fix java wrappers for ERFilter, OCRHMMDecoder, OCRBeamSearchDecoder (#1210)

* fix java wrappers for ERFilter, OCRHMMDecoder, OCRBeamSearchDecoder

* fix comments

issue #980 resolves - Some module has a round error similar to #975.  (#981)

* round function changes to cvRound

Minor changes to fix lower visual studio version build errors.

* round function changes to cvRound

Minor changes to fix lower visual studio version build errors.

* round function changes to cvRound

Minor changes to fix lower visual studio version build errors.

Bug in Text module when set Optimization Level to "-Ofast" in xcode (#921)

* Bug in Text module when set Optimization Level to "-Ofast" in xcode

Bug when set Optimization Level to "-Ofast" (Fastest, Aggressive Optimizations) in xcode, read the wrong patch_size parameters to 7 (should be 8). This bug will occured in loadOCRHMMClassifierCNN function called by example_text_character_recognition and example_text_segmented_word_recognition.

*  Bug in Text module when set Optimization Level to "-Ofast" in xcode

Bug when set Optimization Level to "-Ofast" (Fastest, Aggressive Optimizations) in xcode, read the wrong patch_size parameters to 7 (should be 8). This bug will occured in loadOCRHMMClassifierCNN function called by example_text_character_recognition and example_text_segmented_word_recognition.

changed double *p to vector<double>

Required for MSVC 2008, shouldn't affect results otherwise.

Overload the run() method in BaseOCR class in order to adapt to different classifier callbacks. The original run() method accepts only one Mat input image, this is expected to be a binarzed image with black and white text and works both with the OCRTesseract class and the OCRHMMDecoder class when the character classifier callback works with binary images (e.g. NM). The new run() method accepts two Mat input parameters. One for the gray scale (or color) source image and the other for a binary mask where each connected component corresponds to a pre-segmented character in the input image. This way the OCRHMMDecoder is able to work with character classifiers that operate in grey scale (or color) images (e.g. a CNN).

Fix bug #4373: Error (Assertion failed in resize) when passing very elongated contours to the recognition module

Adds createOCRHMMTransitionsTable() utility function to create a tailored language model transitions table from a given list of words (lexicon)