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