Package org.opencv.text

Class Text

  • public class Text
extends Object

    • Constructor Detail

      • Text

        public Text()

    • Method Detail

      • detectTextSWT

        public static void detectTextSWT​(Mat input,
                                 MatOfRect result,
                                 boolean dark_on_light,
                                 Mat draw,
                                 Mat chainBBs)
        Applies the Stroke Width Transform operator followed by filtering of connected components of similar Stroke Widths to return letter candidates. It also chain them by proximity and size, saving the result in chainBBs.
        Parameters:
        input - the input image with 3 channels.
        result - a vector of resulting bounding boxes where probability of finding text is high
        dark_on_light - a boolean value signifying whether the text is darker or lighter than the background, it is observed to reverse the gradient obtained from Scharr operator, and significantly affect the result.
        draw - an optional Mat of type CV_8UC3 which visualises the detected letters using bounding boxes.
        chainBBs - an optional parameter which chains the letter candidates according to heuristics in the paper and returns all possible regions where text is likely to occur.

      • detectTextSWT

        public static void detectTextSWT​(Mat input,
                                 MatOfRect result,
                                 boolean dark_on_light,
                                 Mat draw)
        Applies the Stroke Width Transform operator followed by filtering of connected components of similar Stroke Widths to return letter candidates. It also chain them by proximity and size, saving the result in chainBBs.
        Parameters:
        input - the input image with 3 channels.
        result - a vector of resulting bounding boxes where probability of finding text is high
        dark_on_light - a boolean value signifying whether the text is darker or lighter than the background, it is observed to reverse the gradient obtained from Scharr operator, and significantly affect the result.
        draw - an optional Mat of type CV_8UC3 which visualises the detected letters using bounding boxes.

      • detectTextSWT

        public static void detectTextSWT​(Mat input,
                                 MatOfRect result,
                                 boolean dark_on_light)
        Applies the Stroke Width Transform operator followed by filtering of connected components of similar Stroke Widths to return letter candidates. It also chain them by proximity and size, saving the result in chainBBs.
        Parameters:
        input - the input image with 3 channels.
        result - a vector of resulting bounding boxes where probability of finding text is high
        dark_on_light - a boolean value signifying whether the text is darker or lighter than the background, it is observed to reverse the gradient obtained from Scharr operator, and significantly affect the result.

      • loadOCRHMMClassifierNM

        @Deprecated
public static OCRHMMDecoder_ClassifierCallback loadOCRHMMClassifierNM​(String filename)
        Deprecated.
        loadOCRHMMClassifier instead
        Allow to implicitly load the default character classifier when creating an OCRHMMDecoder object.
        Parameters:
        filename - The XML or YAML file with the classifier model (e.g. OCRHMM_knn_model_data.xml) The KNN default classifier is based in the scene text recognition method proposed by Lukás Neumann & Jiri Matas in [Neumann11b]. Basically, the region (contour) in the input image is normalized to a fixed size, while retaining the centroid and aspect ratio, in order to extract a feature vector based on gradient orientations along the chain-code of its perimeter. Then, the region is classified using a KNN model trained with synthetic data of rendered characters with different standard font types.
        Returns:
        automatically generated

      • loadOCRHMMClassifierCNN

        @Deprecated
public static OCRHMMDecoder_ClassifierCallback loadOCRHMMClassifierCNN​(String filename)
        Deprecated.
        use loadOCRHMMClassifier instead
        Allow to implicitly load the default character classifier when creating an OCRHMMDecoder object.
        Parameters:
        filename - The XML or YAML file with the classifier model (e.g. OCRBeamSearch_CNN_model_data.xml.gz) The CNN default classifier is based in the scene text recognition method proposed by Adam Coates & Andrew NG in [Coates11a]. The character classifier consists in a Single Layer Convolutional Neural Network and a linear classifier. It is applied to the input image in a sliding window fashion, providing a set of recognitions at each window location.
        Returns:
        automatically generated

      • loadOCRHMMClassifier

        public static OCRHMMDecoder_ClassifierCallback loadOCRHMMClassifier​(String filename,
                                                                    int classifier)
        Allow to implicitly load the default character classifier when creating an OCRHMMDecoder object.
        Parameters:
        filename - The XML or YAML file with the classifier model (e.g. OCRBeamSearch_CNN_model_data.xml.gz)
        classifier - Can be one of classifier_type enum values.
        Returns:
        automatically generated

      • createOCRHMMTransitionsTable

        public static Mat createOCRHMMTransitionsTable​(String vocabulary,
                                               List<String> lexicon)
        Utility function to create a tailored language model transitions table from a given list of words (lexicon).
        Parameters:
        vocabulary - The language vocabulary (chars when ASCII English text).
        lexicon - The list of words that are expected to be found in a particular image. The function calculate frequency statistics of character pairs from the given lexicon and fills the output transition_probabilities_table with them. The transition_probabilities_table can be used as input in the OCRHMMDecoder::create() and OCRBeamSearchDecoder::create() methods. Note: - (C++) An alternative would be to load the default generic language transition table provided in the text module samples folder (created from ispell 42869 english words list) : <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/OCRHMM_transitions_table.xml>
        Returns:
        automatically generated

      • loadOCRBeamSearchClassifierCNN

        public static OCRBeamSearchDecoder_ClassifierCallback loadOCRBeamSearchClassifierCNN​(String filename)
        Allow to implicitly load the default character classifier when creating an OCRBeamSearchDecoder object.
        Parameters:
        filename - The XML or YAML file with the classifier model (e.g. OCRBeamSearch_CNN_model_data.xml.gz) The CNN default classifier is based in the scene text recognition method proposed by Adam Coates & Andrew NG in [Coates11a]. The character classifier consists in a Single Layer Convolutional Neural Network and a linear classifier. It is applied to the input image in a sliding window fashion, providing a set of recognitions at each window location.
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(ERFilter_Callback cb,
                                         int thresholdDelta,
                                         float minArea,
                                         float maxArea,
                                         float minProbability,
                                         boolean nonMaxSuppression,
                                         float minProbabilityDiff)
        Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12. loadClassifierNM1, e.g. from file in samples/cpp/trained_classifierNM1.xml 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).
        Parameters:
        cb - automatically generated
        thresholdDelta - automatically generated
        minArea - automatically generated
        maxArea - automatically generated
        minProbability - automatically generated
        nonMaxSuppression - automatically generated
        minProbabilityDiff - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(ERFilter_Callback cb,
                                         int thresholdDelta,
                                         float minArea,
                                         float maxArea,
                                         float minProbability,
                                         boolean nonMaxSuppression)
        Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12. loadClassifierNM1, e.g. from file in samples/cpp/trained_classifierNM1.xml 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).
        Parameters:
        cb - automatically generated
        thresholdDelta - automatically generated
        minArea - automatically generated
        maxArea - automatically generated
        minProbability - automatically generated
        nonMaxSuppression - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(ERFilter_Callback cb,
                                         int thresholdDelta,
                                         float minArea,
                                         float maxArea,
                                         float minProbability)
        Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12. loadClassifierNM1, e.g. from file in samples/cpp/trained_classifierNM1.xml 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).
        Parameters:
        cb - automatically generated
        thresholdDelta - automatically generated
        minArea - automatically generated
        maxArea - automatically generated
        minProbability - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(ERFilter_Callback cb,
                                         int thresholdDelta,
                                         float minArea,
                                         float maxArea)
        Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12. loadClassifierNM1, e.g. from file in samples/cpp/trained_classifierNM1.xml 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).
        Parameters:
        cb - automatically generated
        thresholdDelta - automatically generated
        minArea - automatically generated
        maxArea - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(ERFilter_Callback cb,
                                         int thresholdDelta,
                                         float minArea)
        Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12. loadClassifierNM1, e.g. from file in samples/cpp/trained_classifierNM1.xml 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).
        Parameters:
        cb - automatically generated
        thresholdDelta - automatically generated
        minArea - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(ERFilter_Callback cb,
                                         int thresholdDelta)
        Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12. loadClassifierNM1, e.g. from file in samples/cpp/trained_classifierNM1.xml 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).
        Parameters:
        cb - automatically generated
        thresholdDelta - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(ERFilter_Callback cb)
        Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12. loadClassifierNM1, e.g. from file in samples/cpp/trained_classifierNM1.xml 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).
        Parameters:
        cb - automatically generated
        Returns:
        automatically generated

      • createERFilterNM2

        public static ERFilter createERFilterNM2​(ERFilter_Callback cb,
                                         float minProbability)
        Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm CITE: Neumann12. loadClassifierNM2, e.g. from file in samples/cpp/trained_classifierNM2.xml 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. The classifier uses all the features calculated in the first stage and the following additional features: hole area ratio, convex hull ratio, and number of outer inflexion points.
        Parameters:
        cb - automatically generated
        minProbability - automatically generated
        Returns:
        automatically generated

      • createERFilterNM2

        public static ERFilter createERFilterNM2​(ERFilter_Callback cb)
        Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm CITE: Neumann12. loadClassifierNM2, e.g. from file in samples/cpp/trained_classifierNM2.xml 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. The classifier uses all the features calculated in the first stage and the following additional features: hole area ratio, convex hull ratio, and number of outer inflexion points.
        Parameters:
        cb - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(String filename,
                                         int thresholdDelta,
                                         float minArea,
                                         float maxArea,
                                         float minProbability,
                                         boolean nonMaxSuppression,
                                         float minProbabilityDiff)
        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
        Parameters:
        filename - automatically generated
        thresholdDelta - automatically generated
        minArea - automatically generated
        maxArea - automatically generated
        minProbability - automatically generated
        nonMaxSuppression - automatically generated
        minProbabilityDiff - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(String filename,
                                         int thresholdDelta,
                                         float minArea,
                                         float maxArea,
                                         float minProbability,
                                         boolean nonMaxSuppression)
        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
        Parameters:
        filename - automatically generated
        thresholdDelta - automatically generated
        minArea - automatically generated
        maxArea - automatically generated
        minProbability - automatically generated
        nonMaxSuppression - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(String filename,
                                         int thresholdDelta,
                                         float minArea,
                                         float maxArea,
                                         float minProbability)
        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
        Parameters:
        filename - automatically generated
        thresholdDelta - automatically generated
        minArea - automatically generated
        maxArea - automatically generated
        minProbability - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(String filename,
                                         int thresholdDelta,
                                         float minArea,
                                         float maxArea)
        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
        Parameters:
        filename - automatically generated
        thresholdDelta - automatically generated
        minArea - automatically generated
        maxArea - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(String filename,
                                         int thresholdDelta,
                                         float minArea)
        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
        Parameters:
        filename - automatically generated
        thresholdDelta - automatically generated
        minArea - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(String filename,
                                         int thresholdDelta)
        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
        Parameters:
        filename - automatically generated
        thresholdDelta - automatically generated
        Returns:
        automatically generated

      • createERFilterNM1

        public static ERFilter createERFilterNM1​(String filename)
        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
        Parameters:
        filename - automatically generated
        Returns:
        automatically generated

      • createERFilterNM2

        public static ERFilter createERFilterNM2​(String filename,
                                         float minProbability)
        Reads an Extremal Region Filter for the 2nd stage classifier of N&M algorithm from the provided path e.g. /path/to/cpp/trained_classifierNM2.xml
        Parameters:
        filename - automatically generated
        minProbability - automatically generated
        Returns:
        automatically generated

      • createERFilterNM2

        public static ERFilter createERFilterNM2​(String filename)
        Reads an Extremal Region Filter for the 2nd stage classifier of N&M algorithm from the provided path e.g. /path/to/cpp/trained_classifierNM2.xml
        Parameters:
        filename - automatically generated
        Returns:
        automatically generated

      • loadClassifierNM1

        public static ERFilter_Callback loadClassifierNM1​(String filename)
        Allow to implicitly load the default classifier when creating an ERFilter object.
        Parameters:
        filename - The XML or YAML file with the classifier model (e.g. trained_classifierNM1.xml) returns a pointer to ERFilter::Callback.
        Returns:
        automatically generated

      • loadClassifierNM2

        public static ERFilter_Callback loadClassifierNM2​(String filename)
        Allow to implicitly load the default classifier when creating an ERFilter object.
        Parameters:
        filename - The XML or YAML file with the classifier model (e.g. trained_classifierNM2.xml) returns a pointer to ERFilter::Callback.
        Returns:
        automatically generated

      • computeNMChannels

        public static void computeNMChannels​(Mat _src,
                                     List<Mat> _channels,
                                     int _mode)
        Compute the different channels to be processed independently in the N&M algorithm CITE: Neumann12.
        Parameters:
        _src - Source image. Must be RGB CV_8UC3.
        _channels - Output vector<Mat> where computed channels are stored.
        _mode - Mode of operation. Currently the only available options are: ERFILTER_NM_RGBLGrad (used by default) and ERFILTER_NM_IHSGrad. In N&M algorithm, the combination of intensity (I), hue (H), saturation (S), and gradient magnitude channels (Grad) are used in order to obtain high localization recall. This implementation also provides an alternative combination of red (R), green (G), blue (B), lightness (L), and gradient magnitude (Grad).

      • computeNMChannels

        public static void computeNMChannels​(Mat _src,
                                     List<Mat> _channels)
        Compute the different channels to be processed independently in the N&M algorithm CITE: Neumann12.
        Parameters:
        _src - Source image. Must be RGB CV_8UC3.
        _channels - Output vector<Mat> where computed channels are stored. ERFILTER_NM_RGBLGrad (used by default) and ERFILTER_NM_IHSGrad. In N&M algorithm, the combination of intensity (I), hue (H), saturation (S), and gradient magnitude channels (Grad) are used in order to obtain high localization recall. This implementation also provides an alternative combination of red (R), green (G), blue (B), lightness (L), and gradient magnitude (Grad).

      • erGrouping

        public static void erGrouping​(Mat image,
                              Mat channel,
                              List<MatOfPoint> regions,
                              MatOfRect groups_rects,
                              int method,
                              String filename,
                              float minProbablity)
        Find groups of Extremal Regions that are organized as text blocks.
        Parameters:
        regions - Vector of ER's retrieved from the ERFilter algorithm from each channel. provided regions.
        groups_rects - The output of the algorithm are stored in this parameter as list of rectangles.
        method - Grouping method (see text::erGrouping_Modes). Can be one of ERGROUPING_ORIENTATION_HORIZ, ERGROUPING_ORIENTATION_ANY.
        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.
        minProbablity - The minimum probability for accepting a group. Only to use when grouping method is ERGROUPING_ORIENTATION_ANY.
        image - automatically generated
        channel - automatically generated

      • erGrouping

        public static void erGrouping​(Mat image,
                              Mat channel,
                              List<MatOfPoint> regions,
                              MatOfRect groups_rects,
                              int method,
                              String filename)
        Find groups of Extremal Regions that are organized as text blocks.
        Parameters:
        regions - Vector of ER's retrieved from the ERFilter algorithm from each channel. provided regions.
        groups_rects - The output of the algorithm are stored in this parameter as list of rectangles.
        method - Grouping method (see text::erGrouping_Modes). Can be one of ERGROUPING_ORIENTATION_HORIZ, ERGROUPING_ORIENTATION_ANY.
        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. method is ERGROUPING_ORIENTATION_ANY.
        image - automatically generated
        channel - automatically generated

      • erGrouping

        public static void erGrouping​(Mat image,
                              Mat channel,
                              List<MatOfPoint> regions,
                              MatOfRect groups_rects,
                              int method)
        Find groups of Extremal Regions that are organized as text blocks.
        Parameters:
        regions - Vector of ER's retrieved from the ERFilter algorithm from each channel. provided regions.
        groups_rects - The output of the algorithm are stored in this parameter as list of rectangles.
        method - Grouping method (see text::erGrouping_Modes). Can be one of ERGROUPING_ORIENTATION_HORIZ, ERGROUPING_ORIENTATION_ANY. samples/trained_classifier_erGrouping.xml). Only to use when grouping method is ERGROUPING_ORIENTATION_ANY. method is ERGROUPING_ORIENTATION_ANY.
        image - automatically generated
        channel - automatically generated

      • erGrouping

        public static void erGrouping​(Mat image,
                              Mat channel,
                              List<MatOfPoint> regions,
                              MatOfRect groups_rects)
        Find groups of Extremal Regions that are organized as text blocks.
        Parameters:
        regions - Vector of ER's retrieved from the ERFilter algorithm from each channel. provided regions.
        groups_rects - The output of the algorithm are stored in this parameter as list of rectangles. ERGROUPING_ORIENTATION_ANY. samples/trained_classifier_erGrouping.xml). Only to use when grouping method is ERGROUPING_ORIENTATION_ANY. method is ERGROUPING_ORIENTATION_ANY.
        image - automatically generated
        channel - automatically generated

      • detectRegions

        public static void detectRegions​(Mat image,
                                 ERFilter er_filter1,
                                 ERFilter er_filter2,
                                 List<MatOfPoint> regions)
        Converts MSER contours (vector<Point>) to ERStat regions.
        Parameters:
        image - Source image CV_8UC1 from which the MSERs where extracted.
        regions - Output where the ERStat regions are stored. It takes as input the contours provided by the OpenCV MSER feature detector and returns as output two vectors of ERStats. This is because MSER() output contains both MSER+ and MSER- regions in a single vector<Point>, the function separates them in two different vectors (this is as if the ERStats where extracted from two different channels). An example of MSERsToERStats in use can be found in the text detection webcam_demo: <https://github.com/opencv/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp>
        er_filter1 - automatically generated
        er_filter2 - automatically generated

      • detectRegions

        public static void detectRegions​(Mat image,
                                 ERFilter er_filter1,
                                 ERFilter er_filter2,
                                 MatOfRect groups_rects,
                                 int method,
                                 String filename,
                                 float minProbability)
        Extracts text regions from image.
        Parameters:
        image - Source image where text blocks needs to be extracted from. Should be CV_8UC3 (color).
        er_filter1 - Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12
        er_filter2 - Extremal Region Filter for the 2nd stage classifier of N&M algorithm CITE: Neumann12
        groups_rects - Output list of rectangle blocks with text
        method - Grouping method (see text::erGrouping_Modes). Can be one of ERGROUPING_ORIENTATION_HORIZ, ERGROUPING_ORIENTATION_ANY.
        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.
        minProbability - The minimum probability for accepting a group. Only to use when grouping method is ERGROUPING_ORIENTATION_ANY.

      • detectRegions

        public static void detectRegions​(Mat image,
                                 ERFilter er_filter1,
                                 ERFilter er_filter2,
                                 MatOfRect groups_rects,
                                 int method,
                                 String filename)
        Extracts text regions from image.
        Parameters:
        image - Source image where text blocks needs to be extracted from. Should be CV_8UC3 (color).
        er_filter1 - Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12
        er_filter2 - Extremal Region Filter for the 2nd stage classifier of N&M algorithm CITE: Neumann12
        groups_rects - Output list of rectangle blocks with text
        method - Grouping method (see text::erGrouping_Modes). Can be one of ERGROUPING_ORIENTATION_HORIZ, ERGROUPING_ORIENTATION_ANY.
        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.

      • detectRegions

        public static void detectRegions​(Mat image,
                                 ERFilter er_filter1,
                                 ERFilter er_filter2,
                                 MatOfRect groups_rects,
                                 int method)
        Extracts text regions from image.
        Parameters:
        image - Source image where text blocks needs to be extracted from. Should be CV_8UC3 (color).
        er_filter1 - Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12
        er_filter2 - Extremal Region Filter for the 2nd stage classifier of N&M algorithm CITE: Neumann12
        groups_rects - Output list of rectangle blocks with text
        method - Grouping method (see text::erGrouping_Modes). Can be one of ERGROUPING_ORIENTATION_HORIZ, ERGROUPING_ORIENTATION_ANY.

      • detectRegions

        public static void detectRegions​(Mat image,
                                 ERFilter er_filter1,
                                 ERFilter er_filter2,
                                 MatOfRect groups_rects)
        Extracts text regions from image.
        Parameters:
        image - Source image where text blocks needs to be extracted from. Should be CV_8UC3 (color).
        er_filter1 - Extremal Region Filter for the 1st stage classifier of N&M algorithm CITE: Neumann12
        er_filter2 - Extremal Region Filter for the 2nd stage classifier of N&M algorithm CITE: Neumann12
        groups_rects - Output list of rectangle blocks with text