Fix java wrappers for Text module

modules/text/CMakeLists.txt

@@ -1,5 +1,5 @@
 set(the_description "Text Detection and Recognition")
-ocv_define_module(text opencv_ml opencv_imgproc opencv_core opencv_features2d OPTIONAL opencv_highgui WRAP python)
+ocv_define_module(text opencv_ml opencv_imgproc opencv_core opencv_features2d OPTIONAL opencv_highgui WRAP python java)
 
 if(NOT CMAKE_CROSSCOMPILING OR OPENCV_FIND_TESSERACT)
   set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_CURRENT_SOURCE_DIR}/cmake)

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

@@ -193,10 +193,10 @@ public:
 loadClassifierNM1, e.g. 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  maxArea :   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
+@param  minProbabilityDiff :   The minimum probability difference between local maxima and local minima ERs
 
 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
@@ -227,6 +227,24 @@ features: hole area ratio, convex hull ratio, and number of outer inflexion poin
 CV_EXPORTS_W Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb,
                                                   float minProbability = (float)0.3);
 
+/** @brief Reads an Extremal Region Filter for the 1st stage classifier of N&M algorithm
+    from the provided path e.g. /path/to/cpp/trained_classifierNM1.xml
+
+@overload
+ */
+CV_EXPORTS_W  Ptr<ERFilter> createERFilterNM1(const String& filename,
+                                                  int thresholdDelta = 1, float minArea = (float)0.00025,
+                                                  float maxArea = (float)0.13, float minProbability = (float)0.4,
+                                                  bool nonMaxSuppression = true,
+                                                  float minProbabilityDiff = (float)0.1);
+
+/** @brief Reads an Extremal Region Filter for the 2nd stage classifier of N&M algorithm
+    from the provided path e.g. /path/to/cpp/trained_classifierNM2.xml
+
+@overload
+ */
+CV_EXPORTS_W Ptr<ERFilter> createERFilterNM2(const String& filename,
+                                                  float minProbability = (float)0.3);
 
 /** @brief Allow to implicitly load the default classifier when creating an ERFilter object.
 

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

@@ -61,6 +61,31 @@ enum
     OCR_LEVEL_TEXTLINE
 };
 
+//! Tesseract.PageSegMode Enumeration
+enum page_seg_mode
+{
+    PSM_OSD_ONLY,
+    PSM_AUTO_OSD,
+    PSM_AUTO_ONLY,
+    PSM_AUTO,
+    PSM_SINGLE_COLUMN,
+    PSM_SINGLE_BLOCK_VERT_TEXT,
+    PSM_SINGLE_BLOCK,
+    PSM_SINGLE_LINE,
+    PSM_SINGLE_WORD,
+    PSM_CIRCLE_WORD,
+    PSM_SINGLE_CHAR
+};
+
+//! Tesseract.OcrEngineMode Enumeration
+enum ocr_engine_mode
+{
+    OEM_TESSERACT_ONLY,
+    OEM_CUBE_ONLY,
+    OEM_TESSERACT_CUBE_COMBINED,
+    OEM_DEFAULT
+};
+
 //base class BaseOCR declares a common API that would be used in a typical text recognition scenario
 class CV_EXPORTS_W BaseOCR
 {
@@ -136,7 +161,7 @@ public:
     possible values.
      */
     CV_WRAP static Ptr<OCRTesseract> create(const char* datapath=NULL, const char* language=NULL,
-                                    const char* char_whitelist=NULL, int oem=3, int psmode=3);
+                                    const char* char_whitelist=NULL, int oem=OEM_DEFAULT, int psmode=PSM_AUTO);
 };
 
 

modules/text/src/erfilter.cpp

@@ -279,9 +279,9 @@ void ERFilterNM::er_tree_extract( InputArray image )
 
     // the quads for euler 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 
-    // (see in page 4 at the end of first column). 
+    // 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
+    // (see in page 4 at the end of first column).
     // Q_1 and Q_2 have four patterns, while Q_3 has only two.
     const int quads[3][4] =
     {
@@ -1145,6 +1145,17 @@ Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProb
     return (Ptr<ERFilter>)filter;
 }
 
+Ptr<ERFilter> createERFilterNM1(const String& filename, int _thresholdDelta,
+                                float _minArea, float _maxArea, float _minProbability,
+                                bool _nonMaxSuppression, float _minProbabilityDiff) {
+    return createERFilterNM1(loadClassifierNM1(filename), _thresholdDelta, _minArea, _maxArea, _minProbability, _nonMaxSuppression, _minProbabilityDiff);
+
+}
+
+Ptr<ERFilter> createERFilterNM2(const String& filename, float _minProbability) {
+    return createERFilterNM2(loadClassifierNM2(filename), _minProbability);
+}
+
 /*!
     Allow to implicitly load the default classifier when creating an ERFilter object.
     The function takes as parameter the XML or YAML file with the classifier model