tesseract/wordrec/lm_state.h
2017-05-03 23:08:49 +02:00

239 lines
9.4 KiB
C++

///////////////////////////////////////////////////////////////////////
// File: lm_state.h
// Description: Structures and functionality for capturing the state of
// segmentation search guided by the language model.
//
// Author: Rika Antonova
// Created: Mon Jun 20 11:26:43 PST 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.
//
///////////////////////////////////////////////////////////////////////
#ifndef TESSERACT_WORDREC_LANGUAGE_MODEL_DEFS_H_
#define TESSERACT_WORDREC_LANGUAGE_MODEL_DEFS_H_
#include "associate.h"
#include "elst.h"
#include "dawg.h"
#include "lm_consistency.h"
#include "matrix.h"
#include "ratngs.h"
#include "stopper.h"
#include "strngs.h"
namespace tesseract {
/// Used for expressing various language model flags.
typedef unsigned char LanguageModelFlagsType;
/// The following structs are used for storing the state of the language model
/// in the segmentation search graph. In this graph the nodes are BLOB_CHOICEs
/// and the links are the relationships between the underlying blobs (see
/// segsearch.h for a more detailed description).
///
/// Each of the BLOB_CHOICEs contains LanguageModelState struct, which has
/// a list of N best paths (list of ViterbiStateEntry) explored by the Viterbi
/// search leading up to and including this BLOB_CHOICE.
///
/// Each ViterbiStateEntry contains information from various components of the
/// language model: dawgs in which the path is found, character ngram model
/// probability of the path, script/chartype/font consistency info, state for
/// language-specific heuristics (e.g. hyphenated and compound words,
/// lower/upper case preferences, etc).
///
/// Each ViterbiStateEntry also contains the parent pointer, so that the path
/// that it represents (WERD_CHOICE) can be constructed by following these
/// parent pointers.
/// Struct for storing additional information used by Dawg language model
/// component. It stores the set of active dawgs in which the sequence of
/// letters on a path can be found.
struct LanguageModelDawgInfo {
LanguageModelDawgInfo(const DawgPositionVector *a, PermuterType pt)
: active_dawgs(*a), permuter(pt) {}
DawgPositionVector active_dawgs;
PermuterType permuter;
};
/// Struct for storing additional information used by Ngram language model
/// component.
struct LanguageModelNgramInfo {
LanguageModelNgramInfo(const char *c, int l, bool p, float nc, float ncc)
: context(c), context_unichar_step_len(l), pruned(p), ngram_cost(nc),
ngram_and_classifier_cost(ncc) {}
STRING context; //< context string
/// Length of the context measured by advancing using UNICHAR::utf8_step()
/// (should be at most the order of the character ngram model used).
int context_unichar_step_len;
/// The paths with pruned set are pruned out from the perspective of the
/// character ngram model. They are explored further because they represent
/// a dictionary match or a top choice. Thus ngram_info is still computed
/// for them in order to calculate the combined cost.
bool pruned;
/// -ln(P_ngram_model(path))
float ngram_cost;
/// -[ ln(P_classifier(path)) + scale_factor * ln(P_ngram_model(path)) ]
float ngram_and_classifier_cost;
};
/// Struct for storing the information about a path in the segmentation graph
/// explored by Viterbi search.
struct ViterbiStateEntry : public ELIST_LINK {
ViterbiStateEntry(ViterbiStateEntry *pe,
BLOB_CHOICE *b, float c, float ol,
const LMConsistencyInfo &ci,
const AssociateStats &as,
LanguageModelFlagsType tcf,
LanguageModelDawgInfo *d,
LanguageModelNgramInfo *n,
const char *debug_uch)
: cost(c), curr_b(b), parent_vse(pe), competing_vse(NULL),
ratings_sum(b->rating()),
min_certainty(b->certainty()), adapted(b->IsAdapted()), length(1),
outline_length(ol), consistency_info(ci), associate_stats(as),
top_choice_flags(tcf), dawg_info(d), ngram_info(n),
updated(true) {
debug_str = (debug_uch == NULL) ? NULL : new STRING();
if (pe != NULL) {
ratings_sum += pe->ratings_sum;
if (pe->min_certainty < min_certainty) {
min_certainty = pe->min_certainty;
}
adapted += pe->adapted;
length += pe->length;
outline_length += pe->outline_length;
if (debug_uch != NULL) *debug_str += *(pe->debug_str);
}
if (debug_str != NULL && debug_uch != NULL) *debug_str += debug_uch;
}
~ViterbiStateEntry() {
delete dawg_info;
delete ngram_info;
delete debug_str;
}
/// Comparator function for sorting ViterbiStateEntry_LISTs in
/// non-increasing order of costs.
static int Compare(const void *e1, const void *e2) {
const ViterbiStateEntry *ve1 =
*reinterpret_cast<const ViterbiStateEntry * const *>(e1);
const ViterbiStateEntry *ve2 =
*reinterpret_cast<const ViterbiStateEntry * const *>(e2);
return (ve1->cost < ve2->cost) ? -1 : 1;
}
inline bool Consistent() const {
if (dawg_info != NULL && consistency_info.NumInconsistentCase() == 0) {
return true;
}
return consistency_info.Consistent();
}
/// Returns true if this VSE has an alphanumeric character as its classifier
/// result.
bool HasAlnumChoice(const UNICHARSET& unicharset) {
if (curr_b == NULL) return false;
UNICHAR_ID unichar_id = curr_b->unichar_id();
if (unicharset.get_isalpha(unichar_id) ||
unicharset.get_isdigit(unichar_id))
return true;
return false;
}
void Print(const char *msg) const;
/// The cost is an adjusted ratings sum, that is adjusted by all the language
/// model components that use Viterbi search.
float cost;
/// Pointers to BLOB_CHOICE and parent ViterbiStateEntry (not owned by this).
BLOB_CHOICE *curr_b;
ViterbiStateEntry *parent_vse;
/// Pointer to a case-competing ViterbiStateEntry in the same list that
/// represents a path ending in the same letter of the opposite case.
ViterbiStateEntry *competing_vse;
/// Various information about the characters on the path represented
/// by this ViterbiStateEntry.
float ratings_sum; //< sum of ratings of character on the path
float min_certainty; //< minimum certainty on the path
int adapted; //< number of BLOB_CHOICES from adapted templates
int length; //< number of characters on the path
float outline_length; //< length of the outline so far
LMConsistencyInfo consistency_info; //< path consistency info
AssociateStats associate_stats; //< character widths/gaps/seams
/// Flags for marking the entry as a top choice path with
/// the smallest rating or lower/upper case letters).
LanguageModelFlagsType top_choice_flags;
/// Extra information maintained by Dawg language model component
/// (owned by ViterbiStateEntry).
LanguageModelDawgInfo *dawg_info;
/// Extra information maintained by Ngram language model component
/// (owned by ViterbiStateEntry).
LanguageModelNgramInfo *ngram_info;
bool updated; //< set to true if the entry has just been created/updated
/// UTF8 string representing the path corresponding to this vse.
/// Populated only in when language_model_debug_level > 0.
STRING *debug_str;
};
ELISTIZEH(ViterbiStateEntry);
/// Struct to store information maintained by various language model components.
struct LanguageModelState {
LanguageModelState() :
viterbi_state_entries_prunable_length(0),
viterbi_state_entries_prunable_max_cost(MAX_FLOAT32),
viterbi_state_entries_length(0) {}
~LanguageModelState() {}
/// Clears the viterbi search state back to its initial conditions.
void Clear();
void Print(const char *msg);
/// Storage for the Viterbi state.
ViterbiStateEntry_LIST viterbi_state_entries;
/// Number and max cost of prunable paths in viterbi_state_entries.
int viterbi_state_entries_prunable_length;
float viterbi_state_entries_prunable_max_cost;
/// Total number of entries in viterbi_state_entries.
int viterbi_state_entries_length;
};
/// Bundle together all the things pertaining to the best choice/state.
struct BestChoiceBundle {
explicit BestChoiceBundle(int matrix_dimension)
: updated(false), best_vse(NULL) {
beam.reserve(matrix_dimension);
for (int i = 0; i < matrix_dimension; ++i)
beam.push_back(new LanguageModelState);
}
~BestChoiceBundle() {}
/// Flag to indicate whether anything was changed.
bool updated;
/// Places to try to fix the word suggested by ambiguity checking.
DANGERR fixpt;
/// The beam. One LanguageModelState containing a list of ViterbiStateEntry
/// per row in the ratings matrix containing all VSEs whose BLOB_CHOICE is
/// somewhere in the corresponding row.
PointerVector<LanguageModelState> beam;
/// Best ViterbiStateEntry and BLOB_CHOICE.
ViterbiStateEntry *best_vse;
};
} // namespace tesseract
#endif // TESSERACT_WORDREC_LANGUAGE_MODEL_DEFS_H_