@@ -21,16 +21,20 @@ In the second stage, the ERs that passed the first stage are classified into cha
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 grouping algorithm used in this implementation has been proposed by Lluis Gomez and Dimosthenis Karatzas in [Gomez13] and basically consist in finding meaningful groups of regions using a perceptual organization based clustering analisys (see :ocv:func:`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 [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 :ocv:func:`erGrouping`).
To see the text detector at work, have a look at the textdetection demo: https://github.com/Itseez/opencv/blob/master/samples/cpp/textdetection.cpp
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
.. [Neumann12] Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012. The paper is available online at http://cmp.felk.cvut.cz/~neumalu1/neumann-cvpr2012.pdf
.. [Neumann11] Neumann L., Matas J.: Text Localization in Real-world Images using Efficiently Pruned Exhaustive Search, ICDAR 2011. The paper is available online at http://cmp.felk.cvut.cz/~neumalu1/icdar2011_article.pdf
.. [Gomez13] Gomez L. and Karatzas D.: Multi-script Text Extraction from Natural Scenes, ICDAR 2013. The paper is available online at http://158.109.8.37/files/GoK2013.pdf
.. [Gomez14] Gomez L. and Karatzas D.: A Fast Hierarchical Method for Multi-script and Arbitrary Oriented Scene Text Extraction, arXiv:1407.7504 [cs.CV]. The paper is available online at http://arxiv.org/abs/1407.7504
ERStat
------
...
...
@@ -198,14 +202,24 @@ erGrouping
----------
Find groups of Extremal Regions that are organized as text blocks.
:param image: Original RGB or Greyscale image from wich the regions were extracted.
:param src: Vector of single channel images CV_8UC1 from wich the regions were extracted.
:param regions: Vector of ER's retreived from the ERFilter algorithm from each channel.
:param groups: The output of the algorithm is stored in this parameter as set of lists of indexes to provided regions.
:param groups_rects: The output of the algorithm are stored in this parameter as list of rectangles.
:param method: Grouping method (see the details below). Can be one of ``ERGROUPING_ORIENTATION_HORIZ``, ``ERGROUPING_ORIENTATION_ANY``.
:param filename: The XML or YAML file with the classifier model (e.g. samples/trained_classifier_erGrouping.xml). Only to use when grouping method is ``ERGROUPING_ORIENTATION_ANY``.
:param minProbability: The minimum probability for accepting a group. Only to use when grouping method is ``ERGROUPING_ORIENTATION_ANY``.
:param src: Vector of sinle channel images CV_8UC1 from wich the regions were extracted
:param regions: Vector of ER's retreived from the ERFilter algorithm from each channel
:param filename: The XML or YAML file with the classifier model (e.g. trained_classifier_erGrouping.xml)
:param minProbability: The minimum probability for accepting a group
:param groups: The output of the algorithm are stored in this parameter as list of rectangles.
This function implements two different grouping algorithms:
This function implements the grouping algorithm described in [Gomez13]. Notice that this implementation constrains the results to horizontally-aligned text and latin script (since ERFilter classifiers are trained only for latin script detection).
* **ERGROUPING_ORIENTATION_HORIZ**
Exhaustive Search algorithm proposed in [Neumann11] for grouping horizontally aligned text. The algorithm models a verification function for all the possible ER sequences. The verification fuction for ER pairs consists in a set of threshold-based pairwise rules which compare measurements of two regions (height ratio, centroid angle, and region distance). The verification function for ER triplets creates a word text line estimate using Least Median-Squares fitting for a given triplet and then verifies that the estimate is valid (based on thresholds created during training). Verification functions for sequences larger than 3 are approximated by verifying that the text line parameters of all (sub)sequences of length 3 are consistent.
The algorithm combines two different clustering techniques in a single parameter-free procedure to detect groups of regions organized as text. The maximally meaningful groups are fist detected in several feature spaces, where each feature space is a combination of proximity information (x,y coordinates) and a similarity measure (intensity, color, size, gradient magnitude, etc.), thus providing a set of hypotheses of text groups. Evidence Accumulation framework is used to combine all these hypotheses to get the final estimate. Each of the resulting groups are finally validated using a classifier in order to assess if they form a valid horizontally-aligned text block.
* **ERGROUPING_ORIENTATION_ANY**
Text grouping method proposed in [Gomez13][Gomez14] for grouping arbitrary oriented text. Regions are agglomerated by Single Linkage Clustering in a weighted feature space that combines proximity (x,y coordinates) and similarity measures (color, size, gradient magnitude, stroke width, etc.). SLC provides a dendrogram where each node represents a text group hypothesis. Then the algorithm finds the branches corresponding to text groups by traversing this dendrogram with a stopping rule that combines the output of a rotation invariant text group classifier and a probabilistic measure for hierarchical clustering validity assessment.
OCRTesseract class provides an interface with the tesseract-ocr API (v3.02.02) in C++. Notice that it is compiled only when tesseract-ocr is correctly installed. ::
To see the OCRTesseract combined with scene text detection, have a look at the end_to_end_recognition demo: https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/end_to_end_recognition.cpp
:param datapath: the name of the parent directory of tessdata ended with "/", or NULL to use the system's default directory.
:param language: an ISO 639-3 code or NULL will default to "eng".
:param char_whitelist: specifies the list of characters used for recognition. NULL defaults to "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ".
:param oem: tesseract-ocr offers different OCR Engine Modes (OEM), by deffault tesseract::OEM_DEFAULT is used. See the tesseract-ocr API documentation for other possible values.
:param psmode: tesseract-ocr offers different Page Segmentation Modes (PSM) tesseract::PSM_AUTO (fully automatic layout analysis) is used. See the tesseract-ocr API documentation for other possible values.
OCRTesseract::run
-----------------
Recognize text using the tesseract-ocr API. Takes image on input and returns recognized text in the output_text parameter. Optionally provides also the Rects for individual text elements found (e.g. words), and the list of those text elements with their confidence values.
:param image: Input image ``CV_8UC1`` or ``CV_8UC3``
:param output_text: Output text of the tesseract-ocr.
:param component_rects: If provided the method will output a list of Rects for the individual text elements found (e.g. words or text lines).
:param component_text: If provided the method will output a list of text strings for the recognition of individual text elements found (e.g. words or text lines).
:param component_confidences: If provided the method will output a list of confidence values for the recognition of individual text elements found (e.g. words or text lines).
:param component_level: ``OCR_LEVEL_WORD`` (by default), or ``OCR_LEVEL_TEXT_LINE``.
