Class cv::text::OCRHMMDecoder#

OCRHMMDecoder class provides an interface for OCR using Hidden Markov Models. View details

#include <opencv2/text/ocr.hpp>

Collaboration diagram for cv::text::OCRHMMDecoder:

cv::text::OCRHMMDecoder Node1 cv::text::OCRHMMDecoder   + run() + run() + run() + run() + create() + create() Node2 cv::text::BaseOCR   + ~BaseOCR() + run() + run() Node2->Node1 Node3 Ptr< cv::text::OCRHMMDecoder ::ClassifierCallback >     Node3->Node1 #classifier Node4 std::string     Node4->Node1 #vocabulary Node5 std::basic_string< Char >     Node5->Node4 Node6 cv::Mat   + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() and 152 more... + diag() + eye() + eye() + getDefaultAllocator() + getStdAllocator() + ones() + ones() + ones() + ones() + setDefaultAllocator() + zeros() + zeros() + zeros() + zeros() # forEach_impl() Node6->Node1 #emission_p #transition_p Node7 int     Node7->Node6 +cols +dims +dummy +flags +rows Node8 uint8_t     Node8->Node6 +data +dataend +datalimit +datastart Node9 cv::MatAllocator   + MatAllocator() + ~MatAllocator() + allocate() + allocate() + copy() + deallocate() + download() + getBufferPoolController() + map() + unmap() + upload() Node9->Node6 +allocator Node10 UMatData *     Node10->Node6 +u Node11 MatSize     Node11->Node6 +size Node12 MatStep     Node12->Node6 +step Node13 decoder_mode     Node13->Node1 #mode

cv::text::OCRHMMDecoder Node1 cv::text::OCRHMMDecoder   + run() + run() + run() + run() + create() + create() Node2 cv::text::BaseOCR   + ~BaseOCR() + run() + run() Node2->Node1 Node3 Ptr< cv::text::OCRHMMDecoder ::ClassifierCallback >     Node3->Node1 #classifier Node4 std::string     Node4->Node1 #vocabulary Node5 std::basic_string< Char >     Node5->Node4 Node6 cv::Mat   + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() + Mat() and 152 more... + diag() + eye() + eye() + getDefaultAllocator() + getStdAllocator() + ones() + ones() + ones() + ones() + setDefaultAllocator() + zeros() + zeros() + zeros() + zeros() # forEach_impl() Node6->Node1 #emission_p #transition_p Node7 int     Node7->Node6 +cols +dims +dummy +flags +rows Node8 uint8_t     Node8->Node6 +data +dataend +datalimit +datastart Node9 cv::MatAllocator   + MatAllocator() + ~MatAllocator() + allocate() + allocate() + copy() + deallocate() + download() + getBufferPoolController() + map() + unmap() + upload() Node9->Node6 +allocator Node10 UMatData *     Node10->Node6 +u Node11 MatSize     Node11->Node6 +size Node12 MatStep     Node12->Node6 +step Node13 decoder_mode     Node13->Node1 #mode

Public Member Functions#

Public Member Functions inherited from cv::text::BaseOCR

Return

Name

Description

~BaseOCR()

void

run(
    Mat & image,
    Mat & mask,
    std::string & output_text,
    std::vector< Rect > * component_rects = NULL,
    std::vector< std::string > * component_texts = NULL,
    std::vector< float > * component_confidences = NULL,
    int component_level = 0 )

void

run(
    Mat & image,
    std::string & output_text,
    std::vector< Rect > * component_rects = NULL,
    std::vector< std::string > * component_texts = NULL,
    std::vector< float > * component_confidences = NULL,
    int component_level = 0 )

Detailed Description#

OCRHMMDecoder class provides an interface for OCR using Hidden Markov Models.

Note

Member Function Documentation#

run()#

String cv::text::OCRHMMDecoder::run(
InputArray image,
InputArray mask,
int min_confidence,
int component_level = 0 )

Python:

cv.text.OCRHMMDecoder.run(image, min_confidence[, component_level]) -> retval

cv.text.OCRHMMDecoder.run(image, mask, min_confidence[, component_level]) -> retval

run()#

String cv::text::OCRHMMDecoder::run(
InputArray image,
int min_confidence,
int component_level = 0 )

Python:

cv.text.OCRHMMDecoder.run(image, min_confidence[, component_level]) -> retval

cv.text.OCRHMMDecoder.run(image, mask, min_confidence[, component_level]) -> retval

run()#

void cv::text::OCRHMMDecoder::run(
Mat & image,
Mat & mask,
std::string & output_text,
std::vector< Rect > * component_rects = NULL,
std::vector< std::string > * component_texts = NULL,
std::vector< float > * component_confidences = NULL,
int component_level = 0 )

Python:

cv.text.OCRHMMDecoder.run(image, min_confidence[, component_level]) -> retval

cv.text.OCRHMMDecoder.run(image, mask, min_confidence[, component_level]) -> retval

Recognize text using HMM.

Takes an image and a mask (where each connected component corresponds to a segmented character) on input and returns recognized text in the output_text parameter. Optionally provides also the Rects for individual text elements found (e.g. words), and the list of those text elements with their confidence values.

Parameters

  • image — Input image CV_8UC1 or CV_8UC3 with a single text line (or word).

  • mask — Input binary image CV_8UC1 same size as input image. Each connected component in mask corresponds to a segmented character in the input image.

  • output_text — Output text. Most likely character sequence found by the HMM decoder.

  • component_rects — If provided the method will output a list of Rects for the individual text elements found (e.g. words).

  • component_texts — If provided the method will output a list of text strings for the recognition of individual text elements found (e.g. words).

  • component_confidences — If provided the method will output a list of confidence values for the recognition of individual text elements found (e.g. words).

  • component_level — Only OCR_LEVEL_WORD is supported.

run()#

void cv::text::OCRHMMDecoder::run(
Mat & image,
std::string & output_text,
std::vector< Rect > * component_rects = NULL,
std::vector< std::string > * component_texts = NULL,
std::vector< float > * component_confidences = NULL,
int component_level = 0 )

Python:

cv.text.OCRHMMDecoder.run(image, min_confidence[, component_level]) -> retval

cv.text.OCRHMMDecoder.run(image, mask, min_confidence[, component_level]) -> retval

Recognize text using HMM.

Takes binary image on input and returns recognized text in the output_text parameter. Optionally provides also the Rects for individual text elements found (e.g. words), and the list of those text elements with their confidence values.

Parameters

  • image — Input binary image CV_8UC1 with a single text line (or word).

  • output_text — Output text. Most likely character sequence found by the HMM decoder.

  • component_rects — If provided the method will output a list of Rects for the individual text elements found (e.g. words).

  • component_texts — If provided the method will output a list of text strings for the recognition of individual text elements found (e.g. words).

  • component_confidences — If provided the method will output a list of confidence values for the recognition of individual text elements found (e.g. words).

  • component_level — Only OCR_LEVEL_WORD is supported.

create()#

static Ptr< OCRHMMDecoder > cv::text::OCRHMMDecoder::create(
const Ptr< OCRHMMDecoder::ClassifierCallback > classifier,
const String & vocabulary,
InputArray transition_probabilities_table,
InputArray emission_probabilities_table,
int mode = OCR_DECODER_VITERBI )

Python:

cv.text.OCRHMMDecoder.create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode]) -> retval

cv.text.OCRHMMDecoder.create(filename, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, classifier]]) -> retval

cv.text.OCRHMMDecoder_create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode]) -> retval

cv.text.OCRHMMDecoder_create(filename, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, classifier]]) -> retval

Creates an instance of the OCRHMMDecoder class. Initializes HMMDecoder.

Parameters

  • classifier — The character classifier with built in feature extractor.

  • vocabulary — The language vocabulary (chars when ascii english text). vocabulary.size() must be equal to the number of classes of the classifier.

  • transition_probabilities_table — Table with transition probabilities between character pairs. cols == rows == vocabulary.size().

  • emission_probabilities_table — Table with observation emission probabilities. cols == rows == vocabulary.size().

  • mode — HMM Decoding algorithm. Only OCR_DECODER_VITERBI is available for the moment (http://en.wikipedia.org/wiki/Viterbi_algorithm).

create()#

static Ptr< OCRHMMDecoder > cv::text::OCRHMMDecoder::create(
const String & filename,
const String & vocabulary,
InputArray transition_probabilities_table,
InputArray emission_probabilities_table,
int mode = OCR_DECODER_VITERBI,
int classifier = OCR_KNN_CLASSIFIER )

Python:

cv.text.OCRHMMDecoder.create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode]) -> retval

cv.text.OCRHMMDecoder.create(filename, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, classifier]]) -> retval

cv.text.OCRHMMDecoder_create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode]) -> retval

cv.text.OCRHMMDecoder_create(filename, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, classifier]]) -> retval

Creates an instance of the OCRHMMDecoder class. Loads and initializes HMMDecoder from the specified path.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Member Data Documentation#

classifier#

Ptr< OCRHMMDecoder::ClassifierCallback > cv::text::OCRHMMDecoder::classifier

emission_p#

Mat cv::text::OCRHMMDecoder::emission_p

mode#

decoder_mode cv::text::OCRHMMDecoder::mode

transition_p#

Mat cv::text::OCRHMMDecoder::transition_p

vocabulary#

std::string cv::text::OCRHMMDecoder::vocabulary

Source file#

The documentation for this class was generated from the following file: