Class-specific Extremal Region Filter algorithm as proposed in :

Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012.

High-level C++ interface and implementation of algorithm is in the objdetect module.
C++ example, a test image, and the default classifiers in xml files.

modules/objdetect/CMakeLists.txt

@@ -1,2 +1,2 @@
 set(the_description "Object Detection")
-ocv_define_module(objdetect opencv_core opencv_imgproc OPTIONAL opencv_highgui)
+ocv_define_module(objdetect opencv_core opencv_imgproc opencv_ml OPTIONAL opencv_highgui)

modules/objdetect/include/opencv2/objdetect.hpp

@@ -394,5 +394,6 @@ CV_EXPORTS_W void drawDataMatrixCodes(InputOutputArray image,
 }
 
 #include "opencv2/objdetect/linemod.hpp"
+#include "opencv2/objdetect/erfilter.hpp"
 
 #endif

modules/objdetect/include/opencv2/objdetect/erfilter.hpp

@@ -0,0 +1,202 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
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+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
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+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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+//     derived from this software without specific prior written permission.
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+// This software is provided by the copyright holders and contributors "as is" and
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+// In no event shall the Intel Corporation or contributors be liable for any direct,
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+// and on any theory of liability, whether in contract, strict liability,
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+//M*/
+
+#ifndef __OPENCV_OBJDETECT_ERFILTER_HPP__
+#define __OPENCV_OBJDETECT_ERFILTER_HPP__
+
+#include "opencv2/core.hpp"
+#include <vector>
+#include <deque>
+
+namespace cv
+{
+
+/*!
+    Extremal Region Stat structure 
+
+    The ERStat structure represents a class-specific Extremal Region (ER).
+
+    An ER is a 4-connected set of pixels with all its grey-level values smaller than the values 
+    in its outer boundary. A class-specific ER is selected (using a classifier) from all the ER's 
+    in the component tree of the image.
+*/
+struct CV_EXPORTS ERStat
+{
+public:
+    //! Constructor
+    ERStat(int level = 256, int pixel = 0, int x = 0, int y = 0);
+    //! Destructor
+    ~ERStat(){};
+
+    //! seed point and the threshold (max grey-level value)
+    int pixel; 
+    int level; 
+
+    //! incrementally computable features
+    int area; 
+    int perimeter;
+    int euler;                 //!< euler number
+    int bbox[4];
+    double raw_moments[2];     //!< order 1 raw moments to derive the centroid
+    double central_moments[3]; //!< order 2 central moments to construct the covariance matrix
+    std::deque<int> *crossings;//!< horizontal crossings
+
+    //! 1st stage features
+    float aspect_ratio;
+    float compactness;
+    float num_holes;
+    float med_crossings;
+
+    //! 2nd stage features
+    float hole_area_ratio;
+    float convex_hull_ratio;
+    float num_inflexion_points;
+
+    // TODO Other features can be added (average color, standard deviation, and such)
+    
+
+    // TODO shall we include the pixel list whenever available (i.e. after 2nd stage) ?
+    std::vector<int> *pixels; 
+ 
+    //! probability that the ER belongs to the class we are looking for
+    double probability;
+
+    //! pointers preserving the tree structure of the component tree
+    ERStat* parent; 
+    ERStat* child; 
+    ERStat* next;
+    ERStat* prev;
+
+    //! wenever the regions is a local maxima of the probability 
+    bool local_maxima;
+    ERStat* max_probability_ancestor;
+    ERStat* min_probability_ancestor;
+};
+
+/*!
+    Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithms
+    Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
+
+    Extracts the component tree (if needed) and filter the extremal regions (ER's) by using a given classifier.
+*/
+class CV_EXPORTS ERFilter : public cv::Algorithm
+{
+public:
+
+    //! callback with the classifier is made a class. By doing it we hide SVM, Boost etc.
+    class CV_EXPORTS Callback
+    {
+    public:
+        virtual ~Callback(){};
+        //! The classifier must return probability measure for the region.
+        virtual double eval(const ERStat& stat) = 0; //const = 0; //TODO why cannot use const = 0 here? 
+    };
+
+    /*! 
+        the key method. Takes image on input and returns the selected regions in a vector of ERStat 
+        only distinctive ERs which correspond to characters are selected by a sequential classifier
+        \param image   is the input image
+        \param regions is output for the first stage, input/output for the second one.
+    */
+    virtual void run( cv::InputArray image, std::vector<ERStat>& regions ) = 0;
+
+
+    //! set/get methods to set the algorithm properties,
+    virtual void setCallback(const cv::Ptr<ERFilter::Callback>& cb) = 0;
+    virtual void setThresholdDelta(int thresholdDelta) = 0;
+    virtual void setMinArea(float minArea) = 0;
+    virtual void setMaxArea(float maxArea) = 0;
+    virtual void setMinProbability(float minProbability) = 0;
+    virtual void setMinProbabilityDiff(float minProbabilityDiff) = 0;
+    virtual void setNonMaxSuppression(bool nonMaxSuppression) = 0;
+    virtual int  getNumRejected() = 0;
+};
+
+
+/*!
+    Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm
+    Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
+ 
+    The component tree of the image is extracted by a threshold increased step by step
+    from 0 to 255, incrementally computable descriptors (aspect_ratio, compactness, 
+    number of holes, and number of horizontal crossings) are computed for each ER 
+    and used as features for a classifier which estimates the class-conditional 
+    probability P(er|character). The value of P(er|character) is tracked using the inclusion 
+    relation of ER across all thresholds and only the ERs which correspond to local maximum 
+    of the probability P(er|character) are selected (if the local maximum of the
+    probability is above a global limit pmin and the difference between local maximum and 
+    local minimum is greater than minProbabilityDiff).
+
+    \param  cb                Callback with the classifier.
+                              if omitted tries to load a default classifier from file trained_classifierNM1.xml
+    \param  thresholdDelta    Threshold step in subsequent thresholds when extracting the component tree
+    \param  minArea           The minimum area (% of image size) allowed for retreived ER's
+    \param  minArea           The maximum area (% of image size) allowed for retreived ER's
+    \param  minProbability    The minimum probability P(er|character) allowed for retreived ER's
+    \param  nonMaxSuppression Whenever non-maximum suppression is done over the branch probabilities  
+    \param  minProbability    The minimum probability difference between local maxima and local minima ERs
+*/
+CV_EXPORTS cv::Ptr<ERFilter> createERFilterNM1(const cv::Ptr<ERFilter::Callback>& cb = NULL, 
+                                                  int thresholdDelta = 1, float minArea = 0.000025, 
+                                                  float maxArea = 0.13, float minProbability = 0.2, 
+                                                  bool nonMaxSuppression = true, 
+                                                  float minProbabilityDiff = 0.1);
+
+/*!
+    Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm
+    Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
+
+    In the second stage, the ERs that passed the first stage are classified into character 
+    and non-character classes using more informative but also more computationally expensive
+    features. The classifier uses all the features calculated in the first stage and the following 
+    additional features: hole area ratio, convex hull ratio, and number of outer inflexion points.
+
+    \param  cb             Callback with the classifier
+                           if omitted tries to load a default classifier from file trained_classifierNM2.xml
+    \param  minProbability The minimum probability P(er|character) allowed for retreived ER's
+*/
+CV_EXPORTS cv::Ptr<ERFilter> createERFilterNM2(const cv::Ptr<ERFilter::Callback>& cb = NULL, 
+                                                  float minProbability = 0.85);
+
+}
+#endif // _OPENCV_ERFILTER_HPP_

samples/cpp/erfilter.cpp

@@ -0,0 +1,120 @@
+
+//--------------------------------------------------------------------------------------------------
+//  A demo program of the Extremal Region Filter algorithm described in 
+//  Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
+//--------------------------------------------------------------------------------------------------
+
+#include  "opencv2/opencv.hpp"
+#include  "opencv2/objdetect.hpp"
+#include  "opencv2/highgui.hpp"
+#include  "opencv2/imgproc.hpp"
+
+#include  <vector>
+#include  <iostream>
+#include  <iomanip>
+
+using  namespace std;
+using  namespace cv;
+
+void  er_draw(Mat &src, Mat &dst, ERStat& er);
+
+void  er_draw(Mat &src, Mat &dst, ERStat& er)
+{
+
+    if (er.parent != NULL) // deprecate the root region 
+    {
+        int newMaskVal = 255;
+        int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
+        floodFill(src,dst,Point(er.pixel%src.cols,er.pixel/src.cols),Scalar(255),0,Scalar(er.level),Scalar(0),flags);
+    }
+
+}
+		
+int  main(int argc, const char * argv[])
+{
+
+
+    vector<ERStat> regions;
+
+    if (argc < 2) {
+        cout << "Demo program of the Extremal Region Filter algorithm described in " << endl;
+        cout << "Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012" << endl << endl;
+        cout << "    Usage: " << argv[0] << " input_image <optional_groundtruth_image>" << endl;
+        cout << "    Default classifier files (trained_classifierNM*.xml) should be in ./" << endl;
+        return -1;
+    }
+
+    Mat original = imread(argv[1]);
+    Mat gt;
+    if (argc > 2)
+    {
+        gt = imread(argv[2]);
+        cvtColor(gt, gt, COLOR_RGB2GRAY);
+        threshold(gt, gt, 254, 255, THRESH_BINARY);
+    }
+    Mat grey(original.size(),CV_8UC1);
+    cvtColor(original,grey,COLOR_RGB2GRAY);
+    
+    double t = (double)getTickCount();
+    
+    // Build ER tree and filter with the 1st stage default classifier
+    Ptr<ERFilter> er_filter1 = createERFilterNM1();
+    
+    er_filter1->run(grey, regions);
+    
+    t = (double)getTickCount() - t;
+    cout << " --------------------------------------------------------------------------------------------------" << endl;
+    cout << "\t FIRST STAGE CLASSIFIER done in " << t * 1000. / getTickFrequency() << " ms." << endl;
+    cout << " --------------------------------------------------------------------------------------------------" << endl;
+    cout << setw(9) << regions.size()+er_filter1->getNumRejected() << "\t Extremal Regions extracted " << endl;
+    cout << setw(9) << regions.size() << "\t Extremal Regions selected by the first stage of the sequential classifier." << endl;
+    cout << "\t \t (saving into out_second_stage.jpg)" << endl;
+    cout << " --------------------------------------------------------------------------------------------------" << endl;
+
+    er_filter1.release();
+
+    // draw regions
+    Mat mask = Mat::zeros(grey.rows+2,grey.cols+2,CV_8UC1);
+    for (int r=0; r<(int)regions.size(); r++)
+        er_draw(grey, mask, regions.at(r));
+    mask = 255-mask;
+    imwrite("out_first_stage.jpg", mask);
+
+    if (argc > 2)
+    {
+        Mat tmp_mask = (255-gt) & (255-mask(Rect(Point(1,1),Size(mask.cols-2,mask.rows-2))));
+        cout << "Recall for the 1st stage filter = " << (float)countNonZero(tmp_mask) / countNonZero(255-gt) << endl;
+    }
+
+    t = (double)getTickCount();
+    
+    // Default second stage classifier
+    Ptr<ERFilter> er_filter2 = createERFilterNM2();
+    er_filter2->run(grey, regions);
+    
+    t = (double)getTickCount() - t;
+    cout << " --------------------------------------------------------------------------------------------------" << endl;
+    cout << "\t SECOND STAGE CLASSIFIER done in " << t * 1000. / getTickFrequency() << " ms." << endl;
+    cout << " --------------------------------------------------------------------------------------------------" << endl;
+    cout << setw(9) << regions.size() << "\t Extremal Regions selected by the second stage of the sequential classifier." << endl;
+    cout << "\t \t (saving into out_second_stage.jpg)" << endl;
+    cout << " --------------------------------------------------------------------------------------------------" << endl;
+
+    er_filter2.release();
+
+    // draw regions
+    mask = mask*0;
+    for (int r=0; r<(int)regions.size(); r++)
+        er_draw(grey, mask, regions.at(r));
+    mask = 255-mask;
+    imwrite("out_second_stage.jpg", mask);
+
+    if (argc > 2)
+    {
+        Mat tmp_mask = (255-gt) & (255-mask(Rect(Point(1,1),Size(mask.cols-2,mask.rows-2))));
+        cout << "Recall for the 2nd stage filter = " << (float)countNonZero(tmp_mask) / countNonZero(255-gt) << endl;
+    }
+
+    regions.clear();
+
+}