createERFilterNM1 - Parameter

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I'm using the Scene Text Detection features of OpenCV and have some troubles finding the optimal parameters for createERFilterNM1. Here is what the doc states:

C++: Ptr<erfilter> createERFilterNM1(const Ptr<erfilter::callback>& cb, int thresholdDelta=1, float minArea=0.00025, float maxArea=0.13, float minProbability=0.4, bool nonMaxSuppression=true, float minProbabilityDiff=0.1 ) Parameters:

• cb – Callback with the classifier. Default classifier can be implicitly load with function loadClassifierNM1(), e.g. from file in samples/cpp/trained_classifierNM1.xml
• thresholdDelta – Threshold step in subsequent thresholds when extracting the component tree
• minArea – The minimum area (% of image size) allowed for retreived ER’s
• maxArea – The maximum area (% of image size) allowed for retreived ER’s
• minProbability – The minimum probability P(er|character) allowed for retreived ER’s
• nonMaxSuppression – Whenever non-maximum suppression is done over the branch probabilities
• minProbabilityDiff – The minimum probability difference between local maxima and local minima ERs

The default values are not the same as in the example. So I wonder what the optimal settings are.

Would it be possible to optimize the settings when I know the fontsize of the to-detect-text? Maybe by setting the max/minArea to the size of a letter? Keeping this in mind I tried to tweak the settings a bit to get text detection for this image to work (I would like to detect the subtitles). With thresholdDelta = 16, minArea = 0.0004 maxArea = 0.04 I get these results. Changing minProbability and minProbabilityDiff seems to change nothing. But maybe it's no use given the quality of the source image.

I lack the clear understanding of the parameters. Could someone please explain the parameters more in detail for me or give some hints on how to find the optimal parameters?

EDIT After reading the doc more carefully I think I understand everything. Ill try to optimize the parameter for a given text size and post again. This may take some time though.