104 lines
5.2 KiB
C++
104 lines
5.2 KiB
C++
/*
|
|
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:
|
|
|
|
|
|
|
|
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
|