Doxygen documentation for all modules

modules/text/include/opencv2/text.hpp

@@ -42,4 +42,60 @@ the use of this software, even if advised of the possibility of such damage.
 #include "opencv2/text/erfilter.hpp"
 #include "opencv2/text/ocr.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 [Neumann12]. 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 conatain 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
+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 [Neumann11] for
+grouping horizontally aligned text, and the method proposed by Lluis Gomez and Dimosthenis Karatzas
+in [Gomez13][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/Itseez/opencv_contrib/blob/master/modules/text/samples/textdetection.cpp>
+
+    @defgroup text_recognize Scene Text Recognition
+  @}
+*/
+
 #endif