[−][src]Function opencv::text::create_er_filter_nm1_1

pub fn create_er_filter_nm1_1(
    filename: &str, 
    threshold_delta: i32, 
    min_area: f32, 
    max_area: f32, 
    min_probability: f32, 
    non_max_suppression: bool, 
    min_probability_diff: f32
) -> Result<Ptr<dyn ERFilter>>

Reads an Extremal Region Filter for the 1st stage classifier of N&M algorithm from the provided path e.g. /path/to/cpp/trained_classifierNM1.xml

Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm Neumann12.

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 component tree of the image is extracted by a threshold increased step by step from 0 to 255, incrementally computable descriptors (aspect_ratio, compactness, number of holes, and number of horizontal crossings) are computed for each ER and used as features for a classifier which estimates the class-conditional probability P(er|character). The value of P(er|character) is tracked using the inclusion relation of ER across all thresholds and only the ERs which correspond to local maximum of the probability P(er|character) are selected (if the local maximum of the probability is above a global limit pmin and the difference between local maximum and local minimum is greater than minProbabilityDiff).

Overloaded parameters

C++ default parameters

  • threshold_delta: 1
  • min_area: (float)0.00025
  • max_area: (float)0.13
  • min_probability: (float)0.4
  • non_max_suppression: true
  • min_probability_diff: (float)0.1