Adds two more examples of croped word recognition, and show how to set the beam size parameter for small lexicon.

OCRBeamSearchDecoder code refactor: fixes most of the TODOs comments, cleans-up code, and fix a bug in score_segmentation(...) member function.

fix small bug

Adds example on segmented word recognition. Shows the use of the OCRHMMDecoder with the NM and CNN default classifiers.

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).

Better CNN model for character recognition. Trained with an augmented dataset by adding translation/scale variations. Updated the croped word recognition with new class numbering (compatible with previous NM classifier).

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

update to use the new createOCRHMMTransitionsTable() function, and fix program description in header comments

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

Adds a first implementation of the OCRBeamSearchDecoder class using the Single Layer CNN character classifier described in Coates, Adam, et al. paper: Text detection and character recognition in scene images with unsupervised feature learning, ICDAR 2011

Fix for Bug #3633: do away with "quads [2][3] = 255;" The four lowest bits in each quads[i][j] correspond to the 2x2 binary patterns Q_1, Q_2, Q_3 in the NM paper [1] (see in page 4 at the end of first column). Q_1 and Q_2 have four patterns, while Q_3 has only two.

Fix for Bug 4074. This seems to be just a typo-error, because the Tesseract API can handle correctly with RGB images (double-checked and it works).

fixed several warnings in opencv_contrib (text, xfeatures2d); added tesseract installation mini-guide in text/readme.md