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*/

#ifndef __OPENCV_TEXT_HPP__
#define __OPENCV_TEXT_HPP__

#include "opencv2/text/erfilter.hpp"
#include "opencv2/text/ocr.hpp"
#include "opencv2/text/textDetector.hpp"
#include "opencv2/text/swt_text_detection.hpp"

/** @defgroup text Scene Text Detection and Recognition

The opencv_text module provides different algorithms for text detection and recognition in natural
scene images.

  @{
    @defgroup text_detect Scene Text Detection

    Class-specific Extremal Regions for Scene Text Detection
    --------------------------------------------------------

    The scene text detection algorithm described below has been initially proposed by Lukás Neumann &
    Jiri Matas @cite Neumann11. The main idea behind Class-specific Extremal Regions is similar to the MSER
    in that suitable Extremal Regions (ERs) are selected from the whole component tree of the image.
    However, this technique differs from MSER in that selection of suitable ERs is done by a sequential
    classifier trained for character detection, i.e. dropping the stability requirement of MSERs and
    selecting class-specific (not necessarily stable) regions.

    The component tree of an image is constructed by thresholding by an increasing value step-by-step
    from 0 to 255 and then linking the obtained connected components from successive levels in a
    hierarchy by their inclusion relation:

    ![image](pics/component_tree.png)

    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'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
    p_min and the difference between local maximum and local minimum is greater than a delta_min
    value).

    In the second stage, the ERs that passed the first stage are classified into character and
    non-character classes using more informative but also more computationally expensive features. (Hole
    area ratio, convex hull ratio, and the number of outer boundary inflexion points).

    This ER filtering process is done in different single-channel projections of the input image in
    order to increase the character localization recall.

    After the ER filtering is done on each input channel, character candidates must be grouped in
    high-level text blocks (i.e. words, text lines, paragraphs, ...). The opencv_text module implements
    two different grouping algorithms: the Exhaustive Search algorithm proposed in @cite Neumann12 for
    grouping horizontally aligned text, and the method proposed by Lluis Gomez and Dimosthenis Karatzas
    in @cite Gomez13 @cite Gomez14 for grouping arbitrary oriented text (see erGrouping).

    To see the text detector at work, have a look at the textdetection demo:
    <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/textdetection.cpp>

    @defgroup text_recognize Scene Text Recognition
  @}
*/

#endif
