opencv/opencv_contrib
1
0
Fork 0
mirror of https://github.com/opencv/opencv_contrib.git synced 2025-10-20 04:25:42 +08:00
Code Issues Releases Wiki Activity

Merge pull request #3687 from mshabunin:doc-upgrade-5.x

Browse Source 
Documentation transition to fresh Doxygen (5.x) #3687

Port of #3638
Merge with opencv/opencv#25161
CI part: opencv/ci-gha-workflow#162
This commit is contained in:
Maksim Shabunin
2024-03-06 09:54:37 +03:00
committed by GitHub
parent 6f68a4ebcc
commit 8f3a61b83c
32 changed files with 212 additions and 224 deletions
Download Patch File Download Diff File Expand all files Collapse all files

68
modules/text/include/opencv2/text.hpp
View File

@@ -52,49 +52,49 @@ scene images.
@{
@defgroup text_detect Scene Text Detection
Class-specific Extremal Regions for 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 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:
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)
![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.
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 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).
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.
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).
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>
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
@}