104 lines
5.2 KiB
C++
104 lines
5.2 KiB
C++
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/*
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By downloading, copying, installing or using the software you agree to this
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license. If you do not agree to this license, do not download, install,
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copy or use the software.
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License Agreement
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For Open Source Computer Vision Library
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(3-clause BSD License)
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Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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Third party copyrights are property of their respective owners.
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Redistribution and use in source and binary forms, with or without modification,
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are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice,
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this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the names of the copyright holders nor the names of the contributors
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may be used to endorse or promote products derived from this software
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without specific prior written permission.
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This software is provided by the copyright holders and contributors "as is" and
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any express or implied warranties, including, but not limited to, the implied
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warranties of merchantability and fitness for a particular purpose are
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disclaimed. In no event shall copyright holders or contributors be liable for
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any direct, indirect, incidental, special, exemplary, or consequential damages
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(including, but not limited to, procurement of substitute goods or services;
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loss of use, data, or profits; or business interruption) however caused
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and on any theory of liability, whether in contract, strict liability,
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or tort (including negligence or otherwise) arising in any way out of
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the use of this software, even if advised of the possibility of such damage.
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*/
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#ifndef __OPENCV_TEXT_HPP__
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#define __OPENCV_TEXT_HPP__
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#include "opencv2/text/erfilter.hpp"
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#include "opencv2/text/ocr.hpp"
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#include "opencv2/text/textDetector.hpp"
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#include "opencv2/text/swt_text_detection.hpp"
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/** @defgroup text Scene Text Detection and Recognition
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The opencv_text module provides different algorithms for text detection and recognition in natural
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scene images.
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@{
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@defgroup text_detect Scene Text Detection
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Class-specific Extremal Regions for Scene Text Detection
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--------------------------------------------------------
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The scene text detection algorithm described below has been initially proposed by Lukás Neumann &
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Jiri Matas @cite Neumann11. The main idea behind Class-specific Extremal Regions is similar to the MSER
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in that suitable Extremal Regions (ERs) are selected from the whole component tree of the image.
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However, this technique differs from MSER in that selection of suitable ERs is done by a sequential
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classifier trained for character detection, i.e. dropping the stability requirement of MSERs and
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selecting class-specific (not necessarily stable) regions.
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The component tree of an image is constructed by thresholding by an increasing value step-by-step
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from 0 to 255 and then linking the obtained connected components from successive levels in a
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hierarchy by their inclusion relation:
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The component tree may contain a huge number of regions even for a very simple image as shown in
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the previous image. This number can easily reach the order of 1 x 10\^6 regions for an average 1
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Megapixel image. In order to efficiently select suitable regions among all the ERs the algorithm
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make use of a sequential classifier with two differentiated stages.
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In the first stage incrementally computable descriptors (area, perimeter, bounding box, and Euler's
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number) are computed (in O(1)) for each region r and used as features for a classifier which
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estimates the class-conditional probability p(r|character). Only the ERs which correspond to local
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maximum of the probability p(r|character) are selected (if their probability is above a global limit
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p_min and the difference between local maximum and local minimum is greater than a delta_min
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value).
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In the second stage, the ERs that passed the first stage are classified into character and
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non-character classes using more informative but also more computationally expensive features. (Hole
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area ratio, convex hull ratio, and the number of outer boundary inflexion points).
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This ER filtering process is done in different single-channel projections of the input image in
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order to increase the character localization recall.
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After the ER filtering is done on each input channel, character candidates must be grouped in
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high-level text blocks (i.e. words, text lines, paragraphs, ...). The opencv_text module implements
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two different grouping algorithms: the Exhaustive Search algorithm proposed in @cite Neumann12 for
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grouping horizontally aligned text, and the method proposed by Lluis Gomez and Dimosthenis Karatzas
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in @cite Gomez13 @cite Gomez14 for grouping arbitrary oriented text (see erGrouping).
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To see the text detector at work, have a look at the textdetection demo:
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<https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/textdetection.cpp>
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@defgroup text_recognize Scene Text Recognition
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@}
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*/
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#endif
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