_leading) + num_remainder_leading;
int num_chopped_trailing =
TrailingUnicharsToChopped(word, num_trailing) + num_remainder_trailing;
int retry_leading = 0;
int retry_trailing = 0;
bool is_good = false;
WERD_RES *revised = TrySuperscriptSplits(
num_chopped_leading, leading_certainty, sp_leading,
num_chopped_trailing, trailing_certainty, sp_trailing,
word, &is_good, &retry_leading, &retry_trailing);
if (is_good) {
word->ConsumeWordResults(revised);
} else if (retry_leading || retry_trailing) {
int retry_chopped_leading =
LeadingUnicharsToChopped(revised, retry_leading);
int retry_chopped_trailing =
TrailingUnicharsToChopped(revised, retry_trailing);
WERD_RES *revised2 = TrySuperscriptSplits(
retry_chopped_leading, leading_certainty, sp_leading,
retry_chopped_trailing, trailing_certainty, sp_trailing,
revised, &is_good, &retry_leading, &retry_trailing);
if (is_good) {
word->ConsumeWordResults(revised2);
}
delete revised2;
}
delete revised;
return is_good;
}
/**
* Determine how many characters (rebuilt blobs) on each end of a given word
* might plausibly be superscripts so SubAndSuperscriptFix can try to
* re-recognize them. Even if we find no whole blobs at either end,
* we will set *unlikely_threshold to a certainty that might be used to
* select "bad enough" outlier characters. If *unlikely_threshold is set to 0,
* though, there's really no hope.
*
* @param[in] word The word to examine.
* @param[out] num_rebuilt_leading the number of rebuilt blobs at the start
* of the word which are all up or down and
* seem badly classified.
* @param[out] leading_pos "super" or "sub" (for debugging)
* @param[out] leading_certainty the worst certainty in the leading blobs.
* @param[out] num_rebuilt_trailing the number of rebuilt blobs at the end
* of the word which are all up or down and
* seem badly classified.
* @param[out] trailing_pos "super" or "sub" (for debugging)
* @param[out] trailing_certainty the worst certainty in the trailing blobs.
* @param[out] avg_certainty the average certainty of "normal" blobs in
* the word.
* @param[out] unlikely_threshold the threshold (on certainty) we used to
* select "bad enough" outlier characters.
*/
void Tesseract::GetSubAndSuperscriptCandidates(const WERD_RES *word,
int *num_rebuilt_leading,
ScriptPos *leading_pos,
float *leading_certainty,
int *num_rebuilt_trailing,
ScriptPos *trailing_pos,
float *trailing_certainty,
float *avg_certainty,
float *unlikely_threshold) {
*avg_certainty = *unlikely_threshold = 0.0f;
*num_rebuilt_leading = *num_rebuilt_trailing = 0;
*leading_certainty = *trailing_certainty = 0.0f;
int super_y_bottom =
kBlnBaselineOffset + kBlnXHeight * superscript_min_y_bottom;
int sub_y_top =
kBlnBaselineOffset + kBlnXHeight * subscript_max_y_top;
// Step one: Get an average certainty for "normally placed" characters.
// Counts here are of blobs in the rebuild_word / unichars in best_choice.
*leading_pos = *trailing_pos = SP_NORMAL;
int leading_outliers = 0;
int trailing_outliers = 0;
int num_normal = 0;
float normal_certainty_total = 0.0f;
float worst_normal_certainty = 0.0f;
ScriptPos last_pos = SP_NORMAL;
int num_blobs = word->rebuild_word->NumBlobs();
for (int b = 0; b < num_blobs; ++b) {
TBOX box = word->rebuild_word->blobs[b]->bounding_box();
ScriptPos pos = SP_NORMAL;
if (box.bottom() >= super_y_bottom) {
pos = SP_SUPERSCRIPT;
} else if (box.top() <= sub_y_top) {
pos = SP_SUBSCRIPT;
}
if (pos == SP_NORMAL) {
if (word->best_choice->unichar_id(b) != 0) {
float char_certainty = word->best_choice->certainty(b);
if (char_certainty < worst_normal_certainty) {
worst_normal_certainty = char_certainty;
}
num_normal++;
normal_certainty_total += char_certainty;
}
if (trailing_outliers == b) {
leading_outliers = trailing_outliers;
*leading_pos = last_pos;
}
trailing_outliers = 0;
} else {
if (last_pos == pos) {
trailing_outliers++;
} else {
trailing_outliers = 1;
}
}
last_pos = pos;
}
*trailing_pos = last_pos;
if (num_normal >= 3) { // throw out the worst as an outlier.
num_normal--;
normal_certainty_total -= worst_normal_certainty;
}
if (num_normal > 0) {
*avg_certainty = normal_certainty_total / num_normal;
*unlikely_threshold = superscript_worse_certainty * (*avg_certainty);
}
if (num_normal == 0 ||
(leading_outliers == 0 && trailing_outliers == 0)) {
return;
}
// Step two: Try to split off bits of the word that are both outliers
// and have much lower certainty than average
// Calculate num_leading and leading_certainty.
for (*leading_certainty = 0.0f, *num_rebuilt_leading = 0;
*num_rebuilt_leading < leading_outliers;
(*num_rebuilt_leading)++) {
float char_certainty = word->best_choice->certainty(*num_rebuilt_leading);
if (char_certainty > *unlikely_threshold) {
break;
}
if (char_certainty < *leading_certainty) {
*leading_certainty = char_certainty;
}
}
// Calculate num_trailing and trailing_certainty.
for (*trailing_certainty = 0.0f, *num_rebuilt_trailing = 0;
*num_rebuilt_trailing < trailing_outliers;
(*num_rebuilt_trailing)++) {
int blob_idx = num_blobs - 1 - *num_rebuilt_trailing;
float char_certainty = word->best_choice->certainty(blob_idx);
if (char_certainty > *unlikely_threshold) {
break;
}
if (char_certainty < *trailing_certainty) {
*trailing_certainty = char_certainty;
}
}
}
/**
* Try splitting off the given number of (chopped) blobs from the front and
* back of the given word and recognizing the pieces.
*
* @param[in] num_chopped_leading how many chopped blobs from the left
* end of the word to chop off and try recognizing as a
* superscript (or subscript)
* @param[in] leading_certainty the (minimum) certainty had by the
* characters in the original leading section.
* @param[in] leading_pos "super" or "sub" (for debugging)
* @param[in] num_chopped_trailing how many chopped blobs from the right
* end of the word to chop off and try recognizing as a
* superscript (or subscript)
* @param[in] trailing_certainty the (minimum) certainty had by the
* characters in the original trailing section.
* @param[in] trailing_pos "super" or "sub" (for debugging)
* @param[in] word the word to try to chop up.
* @param[out] is_good do we believe our result?
* @param[out] retry_rebuild_leading, retry_rebuild_trailing
* If non-zero, and !is_good, then the caller may have luck trying
* to split the returned word with this number of (rebuilt) leading
* and trailing blobs / unichars.
* @return A word which is the result of re-recognizing as asked.
*/
WERD_RES *Tesseract::TrySuperscriptSplits(
int num_chopped_leading, float leading_certainty, ScriptPos leading_pos,
int num_chopped_trailing, float trailing_certainty,
ScriptPos trailing_pos,
WERD_RES *word,
bool *is_good,
int *retry_rebuild_leading, int *retry_rebuild_trailing) {
int num_chopped = word->chopped_word->NumBlobs();
*retry_rebuild_leading = *retry_rebuild_trailing = 0;
// Chop apart the word into up to three pieces.
BlamerBundle *bb0 = NULL;
BlamerBundle *bb1 = NULL;
WERD_RES *prefix = NULL;
WERD_RES *core = NULL;
WERD_RES *suffix = NULL;
if (num_chopped_leading > 0) {
prefix = new WERD_RES(*word);
split_word(prefix, num_chopped_leading, &core, &bb0);
} else {
core = new WERD_RES(*word);
}
if (num_chopped_trailing > 0) {
int split_pt = num_chopped - num_chopped_trailing - num_chopped_leading;
split_word(core, split_pt, &suffix, &bb1);
}
// Recognize the pieces in turn.
int saved_cp_multiplier = classify_class_pruner_multiplier;
int saved_im_multiplier = classify_integer_matcher_multiplier;
if (prefix) {
// Turn off Tesseract's y-position penalties for the leading superscript.
classify_class_pruner_multiplier.set_value(0);
classify_integer_matcher_multiplier.set_value(0);
// Adjust our expectations about the baseline for this prefix.
if (superscript_debug >= 3) {
tprintf(" recognizing first %d chopped blobs\n", num_chopped_leading);
}
recog_word_recursive(prefix);
if (superscript_debug >= 2) {
tprintf(" The leading bits look like %s %s\n",
ScriptPosToString(leading_pos),
prefix->best_choice->unichar_string().string());
}
// Restore the normal y-position penalties.
classify_class_pruner_multiplier.set_value(saved_cp_multiplier);
classify_integer_matcher_multiplier.set_value(saved_im_multiplier);
}
if (superscript_debug >= 3) {
tprintf(" recognizing middle %d chopped blobs\n",
num_chopped - num_chopped_leading - num_chopped_trailing);
}
if (suffix) {
// Turn off Tesseract's y-position penalties for the trailing superscript.
classify_class_pruner_multiplier.set_value(0);
classify_integer_matcher_multiplier.set_value(0);
if (superscript_debug >= 3) {
tprintf(" recognizing last %d chopped blobs\n", num_chopped_trailing);
}
recog_word_recursive(suffix);
if (superscript_debug >= 2) {
tprintf(" The trailing bits look like %s %s\n",
ScriptPosToString(trailing_pos),
suffix->best_choice->unichar_string().string());
}
// Restore the normal y-position penalties.
classify_class_pruner_multiplier.set_value(saved_cp_multiplier);
classify_integer_matcher_multiplier.set_value(saved_im_multiplier);
}
// Evaluate whether we think the results are believably better
// than what we already had.
bool good_prefix = !prefix || BelievableSuperscript(
superscript_debug >= 1, *prefix,
superscript_bettered_certainty * leading_certainty,
retry_rebuild_leading, NULL);
bool good_suffix = !suffix || BelievableSuperscript(
superscript_debug >= 1, *suffix,
superscript_bettered_certainty * trailing_certainty,
NULL, retry_rebuild_trailing);
*is_good = good_prefix && good_suffix;
if (!*is_good && !*retry_rebuild_leading && !*retry_rebuild_trailing) {
// None of it is any good. Quit now.
delete core;
delete prefix;
delete suffix;
return NULL;
}
recog_word_recursive(core);
// Now paste the results together into core.
if (suffix) {
suffix->SetAllScriptPositions(trailing_pos);
join_words(core, suffix, bb1);
}
if (prefix) {
prefix->SetAllScriptPositions(leading_pos);
join_words(prefix, core, bb0);
core = prefix;
prefix = NULL;
}
if (superscript_debug >= 1) {
tprintf("%s superscript fix: %s\n", *is_good ? "ACCEPT" : "REJECT",
core->best_choice->unichar_string().string());
}
return core;
}
/**
* Return whether this is believable superscript or subscript text.
*
* We insist that:
* + there are no punctuation marks.
* + there are no italics.
* + no normal-sized character is smaller than superscript_scaledown_ratio
* of what it ought to be, and
* + each character is at least as certain as certainty_threshold.
*
* @param[in] debug If true, spew debug output
* @param[in] word The word whose best_choice we're evaluating
* @param[in] certainty_threshold If any of the characters have less
* certainty than this, reject.
* @param[out] left_ok How many left-side characters were ok?
* @param[out] right_ok How many right-side characters were ok?
* @return Whether the complete best choice is believable as a superscript.
*/
bool Tesseract::BelievableSuperscript(bool debug,
const WERD_RES &word,
float certainty_threshold,
int *left_ok,
int *right_ok) const {
int initial_ok_run_count = 0;
int ok_run_count = 0;
float worst_certainty = 0.0f;
const WERD_CHOICE &wc = *word.best_choice;
const UnicityTable<FontInfo>& fontinfo_table = get_fontinfo_table();
for (int i = 0; i < wc.length(); i++) {
TBLOB *blob = word.rebuild_word->blobs[i];
UNICHAR_ID unichar_id = wc.unichar_id(i);
float char_certainty = wc.certainty(i);
bool bad_certainty = char_certainty < certainty_threshold;
bool is_punc = wc.unicharset()->get_ispunctuation(unichar_id);
bool is_italic = word.fontinfo && word.fontinfo->is_italic();
BLOB_CHOICE *choice = word.GetBlobChoice(i);
if (choice && fontinfo_table.size() > 0) {
// Get better information from the specific choice, if available.
int font_id1 = choice->fontinfo_id();
bool font1_is_italic = font_id1 >= 0
? fontinfo_table.get(font_id1).is_italic() : false;
int font_id2 = choice->fontinfo_id2();
is_italic = font1_is_italic &&
(font_id2 < 0 || fontinfo_table.get(font_id2).is_italic());
}
float height_fraction = 1.0f;
float char_height = blob->bounding_box().height();
float normal_height = char_height;
if (wc.unicharset()->top_bottom_useful()) {
int min_bot, max_bot, min_top, max_top;
wc.unicharset()->get_top_bottom(unichar_id,
&min_bot, &max_bot,
&min_top, &max_top);
float hi_height = max_top - max_bot;
float lo_height = min_top - min_bot;
normal_height = (hi_height + lo_height) / 2;
if (normal_height >= kBlnXHeight) {
// Only ding characters that we have decent information for because
// they're supposed to be normal sized, not tiny specks or dashes.
height_fraction = char_height / normal_height;
}
}
bool bad_height = height_fraction < superscript_scaledown_ratio;
if (debug) {
if (is_italic) {
tprintf(" Rejecting: superscript is italic.\n");
}
if (is_punc) {
tprintf(" Rejecting: punctuation present.\n");
}
const char *char_str = wc.unicharset()->id_to_unichar(unichar_id);
if (bad_certainty) {
tprintf(" Rejecting: don't believe character %s with certainty %.2f "
"which is less than threshold %.2f\n", char_str,
char_certainty, certainty_threshold);
}
if (bad_height) {
tprintf(" Rejecting: character %s seems too small @ %.2f versus "
"expected %.2f\n", char_str, char_height, normal_height);
}
}
if (bad_certainty || bad_height || is_punc || is_italic) {
if (ok_run_count == i) {
initial_ok_run_count = ok_run_count;
}
ok_run_count = 0;
} else {
ok_run_count++;
}
if (char_certainty < worst_certainty) {
worst_certainty = char_certainty;
}
}
bool all_ok = ok_run_count == wc.length();
if (all_ok && debug) {
tprintf(" Accept: worst revised certainty is %.2f\n", worst_certainty);
}
if (!all_ok) {
if (left_ok) *left_ok = initial_ok_run_count;
if (right_ok) *right_ok = ok_run_count;
}
return all_ok;
}
} // namespace tesseract

131
ccmain/tesseractclass.cpp
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@ -77,9 +77,6 @@ Tesseract::Tesseract()
"documents while performing ocr.", this->params()),
STRING_MEMBER(tessedit_write_params_to_file, "",
"Write all parameters to the given file.", this->params()),
BOOL_MEMBER(tessedit_adapt_to_char_fragments, true,
"Adapt to words that contain "
" a character composed form fragments", this->params()),
BOOL_MEMBER(tessedit_adaption_debug, false, "Generate and print debug"
" information for adaption", this->params()),
INT_MEMBER(bidi_debug, 0, "Debug level for BiDi", this->params()),
@ -103,6 +100,8 @@ Tesseract::Tesseract()
"Call Tess to learn blobs", this->params()),
BOOL_MEMBER(tessedit_dump_choices, false,
"Dump char choices", this->params()),
BOOL_MEMBER(tessedit_timing_debug, false, "Print timing stats",
this->params()),
BOOL_MEMBER(tessedit_fix_fuzzy_spaces, true,
"Try to improve fuzzy spaces", this->params()),
BOOL_MEMBER(tessedit_unrej_any_wd, false,
@ -117,7 +116,7 @@ Tesseract::Tesseract()
"Output font info per char", this->params()),
BOOL_MEMBER(tessedit_debug_block_rejection, false,
"Block and Row stats", this->params()),
BOOL_MEMBER(tessedit_enable_bigram_correction, false,
BOOL_MEMBER(tessedit_enable_bigram_correction, true,
"Enable correction based on the word bigram dictionary.",
this->params()),
INT_MEMBER(tessedit_bigram_debug, 0,
@ -142,8 +141,6 @@ Tesseract::Tesseract()
"good_quality_doc gte good char limit", this->params()),
INT_MEMBER(quality_min_initial_alphas_reqd, 2,
"alphas in a good word", this->params()),
BOOL_MEMBER(tessedit_tess_adapt_to_rejmap, false,
"Use reject map to control Tesseract adaption", this->params()),
INT_MEMBER(tessedit_tess_adaption_mode, 0x27,
"Adaptation decision algorithm for tess", this->params()),
BOOL_MEMBER(tessedit_minimal_rej_pass1, false,
@ -154,14 +151,14 @@ Tesseract::Tesseract()
"Log matcher activity", this->params()),
INT_MEMBER(tessedit_test_adaption_mode, 3,
"Adaptation decision algorithm for tess", this->params()),
BOOL_MEMBER(save_blob_choices, false,
"Save the results of the recognition step (blob_choices)"
" within the corresponding WERD_CHOICE", this->params()),
BOOL_MEMBER(test_pt, false, "Test for point", this->params()),
double_MEMBER(test_pt_x, 99999.99, "xcoord", this->params()),
double_MEMBER(test_pt_y, 99999.99, "ycoord", this->params()),
INT_MEMBER(paragraph_debug_level, 0, "Print paragraph debug info.",
this->params()),
BOOL_MEMBER(paragraph_text_based, true,
"Run paragraph detection on the post-text-recognition "
"(more accurate)", this->params()),
INT_MEMBER(cube_debug_level, 0, "Print cube debug info.", this->params()),
STRING_MEMBER(outlines_odd, "%| ", "Non standard number of outlines",
this->params()),
@ -282,6 +279,30 @@ Tesseract::Tesseract()
this->params()),
INT_MEMBER(x_ht_min_change, 8,
"Min change in xht before actually trying it", this->params()),
INT_MEMBER(superscript_debug, 0, "Debug level for sub & superscript fixer",
this->params()),
double_MEMBER(superscript_worse_certainty, 2.0, "How many times worse "
"certainty does a superscript position glyph need to be for "
"us to try classifying it as a char with a different "
"baseline?", this->params()),
double_MEMBER(superscript_bettered_certainty, 0.97, "What reduction in "
"badness do we think sufficient to choose a superscript "
"over what we'd thought. For example, a value of 0.6 means "
"we want to reduce badness of certainty by at least 40%",
this->params()),
double_MEMBER(superscript_scaledown_ratio, 0.4,
"A superscript scaled down more than this is unbelievably "
"small. For example, 0.3 means we expect the font size to "
"be no smaller than 30% of the text line font size.",
this->params()),
double_MEMBER(subscript_max_y_top, 0.5,
"Maximum top of a character measured as a multiple of "
"x-height above the baseline for us to reconsider whether "
"it's a subscript.", this->params()),
double_MEMBER(superscript_min_y_bottom, 0.3,
"Minimum bottom of a character measured as a multiple of "
"x-height above the baseline for us to reconsider whether "
"it's a superscript.", this->params()),
BOOL_MEMBER(tessedit_write_block_separators, false,
"Write block separators in output", this->params()),
BOOL_MEMBER(tessedit_write_rep_codes, false,
@ -314,8 +335,6 @@ Tesseract::Tesseract()
BOOL_MEMBER(tessedit_consistent_reps, true,
"Force all rep chars the same", this->params()),
INT_MEMBER(tessedit_reject_mode, 0, "Rejection algorithm", this->params()),
INT_MEMBER(tessedit_ok_mode, 5,
"Acceptance decision algorithm", this->params()),
BOOL_MEMBER(tessedit_rejection_debug, false,
"Adaption debug", this->params()),
BOOL_MEMBER(tessedit_flip_0O, true,
@ -365,22 +384,111 @@ Tesseract::Tesseract()
" TessdataManager functions.", this->params()),
STRING_MEMBER(tessedit_load_sublangs, "",
"List of languages to load with this one", this->params()),
BOOL_MEMBER(tessedit_use_primary_params_model, false,
"In multilingual mode use params model of the"
" primary language", this->params()),
double_MEMBER(min_orientation_margin, 7.0,
"Min acceptable orientation margin", this->params()),
BOOL_MEMBER(textord_tabfind_show_vlines, false, "Debug line finding",
this->params()),
BOOL_MEMBER(textord_use_cjk_fp_model, FALSE, "Use CJK fixed pitch model",
this->params()),
BOOL_MEMBER(poly_allow_detailed_fx, false,
"Allow feature extractors to see the original outline",
this->params()),
BOOL_INIT_MEMBER(tessedit_init_config_only, false,
"Only initialize with the config file. Useful if the "
"instance is not going to be used for OCR but say only "
"for layout analysis.", this->params()),
BOOL_MEMBER(textord_equation_detect, false, "Turn on equation detector",
this->params()),
// The following parameters were deprecated and removed from their original
// locations. The parameters are temporarily kept here to give Tesseract
// users a chance to updated their [lang].traineddata and config files
// without introducing failures during Tesseract initialization.
// TODO(ocr-team): remove these parameters from the code once we are
// reasonably sure that Tesseract users have updated their data files.
//
// BEGIN DEPRECATED PARAMETERS
INT_MEMBER(tessedit_ok_mode, 5,
"Acceptance decision algorithm", this->params()),
BOOL_INIT_MEMBER(load_fixed_length_dawgs, true, "Load fixed length dawgs"
" (e.g. for non-space delimited languages)",
this->params()),
INT_MEMBER(segment_debug, 0, "Debug the whole segmentation process",
this->params()),
BOOL_MEMBER(permute_debug, 0, "Debug char permutation process",
this->params()),
double_MEMBER(bestrate_pruning_factor, 2.0, "Multiplying factor of"
" current best rate to prune other hypotheses",
this->params()),
BOOL_MEMBER(permute_script_word, 0,
"Turn on word script consistency permuter",
this->params()),
BOOL_MEMBER(segment_segcost_rating, 0,
"incorporate segmentation cost in word rating?",
this->params()),
double_MEMBER(segment_reward_script, 0.95,
"Score multipler for script consistency within a word. "
"Being a 'reward' factor, it should be <= 1. "
"Smaller value implies bigger reward.",
this->params()),
BOOL_MEMBER(permute_fixed_length_dawg, 0,
"Turn on fixed-length phrasebook search permuter",
this->params()),
BOOL_MEMBER(permute_chartype_word, 0,
"Turn on character type (property) consistency permuter",
this->params()),
double_MEMBER(segment_reward_chartype, 0.97,
"Score multipler for char type consistency within a word. ",
this->params()),
double_MEMBER(segment_reward_ngram_best_choice, 0.99,
"Score multipler for ngram permuter's best choice"
" (only used in the Han script path).",
this->params()),
BOOL_MEMBER(ngram_permuter_activated, false,
"Activate character-level n-gram-based permuter",
this->params()),
BOOL_MEMBER(permute_only_top, false, "Run only the top choice permuter",
this->params()),
INT_MEMBER(language_model_fixed_length_choices_depth, 3,
"Depth of blob choice lists to explore"
" when fixed length dawgs are on",
this->params()),
BOOL_MEMBER(use_new_state_cost, FALSE,
"use new state cost heuristics for segmentation state"
" evaluation", this->params()),
double_MEMBER(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.",
this->params()),
double_MEMBER(heuristic_weight_rating, 1.0,
"weight associated with char rating in combined cost of"
"state", this->params()),
double_MEMBER(heuristic_weight_width, 1000.0,
"weight associated with width evidence in combined cost of"
" state", this->params()),
double_MEMBER(heuristic_weight_seamcut, 0.0,
"weight associated with seam cut in combined cost of state",
this->params()),
double_MEMBER(heuristic_max_char_wh_ratio, 2.0,
"max char width-to-height ratio allowed in segmentation",
this->params()),
BOOL_MEMBER(enable_new_segsearch, true,
"Enable new segmentation search path.", this->params()),
double_MEMBER(segsearch_max_fixed_pitch_char_wh_ratio, 2.0,
"Maximum character width-to-height ratio for"
" fixed-pitch fonts",
this->params()),
// END DEPRECATED PARAMETERS
backup_config_file_(NULL),
pix_binary_(NULL),
cube_binary_(NULL),
pix_grey_(NULL),
pix_thresholds_(NULL),
source_resolution_(0),
textord_(this),
right_to_left_(false),
@ -414,6 +522,7 @@ void Tesseract::Clear() {
pixDestroy(&pix_binary_);
pixDestroy(&cube_binary_);
pixDestroy(&pix_grey_);
pixDestroy(&pix_thresholds_);
pixDestroy(&scaled_color_);
deskew_ = FCOORD(1.0f, 0.0f);
reskew_ = FCOORD(1.0f, 0.0f);

184
ccmain/tesseractclass.h
View File

@ -181,6 +181,10 @@ class Tesseract : public Wordrec {
Pix* BestPix() const {
return pix_grey_ != NULL ? pix_grey_ : pix_binary_;
}
void set_pix_thresholds(Pix* thresholds) {
pixDestroy(&pix_thresholds_);
pix_thresholds_ = thresholds;
}
int source_resolution() const {
return source_resolution_;
}
@ -277,10 +281,7 @@ class Tesseract : public Wordrec {
ACCEPTABLE_WERD_TYPE acceptable_word_string(const UNICHARSET& char_set,
const char *s,
const char *lengths);
void match_word_pass2( //recog one word
WERD_RES *word, //word to do
ROW *row,
BLOCK* block);
void match_word_pass_n(int pass_n, WERD_RES *word, ROW *row, BLOCK* block);
void classify_word_pass2(BLOCK* block, ROW *row, WERD_RES *word);
void ReportXhtFixResult(bool accept_new_word, float new_x_ht,
WERD_RES* word, WERD_RES* new_word);
@ -288,12 +289,38 @@ class Tesseract : public Wordrec {
bool TrainedXheightFix(WERD_RES *word, BLOCK* block, ROW *row);
BOOL8 recog_interactive(BLOCK* block, ROW* row, WERD_RES* word_res);
void set_word_fonts(
WERD_RES *word, // set fonts of this word
BLOB_CHOICE_LIST_CLIST *blob_choices); // detailed results
// Set fonts of this word.
void set_word_fonts(WERD_RES *word);
void font_recognition_pass(PAGE_RES* page_res);
BOOL8 check_debug_pt(WERD_RES *word, int location);
//// superscript.cpp ////////////////////////////////////////////////////
bool SubAndSuperscriptFix(WERD_RES *word_res);
void GetSubAndSuperscriptCandidates(const WERD_RES *word,
int *num_rebuilt_leading,
ScriptPos *leading_pos,
float *leading_certainty,
int *num_rebuilt_trailing,
ScriptPos *trailing_pos,
float *trailing_certainty,
float *avg_certainty,
float *unlikely_threshold);
WERD_RES *TrySuperscriptSplits(int num_chopped_leading,
float leading_certainty,
ScriptPos leading_pos,
int num_chopped_trailing,
float trailing_certainty,
ScriptPos trailing_pos,
WERD_RES *word,
bool *is_good,
int *retry_leading,
int *retry_trailing);
bool BelievableSuperscript(bool debug,
const WERD_RES &word,
float certainty_threshold,
int *left_ok,
int *right_ok) const;
//// cube_control.cpp ///////////////////////////////////////////////////
bool init_cube_objects(bool load_combiner,
TessdataManager *tessdata_manager);
@ -314,7 +341,6 @@ class Tesseract : public Wordrec {
// Sets up a fake result and returns false if something goes wrong.
bool cube_recognize(CubeObject *cube_obj, BLOCK* block, WERD_RES *word);
void fill_werd_res(const BoxWord& cube_box_word,
WERD_CHOICE* cube_werd_choice,
const char* cube_best_str,
WERD_RES* tess_werd_res);
bool extract_cube_state(CubeObject* cube_obj, int* num_chars,
@ -429,13 +455,10 @@ class Tesseract : public Wordrec {
// #ifndef GRAPHICS_DISABLED
BOOL8 word_dumper(BLOCK* block, ROW* row, WERD_RES* word_res);
// #endif // GRAPHICS_DISABLED
void blob_feature_display(PAGE_RES* page_res, const TBOX& selection_box);
//// reject.h //////////////////////////////////////////////////////////
void make_reject_map( //make rej map for wd //detailed results
WERD_RES *word,
BLOB_CHOICE_LIST_CLIST *blob_choices,
ROW *row,
inT16 pass //1st or 2nd?
);
// make rej map for word
void make_reject_map(WERD_RES *word, ROW *row, inT16 pass);
BOOL8 one_ell_conflict(WERD_RES *word_res, BOOL8 update_map);
inT16 first_alphanum_index(const char *word,
const char *word_lengths);
@ -456,8 +479,6 @@ class Tesseract : public Wordrec {
WERD_RES *word,
ROW *row);
void nn_recover_rejects(WERD_RES *word, ROW *row);
BOOL8 test_ambig_word( //test for ambiguity
WERD_RES *word);
void set_done( //set done flag
WERD_RES *word,
inT16 pass);
@ -472,12 +493,16 @@ class Tesseract : public Wordrec {
uinT16 mode);
//// tfacepp.cpp ///////////////////////////////////////////////////////
void recog_word_recursive(WERD_RES* word,
BLOB_CHOICE_LIST_CLIST *blob_choices);
void recog_word(WERD_RES *word,
BLOB_CHOICE_LIST_CLIST *blob_choices);
void split_and_recog_word(WERD_RES* word,
BLOB_CHOICE_LIST_CLIST *blob_choices);
void recog_word_recursive(WERD_RES* word);
void recog_word(WERD_RES *word);
void split_and_recog_word(WERD_RES* word);
void split_word(WERD_RES *word,
int split_pt,
WERD_RES **right_piece,
BlamerBundle **orig_blamer_bundle) const;
void join_words(WERD_RES *word,
WERD_RES *word2,
BlamerBundle *orig_bb) const;
//// fixspace.cpp ///////////////////////////////////////////////////////
BOOL8 digit_or_numeric_punct(WERD_RES *word, int char_position);
inT16 eval_word_spacing(WERD_RES_LIST &word_res_list);
@ -495,7 +520,6 @@ class Tesseract : public Wordrec {
PAGE_RES *page_res);
void dump_words(WERD_RES_LIST &perm, inT16 score,
inT16 mode, BOOL8 improved);
BOOL8 uniformly_spaced(WERD_RES *word);
BOOL8 fixspace_thinks_word_done(WERD_RES *word);
inT16 worst_noise_blob(WERD_RES *word_res, float *worst_noise_score);
float blob_noise_score(TBLOB *blob);
@ -538,14 +562,9 @@ class Tesseract : public Wordrec {
void tess_add_doc_word( //test acceptability
WERD_CHOICE *word_choice //after context
);
void tess_segment_pass1(WERD_RES *word,
BLOB_CHOICE_LIST_CLIST *blob_choices);
void tess_segment_pass2(WERD_RES *word,
BLOB_CHOICE_LIST_CLIST *blob_choices);
BOOL8 tess_acceptable_word( //test acceptability
WERD_CHOICE *word_choice, //after context
WERD_CHOICE *raw_choice //before context
);
void tess_segment_pass_n(int pass_n, WERD_RES *word);
bool tess_acceptable_word(WERD_RES *word);
//// applybox.cpp //////////////////////////////////////////////////////
// Applies the box file based on the image name fname, and resegments
// the words in the block_list (page), with:
@ -649,7 +668,7 @@ class Tesseract : public Wordrec {
// estimate.
float ComputeCompatibleXheight(WERD_RES *word_res);
//// Data members ///////////////////////////////////////////////////////
// TODO(ocr-team): Remove obsolete parameters.
// TODO(ocr-team): Find and remove obsolete parameters.
BOOL_VAR_H(tessedit_resegment_from_boxes, false,
"Take segmentation and labeling from box file");
BOOL_VAR_H(tessedit_resegment_from_line_boxes, false,
@ -684,9 +703,6 @@ class Tesseract : public Wordrec {
"documents while performing ocr.");
STRING_VAR_H(tessedit_write_params_to_file, "",
"Write all parameters to the given file.");
BOOL_VAR_H(tessedit_adapt_to_char_fragments, true,
"Adapt to words that contain "
" a character composed form fragments");
BOOL_VAR_H(tessedit_adaption_debug, false,
"Generate and print debug information for adaption");
INT_VAR_H(bidi_debug, 0, "Debug level for BiDi");
@ -706,6 +722,7 @@ class Tesseract : public Wordrec {
BOOL_VAR_H(tessedit_display_outwords, false, "Draw output words");
BOOL_VAR_H(tessedit_training_tess, false, "Call Tess to learn blobs");
BOOL_VAR_H(tessedit_dump_choices, false, "Dump char choices");
BOOL_VAR_H(tessedit_timing_debug, false, "Print timing stats");
BOOL_VAR_H(tessedit_fix_fuzzy_spaces, true,
"Try to improve fuzzy spaces");
BOOL_VAR_H(tessedit_unrej_any_wd, false,
@ -716,7 +733,7 @@ class Tesseract : public Wordrec {
"Add words to the document dictionary");
BOOL_VAR_H(tessedit_debug_fonts, false, "Output font info per char");
BOOL_VAR_H(tessedit_debug_block_rejection, false, "Block and Row stats");
BOOL_VAR_H(tessedit_enable_bigram_correction, false,
BOOL_VAR_H(tessedit_enable_bigram_correction, true,
"Enable correction based on the word bigram dictionary.");
INT_VAR_H(tessedit_bigram_debug, 0, "Amount of debug output for bigram "
"correction.");
@ -731,8 +748,6 @@ class Tesseract : public Wordrec {
"good_quality_doc lte outline error limit");
double_VAR_H(quality_char_pc, 0.95, "good_quality_doc gte good char limit");
INT_VAR_H(quality_min_initial_alphas_reqd, 2, "alphas in a good word");
BOOL_VAR_H(tessedit_tess_adapt_to_rejmap, false,
"Use reject map to control Tesseract adaption");
INT_VAR_H(tessedit_tess_adaption_mode, 0x27,
"Adaptation decision algorithm for tess");
BOOL_VAR_H(tessedit_minimal_rej_pass1, false,
@ -741,13 +756,13 @@ class Tesseract : public Wordrec {
BOOL_VAR_H(tessedit_matcher_log, false, "Log matcher activity");
INT_VAR_H(tessedit_test_adaption_mode, 3,
"Adaptation decision algorithm for tess");
BOOL_VAR_H(save_blob_choices, false,
"Save the results of the recognition step"
" (blob_choices) within the corresponding WERD_CHOICE");
BOOL_VAR_H(test_pt, false, "Test for point");
double_VAR_H(test_pt_x, 99999.99, "xcoord");
double_VAR_H(test_pt_y, 99999.99, "ycoord");
INT_VAR_H(paragraph_debug_level, 0, "Print paragraph debug info.");
BOOL_VAR_H(paragraph_text_based, true,
"Run paragraph detection on the post-text-recognition "
"(more accurate)");
INT_VAR_H(cube_debug_level, 1, "Print cube debug info.");
STRING_VAR_H(outlines_odd, "%| ", "Non standard number of outlines");
STRING_VAR_H(outlines_2, "ij!?%\":;", "Non standard number of outlines");
@ -832,6 +847,26 @@ class Tesseract : public Wordrec {
INT_VAR_H(x_ht_acceptance_tolerance, 8,
"Max allowed deviation of blob top outside of font data");
INT_VAR_H(x_ht_min_change, 8, "Min change in xht before actually trying it");
INT_VAR_H(superscript_debug, 0, "Debug level for sub & superscript fixer");
double_VAR_H(superscript_worse_certainty, 2.0, "How many times worse "
"certainty does a superscript position glyph need to be for us "
"to try classifying it as a char with a different baseline?");
double_VAR_H(superscript_bettered_certainty, 0.97, "What reduction in "
"badness do we think sufficient to choose a superscript over "
"what we'd thought. For example, a value of 0.6 means we want "
"to reduce badness of certainty by 40%");
double_VAR_H(superscript_scaledown_ratio, 0.4,
"A superscript scaled down more than this is unbelievably "
"small. For example, 0.3 means we expect the font size to "
"be no smaller than 30% of the text line font size.");
double_VAR_H(subscript_max_y_top, 0.5,
"Maximum top of a character measured as a multiple of x-height "
"above the baseline for us to reconsider whether it's a "
"subscript.");
double_VAR_H(superscript_min_y_bottom, 0.3,
"Minimum bottom of a character measured as a multiple of "
"x-height above the baseline for us to reconsider whether it's "
"a superscript.");
BOOL_VAR_H(tessedit_write_block_separators, false,
"Write block separators in output");
BOOL_VAR_H(tessedit_write_rep_codes, false,
@ -856,7 +891,6 @@ class Tesseract : public Wordrec {
"Dont reject ANYTHING AT ALL");
BOOL_VAR_H(tessedit_consistent_reps, true, "Force all rep chars the same");
INT_VAR_H(tessedit_reject_mode, 0, "Rejection algorithm");
INT_VAR_H(tessedit_ok_mode, 5, "Acceptance decision algorithm");
BOOL_VAR_H(tessedit_rejection_debug, false, "Adaption debug");
BOOL_VAR_H(tessedit_flip_0O, true, "Contextual 0O O0 flips");
double_VAR_H(tessedit_lower_flip_hyphen, 1.5,
@ -888,17 +922,81 @@ class Tesseract : public Wordrec {
"Debug level for TessdataManager functions.");
STRING_VAR_H(tessedit_load_sublangs, "",
"List of languages to load with this one");
BOOL_VAR_H(tessedit_use_primary_params_model, false,
"In multilingual mode use params model of the primary language");
// Min acceptable orientation margin (difference in scores between top and 2nd
// choice in OSResults::orientations) to believe the page orientation.
double_VAR_H(min_orientation_margin, 7.0,
"Min acceptable orientation margin");
BOOL_VAR_H(textord_tabfind_show_vlines, false, "Debug line finding");
BOOL_VAR_H(textord_use_cjk_fp_model, FALSE, "Use CJK fixed pitch model");
BOOL_VAR_H(poly_allow_detailed_fx, false,
"Allow feature extractors to see the original outline");
BOOL_VAR_H(tessedit_init_config_only, false,
"Only initialize with the config file. Useful if the instance is "
"not going to be used for OCR but say only for layout analysis.");
BOOL_VAR_H(textord_equation_detect, false, "Turn on equation detector");
// The following parameters were deprecated and removed from their original
// locations. The parameters are temporarily kept here to give Tesseract
// users a chance to updated their [lang].traineddata and config files
// without introducing failures during Tesseract initialization.
// TODO(ocr-team): remove these parameters from the code once we are
// reasonably sure that Tesseract users have updated their data files.
//
// BEGIN DEPRECATED PARAMETERS
INT_VAR_H(tessedit_ok_mode, 5, "Acceptance decision algorithm");
BOOL_VAR_H(load_fixed_length_dawgs, true, "Load fixed length"
" dawgs (e.g. for non-space delimited languages)");
INT_VAR_H(segment_debug, 0, "Debug the whole segmentation process");
BOOL_VAR_H(permute_debug, 0, "char permutation debug");
double_VAR_H(bestrate_pruning_factor, 2.0, "Multiplying factor of"
" current best rate to prune other hypotheses");
BOOL_VAR_H(permute_script_word, 0,
"Turn on word script consistency permuter");
BOOL_VAR_H(segment_segcost_rating, 0,
"incorporate segmentation cost in word rating?");
double_VAR_H(segment_reward_script, 0.95,
"Score multipler for script consistency within a word. "
"Being a 'reward' factor, it should be <= 1. "
"Smaller value implies bigger reward.");
BOOL_VAR_H(permute_fixed_length_dawg, 0,
"Turn on fixed-length phrasebook search permuter");
BOOL_VAR_H(permute_chartype_word, 0,
"Turn on character type (property) consistency permuter");
double_VAR_H(segment_reward_chartype, 0.97,
"Score multipler for char type consistency within a word. ");
double_VAR_H(segment_reward_ngram_best_choice, 0.99,
"Score multipler for ngram permuter's best choice"
" (only used in the Han script path).");
BOOL_VAR_H(ngram_permuter_activated, false,
"Activate character-level n-gram-based permuter");
BOOL_VAR_H(permute_only_top, false, "Run only the top choice permuter");
INT_VAR_H(language_model_fixed_length_choices_depth, 3,
"Depth of blob choice lists to explore"
" when fixed length dawgs are on");
BOOL_VAR_H(use_new_state_cost, FALSE,
"use new state cost heuristics for segmentation state evaluation");
double_VAR_H(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_H(heuristic_weight_rating, 1,
"weight associated with char rating in combined cost of state");
double_VAR_H(heuristic_weight_width, 1000.0,
"weight associated with width evidence in combined cost of"
" state");
double_VAR_H(heuristic_weight_seamcut, 0,
"weight associated with seam cut in combined cost of state");
double_VAR_H(heuristic_max_char_wh_ratio, 2.0,
"max char width-to-height ratio allowed in segmentation");
BOOL_VAR_H(enable_new_segsearch, false,
"Enable new segmentation search path.");
double_VAR_H(segsearch_max_fixed_pitch_char_wh_ratio, 2.0,
"Maximum character width-to-height ratio for"
"fixed pitch fonts");
// END DEPRECATED PARAMETERS
//// ambigsrecog.cpp /////////////////////////////////////////////////////////
FILE *init_recog_training(const STRING &fname);
void recog_training_segmented(const STRING &fname,
@ -927,6 +1025,8 @@ class Tesseract : public Wordrec {
Pix* cube_binary_;
// Grey-level input image if the input was not binary, otherwise NULL.
Pix* pix_grey_;
// Thresholds that were used to generate the thresholded image from grey.
Pix* pix_thresholds_;
// Input image resolution after any scaling. The resolution is not well
// transmitted by operations on Pix, so we keep an independent record here.
int source_resolution_;