fix spelling

modules/text/README.md

@@ -12,7 +12,7 @@ Here are instructions on how to install Tesseract on your machine (Linux or Mac;
 Tesseract installation instruction (Linux, Mac)
 -----------------------------------------------
 
-0. Linux users may try to install tesseract-3.03-rc1 (or later) and leptonica-1.70 (or later) with the corresponding developement packages using their package manager. Mac users may try brew. The instructions below are for those who wants to build tesseract from source.
+0. Linux users may try to install tesseract-3.03-rc1 (or later) and leptonica-1.70 (or later) with the corresponding development packages using their package manager. Mac users may try brew. The instructions below are for those who wants to build tesseract from source.
 
 1. download leptonica 1.70 tarball (helper image processing library, used by tesseract. Later versions might work too):
 http://www.leptonica.com/download.html
@@ -33,9 +33,9 @@ mkdir build && cd build
 ../configure --with-extra-includes=/usr/local --with-extra-libraries=/usr/local
 make && sudo make install
 
-tessract will be installed to /usr/local.
+Tesseract will be installed to /usr/local.
 
-3. download the pre-trained classifier data for english language:
+3. download the pre-trained classifier data for English language:
 https://code.google.com/p/tesseract-ocr/downloads/detail?name=eng.traineddata.gz
 
 unzip it (gzip -d eng.traineddata.gz) and copy to /usr/local/share/tessdata.

modules/text/include/opencv2/text.hpp

@@ -66,12 +66,12 @@ hierarchy by their inclusion relation:
 
 ![image](pics/component_tree.png)
 
-The component tree may conatain a huge number of regions even for a very simple image as shown in
+The component tree may contain a huge number of regions even for a very simple image as shown in
 the previous image. This number can easily reach the order of 1 x 10\^6 regions for an average 1
 Megapixel image. In order to efficiently select suitable regions among all the ERs the algorithm
 make use of a sequential classifier with two differentiated stages.
 
-In the first stage incrementally computable descriptors (area, perimeter, bounding box, and euler
+In the first stage incrementally computable descriptors (area, perimeter, bounding box, and Euler's
 number) are computed (in O(1)) for each region r and used as features for a classifier which
 estimates the class-conditional probability p(r|character). Only the ERs which correspond to local
 maximum of the probability p(r|character) are selected (if their probability is above a global limit

modules/text/include/opencv2/text/erfilter.hpp

@@ -78,7 +78,7 @@ public:
     //! incrementally computable features
     int area;
     int perimeter;
-    int euler;                 //!< euler number
+    int euler;                 //!< Euler's number
     Rect rect;
     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
@@ -105,7 +105,7 @@ public:
     ERStat* next;
     ERStat* prev;
 
-    //! wenever the regions is a local maxima of the probability
+    //! whenever the regions is a local maxima of the probability
     bool local_maxima;
     ERStat* max_probability_ancestor;
     ERStat* min_probability_ancestor;
@@ -317,7 +317,7 @@ enum erGrouping_Modes {
 
 @param channels Vector of single channel images CV_8UC1 from wich the regions were extracted.
 
-@param regions Vector of ER's retreived from the ERFilter algorithm from each channel.
+@param regions Vector of ER's retrieved from the ERFilter algorithm from each channel.
 
 @param groups The output of the algorithm is stored in this parameter as set of lists of indexes to
 provided regions.
@@ -353,7 +353,7 @@ CV_EXPORTS_W void erGrouping(InputArray image, InputArray channel,
 
 @param image Source image CV_8UC1 from which the MSERs where extracted.
 
-@param contours Intput vector with all the contours (vector\<Point\>).
+@param contours Input vector with all the contours (vector\<Point\>).
 
 @param regions Output where the ERStat regions are stored.
 

modules/text/include/opencv2/text/ocr.hpp

@@ -153,7 +153,7 @@ public:
     @param language an ISO 639-3 code or NULL will default to "eng".
     @param char_whitelist specifies the list of characters used for recognition. NULL defaults to
     "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ".
-    @param oem tesseract-ocr offers different OCR Engine Modes (OEM), by deffault
+    @param oem tesseract-ocr offers different OCR Engine Modes (OEM), by default
     tesseract::OEM_DEFAULT is used. See the tesseract-ocr API documentation for other possible
     values.
     @param psmode tesseract-ocr offers different Page Segmentation Modes (PSM) tesseract::PSM_AUTO
@@ -195,7 +195,7 @@ public:
     This way it hides the feature extractor and the classifier itself, so developers can write
     their own OCR code.
 
-    The default character classifier and feature extractor can be loaded using the utility funtion
+    The default character classifier and feature extractor can be loaded using the utility function
     loadOCRHMMClassifierNM and KNN model provided in
     <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/OCRHMM_knn_model_data.xml.gz>.
      */
@@ -289,21 +289,21 @@ public:
     (<http://en.wikipedia.org/wiki/Viterbi_algorithm>).
      */
     static Ptr<OCRHMMDecoder> create(const Ptr<OCRHMMDecoder::ClassifierCallback> classifier,// The character classifier with built in feature extractor
-                                     const std::string& vocabulary,                    // The language vocabulary (chars when ascii english text)
+                                     const std::string& vocabulary,                    // The language vocabulary (chars when ASCII English text)
                                                                                        //     size() must be equal to the number of classes
                                      InputArray transition_probabilities_table,        // Table with transition probabilities between character pairs
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      InputArray emission_probabilities_table,          // Table with observation emission probabilities
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      decoder_mode mode = OCR_DECODER_VITERBI);         // HMM Decoding algorithm (only Viterbi for the moment)
 
     CV_WRAP static Ptr<OCRHMMDecoder> create(const Ptr<OCRHMMDecoder::ClassifierCallback> classifier,// The character classifier with built in feature extractor
-                                     const String& vocabulary,                    // The language vocabulary (chars when ascii english text)
+                                     const String& vocabulary,                    // The language vocabulary (chars when ASCII English text)
                                                                                        //     size() must be equal to the number of classes
                                      InputArray transition_probabilities_table,        // Table with transition probabilities between character pairs
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      InputArray emission_probabilities_table,          // Table with observation emission probabilities
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      int mode = OCR_DECODER_VITERBI);         // HMM Decoding algorithm (only Viterbi for the moment)
 
     /** @brief Creates an instance of the OCRHMMDecoder class. Loads and initializes HMMDecoder from the specified path
@@ -312,12 +312,12 @@ public:
      */
     CV_WRAP static Ptr<OCRHMMDecoder> create(const String& filename,
 
-                                     const String& vocabulary,                    // The language vocabulary (chars when ascii english text)
+                                     const String& vocabulary,                    // The language vocabulary (chars when ASCII English text)
                                                                                        //     size() must be equal to the number of classes
                                      InputArray transition_probabilities_table,        // Table with transition probabilities between character pairs
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      InputArray emission_probabilities_table,          // Table with observation emission probabilities
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      int mode = OCR_DECODER_VITERBI,                    // HMM Decoding algorithm (only Viterbi for the moment)
 
                                      int classifier = OCR_KNN_CLASSIFIER);              // The character classifier type
@@ -371,7 +371,7 @@ CV_EXPORTS_W Ptr<OCRHMMDecoder::ClassifierCallback> loadOCRHMMClassifier(const S
 
 /** @brief Utility function to create a tailored language model transitions table from a given list of words (lexicon).
  *
- * @param vocabulary The language vocabulary (chars when ascii english text).
+ * @param vocabulary The language vocabulary (chars when ASCII English text).
  *
  * @param lexicon The list of words that are expected to be found in a particular image.
  *
@@ -466,7 +466,7 @@ public:
 
     @param classifier The character classifier with built in feature extractor.
 
-    @param vocabulary The language vocabulary (chars when ascii english text). vocabulary.size()
+    @param vocabulary The language vocabulary (chars when ASCII English text). vocabulary.size()
     must be equal to the number of classes of the classifier.
 
     @param transition_probabilities_table Table with transition probabilities between character
@@ -481,22 +481,22 @@ public:
     @param beam_size Size of the beam in Beam Search algorithm.
      */
     static Ptr<OCRBeamSearchDecoder> create(const Ptr<OCRBeamSearchDecoder::ClassifierCallback> classifier,// The character classifier with built in feature extractor
-                                     const std::string& vocabulary,                    // The language vocabulary (chars when ascii english text)
+                                     const std::string& vocabulary,                    // The language vocabulary (chars when ASCII English text)
                                                                                        //     size() must be equal to the number of classes
                                      InputArray transition_probabilities_table,        // Table with transition probabilities between character pairs
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      InputArray emission_probabilities_table,          // Table with observation emission probabilities
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      decoder_mode mode = OCR_DECODER_VITERBI,          // HMM Decoding algorithm (only Viterbi for the moment)
                                      int beam_size = 500);                              // Size of the beam in Beam Search algorithm
 
     CV_WRAP static Ptr<OCRBeamSearchDecoder> create(const Ptr<OCRBeamSearchDecoder::ClassifierCallback> classifier, // The character classifier with built in feature extractor
-                                     const String& vocabulary,                    // The language vocabulary (chars when ascii english text)
+                                     const String& vocabulary,                    // The language vocabulary (chars when ASCII English text)
                                                                                        //     size() must be equal to the number of classes
                                      InputArray transition_probabilities_table,        // Table with transition probabilities between character pairs
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      InputArray emission_probabilities_table,          // Table with observation emission probabilities
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      int mode = OCR_DECODER_VITERBI,          // HMM Decoding algorithm (only Viterbi for the moment)
                                      int beam_size = 500);                              // Size of the beam in Beam Search algorithm
 
@@ -506,12 +506,12 @@ public:
 
      */
     CV_WRAP static Ptr<OCRBeamSearchDecoder> create(const String& filename, // The character classifier file
-                                     const String& vocabulary,                    // The language vocabulary (chars when ascii english text)
+                                     const String& vocabulary,                    // The language vocabulary (chars when ASCII English text)
                                                                                        //     size() must be equal to the number of classes
                                      InputArray transition_probabilities_table,        // Table with transition probabilities between character pairs
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      InputArray emission_probabilities_table,          // Table with observation emission probabilities
-                                                                                       //     cols == rows == vocabulari.size()
+                                                                                       //     cols == rows == vocabulary.size()
                                      int mode = OCR_DECODER_VITERBI,          // HMM Decoding algorithm (only Viterbi for the moment)
                                      int beam_size = 500);
 protected:

modules/text/samples/character_recognition.cpp

@@ -37,7 +37,7 @@ int main(int argc, char* argv[])
         return(0);
     }
 
-    string vocabulary = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // must have the same order as the clasifier output classes
+    string vocabulary = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // must have the same order as the classifier output classes
 
     Ptr<OCRHMMDecoder::ClassifierCallback> ocr = loadOCRHMMClassifierCNN("OCRBeamSearch_CNN_model_data.xml.gz");
 

modules/text/samples/cropped_word_recognition.cpp

@@ -36,7 +36,7 @@ int main(int argc, char* argv[])
         return(0);
     }
 
-    string vocabulary = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // must have the same order as the clasifier output classes
+    string vocabulary = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; // must have the same order as the classifier output classes
     vector<string> lexicon;  // a list of words expected to be found on the input image
     lexicon.push_back(string("abb"));
     lexicon.push_back(string("riser"));
@@ -53,7 +53,7 @@ int main(int argc, char* argv[])
     createOCRHMMTransitionsTable(vocabulary,lexicon,transition_p);
 
     // An alternative would be to load the default generic language model
-    //    (created from ispell 42869 english words list)
+    //    (created from ispell 42869 English words list)
     /*Mat transition_p;
     string filename = "OCRHMM_transitions_table.xml";
     FileStorage fs(filename, FileStorage::READ);

modules/text/samples/end_to_end_recognition.cpp

@@ -20,7 +20,7 @@ using namespace std;
 using namespace cv;
 using namespace cv::text;
 
-//Calculate edit distance netween two words
+//Calculate edit distance between two words
 size_t edit_distance(const string& A, const string& B);
 size_t min(size_t x, size_t y, size_t z);
 bool   isRepetitive(const string& s);
@@ -164,7 +164,7 @@ int main(int argc, char* argv[])
     cout << "TIME_OCR = " << ((double)getTickCount() - t_r)*1000/getTickFrequency() << endl;
 
 
-    /* Recognition evaluation with (approximate) hungarian matching and edit distances */
+    /* Recognition evaluation with (approximate) Hungarian matching and edit distances */
 
     if(argc>2)
     {

modules/text/samples/segmented_word_recognition.cpp

@@ -54,7 +54,7 @@ int main(int argc, char* argv[]) {
     else
       image.copyTo(mask);
 
-    // be sure the mask is a binry image
+    // be sure the mask is a binary image
     cvtColor(mask, mask, COLOR_BGR2GRAY);
     threshold(mask, mask, 128., 255, THRESH_BINARY);
 

modules/text/samples/webcam_demo.cpp

@@ -290,7 +290,7 @@ int main(int argc, char* argv[])
         {
 
             outputs[i].erase(remove(outputs[i].begin(), outputs[i].end(), '\n'), outputs[i].end());
-            //cout << "OCR output = \"" << outputs[i] << "\" lenght = " << outputs[i].size() << endl;
+            //cout << "OCR output = \"" << outputs[i] << "\" length = " << outputs[i].size() << endl;
             if (outputs[i].size() < 3)
                 continue;
 

modules/text/src/erfilter.cpp

@@ -107,7 +107,7 @@ ERStat::ERStat(int init_level, int init_pixel, int init_x, int init_y) : pixel(i
 // derivative classes
 
 
-// the classe implementing the interface for the 1st and 2nd stages of Neumann and Matas algorithm
+// the classes implementing the interface for the 1st and 2nd stages of Neumann and Matas algorithm
 class CV_EXPORTS ERFilterNM : public ERFilter
 {
 public:
@@ -277,7 +277,7 @@ void ERFilterNM::er_tree_extract( InputArray image )
     // the component stack
     vector<ERStat*> er_stack;
 
-    // the quads for euler number calculation
+    // the quads for Euler's number calculation
     // quads[2][2] and quads[2][3] are never used.
     // The four lowest bits in each quads[i][j] correspond to the 2x2 binary patterns
     // Q_1, Q_2, Q_3 in the Neumann and Matas CVPR 2012 paper
@@ -336,7 +336,7 @@ void ERFilterNM::er_tree_extract( InputArray image )
                     default: if (y > 0) neighbour_pixel = current_pixel - width; break;
             }
 
-            // if neighbour is not accessible, mark it accessible and retreive its grey-level value
+            // if neighbour is not accessible, mark it accessible and retrieve its grey-level value
             if ( !accessible_pixel_mask[neighbour_pixel] && (neighbour_pixel != current_pixel) )
             {
 
@@ -377,14 +377,14 @@ void ERFilterNM::er_tree_extract( InputArray image )
                 }
             }
 
-        } // else neigbor was already accessible
+        } // else neighbour was already accessible
 
         if (push_new_component) continue;
 
 
         // once here we can add the current pixel to the component at the top of the stack
         // but first we find how many of its neighbours are part of the region boundary (needed for
-        // perimeter and crossings calc.) and the increment in quads counts for euler number calc.
+        // perimeter and crossings calc.) and the increment in quads counts for Euler's number calc.
         int non_boundary_neighbours = 0;
         int non_boundary_neighbours_horiz = 0;
 
@@ -801,7 +801,7 @@ ERStat* ERFilterNM::er_tree_filter ( InputArray image, ERStat * stat, ERStat *pa
     vector<Point> contour_poly;
     vector<Vec4i> hierarchy;
     findContours( region, contours, hierarchy, RETR_TREE, CHAIN_APPROX_NONE, Point(0, 0) );
-    //TODO check epsilon parameter of approxPolyDP (set empirically) : we want more precission
+    //TODO check epsilon parameter of approxPolyDP (set empirically) : we want more precision
     //     if the region is very small because otherwise we'll loose all the convexities
     approxPolyDP( Mat(contours[0]), contour_poly, (float)min(rect.width,rect.height)/17, true );
 
@@ -1089,9 +1089,9 @@ double ERClassifierNM2::eval(const ERStat& stat)
                               default classifier can be implicitly load with function loadClassifierNM1()
                               from file in samples/cpp/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  minArea           The minimum area (% of image size) allowed for retrieved ER's
+    \param  minArea           The maximum area (% of image size) allowed for retrieved ER's
+    \param  minProbability    The minimum probability P(er|character) allowed for retrieved 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
 */
@@ -1222,12 +1222,12 @@ void get_gradient_magnitude(Mat& _grey_img, Mat& _gradient_magnitude)
 
 
 /*!
-    Compute the diferent channels to be processed independently in the N&M algorithm
+    Compute the different channels to be processed independently in the N&M algorithm
     Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
 
     In N&M algorithm, the combination of intensity (I), hue (H), saturation (S), and gradient
-    magnitude channels (Grad) are used in order to obatin high localization recall.
-    This implementation also the alternative combination of red (R), grren (G), blue (B),
+    magnitude channels (Grad) are used in order to obtain high localization recall.
+    This implementation also the alternative combination of red (R), green (G), blue (B),
     lightness (L), and gradient magnitude (Grad).
 
     \param  _src           Source image. Must be RGB CV_8UC3.
@@ -1965,7 +1965,7 @@ public:
 static void generate_dendrogram(double * const Z, cluster_result & Z2, const int_fast32_t N)
 {
     // The array "nodes" is a union-find data structure for the cluster
-    // identites (only needed for unsorted cluster_result input).
+    // identities (only needed for unsorted cluster_result input).
     union_find nodes;
     stable_sort(Z2[0], Z2[N-1]);
     nodes.init(N);
@@ -2196,11 +2196,11 @@ struct HCluster{
     vector<int> elements;   // elements (contour ID)
     int nfa;                // the number of false alarms for this merge
     float dist;             // distance of the merge
-    float dist_ext;         // distamce where this merge will merge with another
+    float dist_ext;         // distance where this merge will merge with another
     long double volume;     // volume of the bounding sphere (or bounding box)
     long double volume_ext; // volume of the sphere(or box) + envolvent empty space
     vector<vector<float> > points; // nD points in this cluster
-    bool max_meaningful;    // is this merge max meaningul ?
+    bool max_meaningful;    // is this merge max meaningful ?
     vector<int> max_in_branch; // otherwise which merges are the max_meaningful in this branch
     int min_nfa_in_branch;  // min nfa detected within the chilhood
     int node1;
@@ -2285,7 +2285,7 @@ void MaxMeaningfulClustering::build_merge_info(double *Z, double *X, int N, int
                                                vector< vector<int> > *meaningful_clusters)
 {
 
-    // walk the whole dendogram
+    // walk the whole dendrogram
     for (int i=0; i<(N-1)*4; i=i+4)
     {
         HCluster cluster;
@@ -2964,7 +2964,7 @@ static float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions,
             f.convex_hull_ratio = (float)contourArea(hull)/contourArea(contours0[0]);
             vector<Vec4i> cx;
             vector<int> hull_idx;
-            //TODO check epsilon parameter of approxPolyDP (set empirically) : we want more precission
+            //TODO check epsilon parameter of approxPolyDP (set empirically) : we want more precision
             //     if the region is very small because otherwise we'll loose all the convexities
             approxPolyDP( Mat(contours0[0]), contours0[0], (float)min(rrect.size.width,rrect.size.height)/17, true );
             convexHull(contours0[0],hull_idx,false,false);
@@ -3007,7 +3007,7 @@ static float extract_features(Mat &grey, Mat& channel, vector<ERStat> &regions,
 
     \param  _image         Original RGB image from wich the regions were extracted.
     \param  _src           Vector of sinle channel images CV_8UC1 from wich the regions were extracted.
-    \param  regions        Vector of ER's retreived from the ERFilter algorithm from each channel
+    \param  regions        Vector of ER's retrieved from the ERFilter algorithm from each channel
     \param  groups         The output of the algorithm are stored in this parameter as list of indexes to provided regions.
     \param  text_boxes     The output of the algorithm are stored in this parameter as list of rectangles.
     \param  filename       The XML or YAML file with the classifier model (e.g. trained_classifier_erGrouping.xml)
@@ -3158,7 +3158,7 @@ struct line_estimates
 };
 
 // distanceLinesEstimates
-// Calculates the distance between two line estimates defined as the largest
+// Calculates the distance between two line estimates defined as the largest
 // normalized vertical difference of their top/bottom lines at their boundary points
 // out float distance
 float distanceLinesEstimates(line_estimates &a, line_estimates &b);
@@ -3328,7 +3328,7 @@ void fitLineOLS(Point p1, Point p2, Point p3, float &a0, float &a1)
     a1=(float)(3*sumxy-sumx*sumy) / (3*sumx2-sumx*sumx);
 }
 
-// Fit line from three points using (heutistic) Least-Median of Squares
+// Fit line from three points using (heuristic) Least-Median of Squares
 // out a0 is the intercept
 // out a1 is the slope
 // returns the error of the single point that doesn't fit the line
@@ -3339,7 +3339,7 @@ float fitLineLMS(Point p1, Point p2, Point p3, float &a0, float &a1)
     a1 = 0;
 
     //Least-Median of Squares does not make sense with only three points
-    //becuse any line passing by two of them has median_error = 0
+    //because any line passing by two of them has median_error = 0
     //So we'll take the one with smaller slope
     float l_a0, l_a1, best_slope=FLT_MAX, err=0;
 
@@ -3730,7 +3730,7 @@ bool sort_couples (Vec3i i,Vec3i j) { return (i[0]<j[0]); }
 
     \param  _img           Original RGB image from wich the regions were extracted.
     \param  _src           Vector of sinle channel images CV_8UC1 from wich the regions were extracted.
-    \param  regions        Vector of ER's retreived from the ERFilter algorithm from each channel
+    \param  regions        Vector of ER's retrieved from the ERFilter algorithm from each channel
     \param  out_groups     The output of the algorithm are stored in this parameter as list of indexes to provided regions.
     \param  out_boxes      The output of the algorithm are stored in this parameter as list of rectangles.
     \param  do_feedback    Whenever the grouping algorithm uses a feedback loop to recover missing regions in a line.
@@ -3823,7 +3823,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
         {
             for (size_t j=i+1; j<valid_pairs.size(); j++)
             {
-                // check colinearity rules
+                // check collinearity rules
                 region_triplet valid_triplet(Vec2i(0,0),Vec2i(0,0),Vec2i(0,0));
                 if (isValidTriplet(regions, valid_pairs[i],valid_pairs[j], valid_triplet))
                 {
@@ -3891,7 +3891,7 @@ void erGroupingNM(InputArray _img, InputArrayOfArrays _src, vector< vector<ERSta
         if (do_feedback_loop)
         {
 
-            //Feedback loop of detected lines to region extraction ... tries to recover missmatches in the region decomposition step by extracting regions in the neighbourhood of a valid sequence and checking if they are consistent with its line estimates
+            //Feedback loop of detected lines to region extraction ... tries to recover mismatches in the region decomposition step by extracting regions in the neighbourhood of a valid sequence and checking if they are consistent with its line estimates
             Ptr<ERFilter> er_filter = createERFilterNM1(loadDummyClassifier(),1,0.005f,0.3f,0.f,false);
             for (int i=0; i<(int)valid_sequences.size(); i++)
             {
@@ -4172,7 +4172,7 @@ void MSERsToERStats(InputArray image, vector<vector<Point> > &contours, vector<v
   }
 }
 
-// Utility funtion for scripting
+// Utility function for scripting
 void detectRegions(InputArray image, const Ptr<ERFilter>& er_filter1, const Ptr<ERFilter>& er_filter2, CV_OUT vector< vector<Point> >& regions)
 {
     // assert correct image type

modules/text/src/ocr_beamsearch_decoder.cpp

@@ -144,7 +144,7 @@ struct beamSearch_node {
     double score;
     vector<int> segmentation;
     bool expanded;
-    // TODO calculating score of its childs would be much faster if we store the last column
+    // TODO calculating score of its child would be much faster if we store the last column
     //      of their "root" path.
 };
 
@@ -231,7 +231,7 @@ public:
 
         // TODO if input is a text line (not a word) we may need to split into words here!
 
-        // do sliding window classification along a croped word image
+        // do sliding window classification along a cropped word image
         classifier->eval(src, recognition_probabilities, oversegmentation);
 
         // if the number of oversegmentation points found is less than 2 we can not do nothing!!