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Diffstat (limited to 'thirdparty1/linux/include/opencv2/text.hpp')
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diff --git a/thirdparty1/linux/include/opencv2/text.hpp b/thirdparty1/linux/include/opencv2/text.hpp new file mode 100644 index 0000000..945194a --- /dev/null +++ b/thirdparty1/linux/include/opencv2/text.hpp @@ -0,0 +1,101 @@ +/* +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" + +/** @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: + + + +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/opencv/opencv_contrib/blob/master/modules/text/samples/textdetection.cpp> + + @defgroup text_recognize Scene Text Recognition + @} +*/ + +#endif |