potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-30 07:43:03 +04:00
Code Packages Releases Wiki Activity

erGrouping now uses a classifier for group validation instead of a set of heuristical ifos.

Browse Source 
Updated documentation and sample to use the new function API
This commit is contained in:
lluis
2013-09-27 16:28:47 +02:00
parent 4b203f7b1a
commit 047b568f6d
13 changed files with 20277 additions and 212 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/objdetect/doc/erfilter.rst
+4 -2
View File
@@ -198,12 +198,14 @@ erGrouping
----------
Find groups of Extremal Regions that are organized as text blocks.
.. ocv:function:: void erGrouping( InputArrayOfArrays src, std::vector<std::vector<ERStat> > &regions, std::vector<Rect> &groups )
.. ocv:function:: void erGrouping( InputArrayOfArrays src, std::vector<std::vector<ERStat> > &regions, const std::string& filename, float minProbablity, std::vector<Rect > &groups)
: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 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).
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 heuristically validated in order to assess if they form a valid horizontally-aligned text block.
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.