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/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/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!!