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https://github.com/tesseract-ocr/tesseract.git
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8f7be2e72c
Signed-off-by: Stefan Weil <sw@weilnetz.de>
393 lines
19 KiB
C++
393 lines
19 KiB
C++
///////////////////////////////////////////////////////////////////////
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// File: recodebeam.h
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// Description: Beam search to decode from the re-encoded CJK as a sequence of
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// smaller numbers in place of a single large code.
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// Author: Ray Smith
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// Created: Fri Mar 13 09:12:01 PDT 2015
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//
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// (C) Copyright 2015, Google Inc.
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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// http://www.apache.org/licenses/LICENSE-2.0
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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///////////////////////////////////////////////////////////////////////
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#ifndef THIRD_PARTY_TESSERACT_LSTM_RECODEBEAM_H_
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#define THIRD_PARTY_TESSERACT_LSTM_RECODEBEAM_H_
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#include "dawg.h"
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#include "dict.h"
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#include "genericheap.h"
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#include "kdpair.h"
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#include "networkio.h"
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#include "ratngs.h"
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#include "unicharcompress.h"
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namespace tesseract {
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// Enum describing what can follow the current node.
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// Consider the following softmax outputs:
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// Timestep 0 1 2 3 4 5 6 7 8
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// X-score 0.01 0.55 0.98 0.42 0.01 0.01 0.40 0.95 0.01
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// Y-score 0.00 0.01 0.01 0.01 0.01 0.97 0.59 0.04 0.01
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// Null-score 0.99 0.44 0.01 0.57 0.98 0.02 0.01 0.01 0.98
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// Then the correct CTC decoding (in which adjacent equal classes are folded,
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// and then all nulls are dropped) is clearly XYX, but simple decoding (taking
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// the max at each timestep) leads to:
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// Null@0.99 X@0.55 X@0.98 Null@0.57 Null@0.98 Y@0.97 Y@0.59 X@0.95 Null@0.98,
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// which folds to the correct XYX. The conversion to Tesseract rating and
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// certainty uses the sum of the log probs (log of the product of probabilities)
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// for the Rating and the minimum log prob for the certainty, but that yields a
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// minimum certainty of log(0.55), which is poor for such an obvious case.
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// CTC says that the probability of the result is the SUM of the products of the
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// probabilities over ALL PATHS that decode to the same result, which includes:
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// NXXNNYYXN, NNXNNYYN, NXXXNYYXN, NNXXNYXXN, and others including XXXXXYYXX.
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// That is intractable, so some compromise between simple and ideal is needed.
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// Observing that evenly split timesteps rarely happen next to each other, we
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// allow scores at a transition between classes to be added for decoding thus:
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// N@0.99 (N+X)@0.99 X@0.98 (N+X)@0.99 N@0.98 Y@0.97 (X+Y+N)@1.00 X@0.95 N@0.98.
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// This works because NNX and NXX both decode to X, so in the middle we can use
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// N+X. Note that the classes either side of a sum must stand alone, i.e. use a
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// single score, to force all paths to pass through them and decode to the same
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// result. Also in the special case of a transition from X to Y, with only one
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// timestep between, it is possible to add X+Y+N, since XXY, XYY, and XNY all
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// decode to XY.
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// An important condition is that we cannot combine X and Null between two
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// stand-alone Xs, since that can decode as XNX->XX or XXX->X, so the scores for
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// X and Null have to go in separate paths. Combining scores in this way
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// provides a much better minimum certainty of log(0.95).
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// In the implementation of the beam search, we have to place the possibilities
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// X, X+N and X+Y+N in the beam under appropriate conditions of the previous
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// node, and constrain what can follow, to enforce the rules explained above.
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// We therefore have 3 different types of node determined by what can follow:
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enum NodeContinuation {
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NC_ANYTHING, // This node used just its own score, so anything can follow.
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NC_ONLY_DUP, // The current node combined another score with the score for
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// itself, without a stand-alone duplicate before, so must be
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// followed by a stand-alone duplicate.
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NC_NO_DUP, // The current node combined another score with the score for
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// itself, after a stand-alone, so can only be followed by
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// something other than a duplicate of the current node.
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NC_COUNT
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};
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// Enum describing the top-n status of a code.
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enum TopNState {
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TN_TOP2, // Winner or 2nd.
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TN_TOPN, // Runner up in top-n, but not 1st or 2nd.
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TN_ALSO_RAN, // Not in the top-n.
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TN_COUNT
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};
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// Lattice element for Re-encode beam search.
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struct RecodeNode {
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RecodeNode()
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: code(-1),
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unichar_id(INVALID_UNICHAR_ID),
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permuter(TOP_CHOICE_PERM),
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start_of_dawg(false),
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start_of_word(false),
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end_of_word(false),
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duplicate(false),
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certainty(0.0f),
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score(0.0f),
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prev(nullptr),
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dawgs(nullptr),
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code_hash(0) {}
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RecodeNode(int c, int uni_id, PermuterType perm, bool dawg_start,
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bool word_start, bool end, bool dup, float cert, float s,
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const RecodeNode* p, DawgPositionVector* d, uint64_t hash)
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: code(c),
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unichar_id(uni_id),
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permuter(perm),
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start_of_dawg(dawg_start),
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start_of_word(word_start),
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end_of_word(end),
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duplicate(dup),
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certainty(cert),
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score(s),
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prev(p),
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dawgs(d),
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code_hash(hash) {}
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// NOTE: If we could use C++11, then this would be a move constructor.
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// Instead we have copy constructor that does a move!! This is because we
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// don't want to copy the whole DawgPositionVector each time, and true
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// copying isn't necessary for this struct. It does get moved around a lot
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// though inside the heap and during heap push, hence the move semantics.
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RecodeNode(RecodeNode& src) : dawgs(nullptr) {
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*this = src;
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ASSERT_HOST(src.dawgs == nullptr);
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}
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RecodeNode& operator=(RecodeNode& src) {
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delete dawgs;
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memcpy(this, &src, sizeof(src));
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src.dawgs = nullptr;
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return *this;
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}
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~RecodeNode() { delete dawgs; }
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// Prints details of the node.
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void Print(int null_char, const UNICHARSET& unicharset, int depth) const;
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// The re-encoded code here = index to network output.
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int code;
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// The decoded unichar_id is only valid for the final code of a sequence.
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int unichar_id;
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// The type of permuter active at this point. Intervals between start_of_word
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// and end_of_word make valid words of type given by permuter where
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// end_of_word is true. These aren't necessarily delimited by spaces.
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PermuterType permuter;
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// True if this is the initial dawg state. May be attached to a space or,
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// in a non-space-delimited lang, the end of the previous word.
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bool start_of_dawg;
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// True if this is the first node in a dictionary word.
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bool start_of_word;
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// True if this represents a valid candidate end of word position. Does not
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// necessarily mark the end of a word, since a word can be extended beyond a
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// candidate end by a continuation, eg 'the' continues to 'these'.
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bool end_of_word;
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// True if this->code is a duplicate of prev->code. Some training modes
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// allow the network to output duplicate characters and crush them with CTC,
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// but that would mess up the dictionary search, so we just smash them
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// together on the fly using the duplicate flag.
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bool duplicate;
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// Certainty (log prob) of (just) this position.
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float certainty;
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// Total certainty of the path to this position.
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float score;
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// The previous node in this chain. Borrowed pointer.
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const RecodeNode* prev;
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// The currently active dawgs at this position. Owned pointer.
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DawgPositionVector* dawgs;
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// A hash of all codes in the prefix and this->code as well. Used for
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// duplicate path removal.
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uint64_t code_hash;
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};
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typedef KDPairInc<double, RecodeNode> RecodePair;
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typedef GenericHeap<RecodePair> RecodeHeap;
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// Class that holds the entire beam search for recognition of a text line.
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class RecodeBeamSearch {
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public:
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// Borrows the pointer, which is expected to survive until *this is deleted.
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RecodeBeamSearch(const UnicharCompress& recoder, int null_char,
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bool simple_text, Dict* dict);
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// Decodes the set of network outputs, storing the lattice internally.
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// If charset is not null, it enables detailed debugging of the beam search.
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void Decode(const NetworkIO& output, double dict_ratio, double cert_offset,
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double worst_dict_cert, const UNICHARSET* charset);
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void Decode(const GENERIC_2D_ARRAY<float>& output, double dict_ratio,
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double cert_offset, double worst_dict_cert,
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const UNICHARSET* charset);
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// Returns the best path as labels/scores/xcoords similar to simple CTC.
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void ExtractBestPathAsLabels(GenericVector<int>* labels,
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GenericVector<int>* xcoords) const;
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// Returns the best path as unichar-ids/certs/ratings/xcoords skipping
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// duplicates, nulls and intermediate parts.
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void ExtractBestPathAsUnicharIds(bool debug, const UNICHARSET* unicharset,
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GenericVector<int>* unichar_ids,
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GenericVector<float>* certs,
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GenericVector<float>* ratings,
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GenericVector<int>* xcoords) const;
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// Returns the best path as a set of WERD_RES.
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void ExtractBestPathAsWords(const TBOX& line_box, float scale_factor,
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bool debug, const UNICHARSET* unicharset,
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PointerVector<WERD_RES>* words);
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// Generates debug output of the content of the beams after a Decode.
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void DebugBeams(const UNICHARSET& unicharset) const;
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// Clipping value for certainty inside Tesseract. Reflects the minimum value
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// of certainty that will be returned by ExtractBestPathAsUnicharIds.
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// Supposedly on a uniform scale that can be compared across languages and
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// engines.
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static const float kMinCertainty;
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// Number of different code lengths for which we have a separate beam.
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static const int kNumLengths = RecodedCharID::kMaxCodeLen + 1;
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// Total number of beams: dawg/nodawg * number of NodeContinuation * number
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// of different lengths.
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static const int kNumBeams = 2 * NC_COUNT * kNumLengths;
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// Returns the relevant factor in the beams_ index.
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static int LengthFromBeamsIndex(int index) { return index % kNumLengths; }
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static NodeContinuation ContinuationFromBeamsIndex(int index) {
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return static_cast<NodeContinuation>((index / kNumLengths) % NC_COUNT);
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}
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static bool IsDawgFromBeamsIndex(int index) {
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return index / (kNumLengths * NC_COUNT) > 0;
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}
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// Computes a beams_ index from the given factors.
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static int BeamIndex(bool is_dawg, NodeContinuation cont, int length) {
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return (is_dawg * NC_COUNT + cont) * kNumLengths + length;
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}
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private:
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// Struct for the Re-encode beam search. This struct holds the data for
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// a single time-step position of the output. Use a PointerVector<RecodeBeam>
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// to hold all the timesteps and prevent reallocation of the individual heaps.
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struct RecodeBeam {
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// Resets to the initial state without deleting all the memory.
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void Clear() {
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for (int i = 0; i < kNumBeams; ++i) {
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beams_[i].clear();
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}
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RecodeNode empty;
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for (int i = 0; i < NC_COUNT; ++i) {
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best_initial_dawgs_[i] = empty;
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}
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}
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// A separate beam for each combination of code length,
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// NodeContinuation, and dictionary flag. Separating out all these types
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// allows the beam to be quite narrow, and yet still have a low chance of
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// losing the best path.
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// We have to keep all these beams separate, since the highest scoring paths
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// come from the paths that are most likely to dead-end at any time, like
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// dawg paths, NC_ONLY_DUP etc.
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// Each heap is stored with the WORST result at the top, so we can quickly
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// get the top-n values.
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RecodeHeap beams_[kNumBeams];
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// While the language model is only a single word dictionary, we can use
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// word starts as a choke point in the beam, and keep only a single dict
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// start node at each step (for each NodeContinuation type), so we find the
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// best one here and push it on the heap, if it qualifies, after processing
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// all of the step.
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RecodeNode best_initial_dawgs_[NC_COUNT];
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};
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typedef KDPairInc<float, int> TopPair;
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// Generates debug output of the content of a single beam position.
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void DebugBeamPos(const UNICHARSET& unicharset, const RecodeHeap& heap) const;
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// Returns the given best_nodes as unichar-ids/certs/ratings/xcoords skipping
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// duplicates, nulls and intermediate parts.
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static void ExtractPathAsUnicharIds(
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const GenericVector<const RecodeNode*>& best_nodes,
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GenericVector<int>* unichar_ids, GenericVector<float>* certs,
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GenericVector<float>* ratings, GenericVector<int>* xcoords);
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// Sets up a word with the ratings matrix and fake blobs with boxes in the
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// right places.
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WERD_RES* InitializeWord(bool leading_space, const TBOX& line_box,
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int word_start, int word_end, float space_certainty,
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const UNICHARSET* unicharset,
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const GenericVector<int>& xcoords,
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float scale_factor);
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// Fills top_n_flags_ with bools that are true iff the corresponding output
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// is one of the top_n.
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void ComputeTopN(const float* outputs, int num_outputs, int top_n);
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// Adds the computation for the current time-step to the beam. Call at each
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// time-step in sequence from left to right. outputs is the activation vector
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// for the current timestep.
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void DecodeStep(const float* outputs, int t, double dict_ratio,
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double cert_offset, double worst_dict_cert,
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const UNICHARSET* charset);
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// Adds to the appropriate beams the legal (according to recoder)
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// continuations of context prev, which is from the given index to beams_,
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// using the given network outputs to provide scores to the choices. Uses only
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// those choices for which top_n_flags[code] == top_n_flag.
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void ContinueContext(const RecodeNode* prev, int index, const float* outputs,
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TopNState top_n_flag, double dict_ratio,
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double cert_offset, double worst_dict_cert,
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RecodeBeam* step);
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// Continues for a new unichar, using dawg or non-dawg as per flag.
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void ContinueUnichar(int code, int unichar_id, float cert,
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float worst_dict_cert, float dict_ratio, bool use_dawgs,
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NodeContinuation cont, const RecodeNode* prev,
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RecodeBeam* step);
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// Adds a RecodeNode composed of the args to the correct heap in step if
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// unichar_id is a valid dictionary continuation of whatever is in prev.
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void ContinueDawg(int code, int unichar_id, float cert, NodeContinuation cont,
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const RecodeNode* prev, RecodeBeam* step);
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// Sets the correct best_initial_dawgs_ with a RecodeNode composed of the args
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// if better than what is already there.
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void PushInitialDawgIfBetter(int code, int unichar_id, PermuterType permuter,
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bool start, bool end, float cert,
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NodeContinuation cont, const RecodeNode* prev,
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RecodeBeam* step);
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// Adds a RecodeNode composed of the args to the correct heap in step for
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// partial unichar or duplicate if there is room or if better than the
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// current worst element if already full.
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void PushDupOrNoDawgIfBetter(int length, bool dup, int code, int unichar_id,
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float cert, float worst_dict_cert,
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float dict_ratio, bool use_dawgs,
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NodeContinuation cont, const RecodeNode* prev,
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RecodeBeam* step);
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// Adds a RecodeNode composed of the args to the correct heap in step if there
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// is room or if better than the current worst element if already full.
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void PushHeapIfBetter(int max_size, int code, int unichar_id,
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PermuterType permuter, bool dawg_start, bool word_start,
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bool end, bool dup, float cert, const RecodeNode* prev,
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DawgPositionVector* d, RecodeHeap* heap);
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// Adds a RecodeNode to heap if there is room
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// or if better than the current worst element if already full.
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void PushHeapIfBetter(int max_size, RecodeNode* node, RecodeHeap* heap);
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// Searches the heap for an entry matching new_node, and updates the entry
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// with reshuffle if needed. Returns true if there was a match.
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bool UpdateHeapIfMatched(RecodeNode* new_node, RecodeHeap* heap);
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// Computes and returns the code-hash for the given code and prev.
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uint64_t ComputeCodeHash(int code, bool dup, const RecodeNode* prev) const;
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// Backtracks to extract the best path through the lattice that was built
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// during Decode. On return the best_nodes vector essentially contains the set
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// of code, score pairs that make the optimal path with the constraint that
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// the recoder can decode the code sequence back to a sequence of unichar-ids.
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void ExtractBestPaths(GenericVector<const RecodeNode*>* best_nodes,
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GenericVector<const RecodeNode*>* second_nodes) const;
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// Helper backtracks through the lattice from the given node, storing the
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// path and reversing it.
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void ExtractPath(const RecodeNode* node,
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GenericVector<const RecodeNode*>* path) const;
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// Helper prints debug information on the given lattice path.
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void DebugPath(const UNICHARSET* unicharset,
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const GenericVector<const RecodeNode*>& path) const;
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// Helper prints debug information on the given unichar path.
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void DebugUnicharPath(const UNICHARSET* unicharset,
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const GenericVector<const RecodeNode*>& path,
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const GenericVector<int>& unichar_ids,
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const GenericVector<float>& certs,
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const GenericVector<float>& ratings,
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const GenericVector<int>& xcoords) const;
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static const int kBeamWidths[RecodedCharID::kMaxCodeLen + 1];
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// The encoder/decoder that we will be using.
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const UnicharCompress& recoder_;
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// The beam for each timestep in the output.
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PointerVector<RecodeBeam> beam_;
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// The number of timesteps valid in beam_;
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int beam_size_;
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// A flag to indicate which outputs are the top-n choices. Current timestep
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// only.
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GenericVector<TopNState> top_n_flags_;
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// A record of the highest and second scoring codes.
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int top_code_;
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int second_code_;
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// Heap used to compute the top_n_flags_.
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GenericHeap<TopPair> top_heap_;
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// Borrowed pointer to the dictionary to use in the search.
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Dict* dict_;
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// True if the language is space-delimited, which is true for most languages
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// except chi*, jpn, tha.
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bool space_delimited_;
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// True if the input is simple text, ie adjacent equal chars are not to be
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// eliminated.
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bool is_simple_text_;
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// The encoded (class label) of the null/reject character.
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int null_char_;
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};
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} // namespace tesseract.
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#endif // THIRD_PARTY_TESSERACT_LSTM_RECODEBEAM_H_
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