vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

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+/*
+By downloading, copying, installing or using the software you agree to this
+license. If you do not agree to this license, do not download, install,
+copy or use the software.
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are
+disclaimed. In no event shall copyright holders or contributors be liable for
+any direct, indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+*/
+
+#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