cv::text Namespace Reference

Classes
class	BaseOCR
class	ERFilter
	Base class for 1st and 2nd stages of Neumann and Matas scene text detection algorithm [184]. : More...
struct	ERStat
	The ERStat structure represents a class-specific Extremal Region (ER). More...
class	OCRBeamSearchDecoder
	OCRBeamSearchDecoder class provides an interface for OCR using Beam Search algorithm. More...
class	OCRHMMDecoder
	OCRHMMDecoder class provides an interface for OCR using Hidden Markov Models. More...
class	OCRHolisticWordRecognizer
	OCRHolisticWordRecognizer class provides the functionallity of segmented wordspotting. Given a predefined vocabulary , a DictNet is employed to select the most probable word given an input image. More...
class	OCRTesseract
	OCRTesseract class provides an interface with the tesseract-ocr API (v3.02.02) in C++. More...
class	TextDetector
	An abstract class providing interface for text detection algorithms. More...
class	TextDetectorCNN
	TextDetectorCNN class provides the functionallity of text bounding box detection. This class is representing to find bounding boxes of text words given an input image. This class uses OpenCV dnn module to load pre-trained model described in [147]. The original repository with the modified SSD Caffe version: https://github.com/MhLiao/TextBoxes. Model can be downloaded from DropBox. Modified .prototxt file with the model description can be found in opencv_contrib/modules/text/samples/textbox.prototxt. More...

Enumerations
enum	{   ERFILTER_NM_RGBLGrad,   ERFILTER_NM_IHSGrad }
	computeNMChannels operation modes More...
enum	{   OCR_LEVEL_WORD,   OCR_LEVEL_TEXTLINE }
enum	classifier_type {   OCR_KNN_CLASSIFIER = 0,   OCR_CNN_CLASSIFIER = 1 }
enum	decoder_mode { OCR_DECODER_VITERBI = 0 }
enum	erGrouping_Modes {   ERGROUPING_ORIENTATION_HORIZ,   ERGROUPING_ORIENTATION_ANY }
	text::erGrouping operation modes More...
enum	ocr_engine_mode {   OEM_TESSERACT_ONLY,   OEM_CUBE_ONLY,   OEM_TESSERACT_CUBE_COMBINED,   OEM_DEFAULT }
	Tesseract.OcrEngineMode Enumeration. More...
enum	page_seg_mode {   PSM_OSD_ONLY,   PSM_AUTO_OSD,   PSM_AUTO_ONLY,   PSM_AUTO,   PSM_SINGLE_COLUMN,   PSM_SINGLE_BLOCK_VERT_TEXT,   PSM_SINGLE_BLOCK,   PSM_SINGLE_LINE,   PSM_SINGLE_WORD,   PSM_CIRCLE_WORD,   PSM_SINGLE_CHAR }
	Tesseract.PageSegMode Enumeration. More...

Functions
void	computeNMChannels (InputArray _src, OutputArrayOfArrays _channels, int _mode=ERFILTER_NM_RGBLGrad)
	Compute the different channels to be processed independently in the N&M algorithm [184]. More...
Ptr< ERFilter >	createERFilterNM1 (const Ptr< ERFilter::Callback > &cb, int thresholdDelta=1, float minArea=(float) 0.00025, float maxArea=(float) 0.13, float minProbability=(float) 0.4, bool nonMaxSuppression=true, float minProbabilityDiff=(float) 0.1)
	Create an Extremal Region Filter for the 1st stage classifier of N&M algorithm [184]. More...
Ptr< ERFilter >	createERFilterNM1 (const String &filename, int thresholdDelta=1, float minArea=(float) 0.00025, float maxArea=(float) 0.13, float minProbability=(float) 0.4, bool nonMaxSuppression=true, float minProbabilityDiff=(float) 0.1)
	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. More...
Ptr< ERFilter >	createERFilterNM2 (const Ptr< ERFilter::Callback > &cb, float minProbability=(float) 0.3)
	Create an Extremal Region Filter for the 2nd stage classifier of N&M algorithm [184]. More...
Ptr< ERFilter >	createERFilterNM2 (const String &filename, float minProbability=(float) 0.3)
	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. More...
void	createOCRHMMTransitionsTable (std::string &vocabulary, std::vector< std::string > &lexicon, OutputArray transition_probabilities_table)
	Utility function to create a tailored language model transitions table from a given list of words (lexicon). More...
Mat	createOCRHMMTransitionsTable (const String &vocabulary, std::vector< cv::String > &lexicon)
void	detectRegions (InputArray image, const Ptr< ERFilter > &er_filter1, const Ptr< ERFilter > &er_filter2, std::vector< std::vector< Point > > &regions)
void	detectRegions (InputArray image, const Ptr< ERFilter > &er_filter1, const Ptr< ERFilter > &er_filter2, std::vector< Rect > &groups_rects, int method=ERGROUPING_ORIENTATION_HORIZ, const String &filename=String(), float minProbability=(float) 0.5)
	Extracts text regions from image. More...
void	detectTextSWT (InputArray input, std::vector< cv::Rect > &result, bool dark_on_light, OutputArray &draw=noArray(), OutputArray &chainBBs=noArray())
	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. More...
void	erGrouping (InputArray img, InputArrayOfArrays channels, std::vector< std::vector< ERStat > > &regions, std::vector< std::vector< Vec2i > > &groups, std::vector< Rect > &groups_rects, int method=ERGROUPING_ORIENTATION_HORIZ, const std::string &filename=std::string(), float minProbablity=0.5)
	Find groups of Extremal Regions that are organized as text blocks. More...
void	erGrouping (InputArray image, InputArray channel, std::vector< std::vector< Point > > regions, std::vector< Rect > &groups_rects, int method=ERGROUPING_ORIENTATION_HORIZ, const String &filename=String(), float minProbablity=(float) 0.5)
Ptr< ERFilter::Callback >	loadClassifierNM1 (const String &filename)
	Allow to implicitly load the default classifier when creating an ERFilter object. More...
Ptr< ERFilter::Callback >	loadClassifierNM2 (const String &filename)
	Allow to implicitly load the default classifier when creating an ERFilter object. More...
Ptr< OCRBeamSearchDecoder::ClassifierCallback >	loadOCRBeamSearchClassifierCNN (const String &filename)
	Allow to implicitly load the default character classifier when creating an OCRBeamSearchDecoder object. More...
Ptr< OCRHMMDecoder::ClassifierCallback >	loadOCRHMMClassifier (const String &filename, int classifier)
	Allow to implicitly load the default character classifier when creating an OCRHMMDecoder object. More...
Ptr< OCRHMMDecoder::ClassifierCallback >	loadOCRHMMClassifierCNN (const String &filename)
	Allow to implicitly load the default character classifier when creating an OCRHMMDecoder object. More...
Ptr< OCRHMMDecoder::ClassifierCallback >	loadOCRHMMClassifierNM (const String &filename)
	Allow to implicitly load the default character classifier when creating an OCRHMMDecoder object. More...
void	MSERsToERStats (InputArray image, std::vector< std::vector< Point > > &contours, std::vector< std::vector< ERStat > > &regions)
	Converts MSER contours (vector<Point>) to ERStat regions. More...

Function Documentation

createOCRHMMTransitionsTable() [1/2]

void cv::text::createOCRHMMTransitionsTable	(	std::string &	vocabulary,
		std::vector< std::string > &	lexicon,
		OutputArray	transition_probabilities_table
	)

Python:
	cv.text.createOCRHMMTransitionsTable(	vocabulary, lexicon	) ->	retval

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.
transition_probabilities_table	Output table with transition probabilities between character pairs. cols == rows == vocabulary.size().

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

createOCRHMMTransitionsTable() [2/2]

Mat cv::text::createOCRHMMTransitionsTable	(	const String &	vocabulary,
		std::vector< cv::String > &	lexicon
	)

Python:
	cv.text.createOCRHMMTransitionsTable(	vocabulary, lexicon	) ->	retval

detectTextSWT()

void cv::text::detectTextSWT	(	InputArray	input,
		std::vector< cv::Rect > &	result,
		bool	dark_on_light,
		OutputArray &	draw = noArray(),
		OutputArray &	chainBBs = noArray()
	)

Python:
	cv.text.detectTextSWT(	input, dark_on_light[, draw[, chainBBs]]	) ->	result, draw, 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.

loadOCRBeamSearchClassifierCNN()

Ptr<OCRBeamSearchDecoder::ClassifierCallback> cv::text::loadOCRBeamSearchClassifierCNN

(

const String &

filename

)

Python:
	cv.text.loadOCRBeamSearchClassifierCNN(	filename	) ->	retval

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.