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git-svn-id: https://tesseract-ocr.googlecode.com/svn/trunk@526 d0cd1f9f-072b-0410-8dd7-cf729c803f20
95 lines
4.1 KiB
C++
95 lines
4.1 KiB
C++
/**********************************************************************
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* File: conv_net_classifier.h
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* Description: Declaration of Convolutional-NeuralNet Character Classifier
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* Author: Ahmad Abdulkader
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* Created: 2007
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*
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* (C) Copyright 2008, 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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// The ConvNetCharClassifier inherits from the base classifier class:
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// "CharClassifierBase". It implements a Convolutional Neural Net classifier
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// instance of the base classifier. It uses the Tesseract Neural Net library
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// The Neural Net takes a scaled version of a bitmap and feeds it to a
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// Convolutional Neural Net as input and performs a FeedForward. Each output
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// of the net corresponds to class_id in the CharSet passed at construction
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// time.
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// Afterwards, the outputs of the Net are "folded" using the folding set
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// (if any)
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#ifndef CONV_NET_CLASSIFIER_H
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#define CONV_NET_CLASSIFIER_H
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#include <string>
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#include "char_samp.h"
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#include "char_altlist.h"
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#include "char_set.h"
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#include "feature_base.h"
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#include "classifier_base.h"
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#include "neural_net.h"
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#include "lang_model.h"
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#include "tuning_params.h"
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namespace tesseract {
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// Folding Ratio is the ratio of the max-activation of members of a folding
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// set that is used to compute the min-activation of the rest of the set
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static const float kFoldingRatio = 0.75;
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class ConvNetCharClassifier : public CharClassifier {
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public:
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ConvNetCharClassifier(CharSet *char_set, TuningParams *params,
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FeatureBase *feat_extract);
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virtual ~ConvNetCharClassifier();
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// The main training function. Given a sample and a class ID the classifier
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// updates its parameters according to its learning algorithm. This function
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// is currently not implemented. TODO(ahmadab): implement end-2-end training
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virtual bool Train(CharSamp *char_samp, int ClassID);
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// A secondary function needed for training. Allows the trainer to set the
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// value of any train-time paramter. This function is currently not
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// implemented. TODO(ahmadab): implement end-2-end training
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virtual bool SetLearnParam(char *var_name, float val);
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// Externally sets the Neural Net used by the classifier. Used for training
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void SetNet(tesseract::NeuralNet *net);
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// Classifies an input charsamp and return a CharAltList object containing
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// the possible candidates and corresponding scores
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virtual CharAltList * Classify(CharSamp *char_samp);
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// Computes the cost of a specific charsamp being a character (versus a
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// non-character: part-of-a-character OR more-than-one-character)
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virtual int CharCost(CharSamp *char_samp);
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private:
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// Neural Net object used for classification
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tesseract::NeuralNet *char_net_;
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// data buffers used to hold Neural Net inputs and outputs
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float *net_input_;
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float *net_output_;
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// Init the classifier provided a data-path and a language string
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virtual bool Init(const string &data_file_path, const string &lang,
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LangModel *lang_mod);
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// Loads the NeuralNets needed for the classifier
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bool LoadNets(const string &data_file_path, const string &lang);
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// Loads the folding sets provided a data-path and a language string
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virtual bool LoadFoldingSets(const string &data_file_path,
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const string &lang,
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LangModel *lang_mod);
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// Folds the output of the NeuralNet using the loaded folding sets
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virtual void Fold();
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// Scales the input char_samp and feeds it to the NeuralNet as input
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bool RunNets(CharSamp *char_samp);
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};
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}
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#endif // CONV_NET_CLASSIFIER_H
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