Class cv::text::OCRBeamSearchDecoder#
OCRBeamSearchDecoder class provides an interface for OCR using Beam Search algorithm. View details
#include <opencv2/text/ocr.hpp>Collaboration diagram for cv::text::OCRBeamSearchDecoder:
Public Member Functions#
Public Member Functions inherited from cv::text::BaseOCR
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Detailed Description#
OCRBeamSearchDecoder class provides an interface for OCR using Beam Search algorithm.
Note
(C++) An example on using OCRBeamSearchDecoder recognition combined with scene text detection can be found at the demo sample: opencv/opencv_contrib
Member Function Documentation#
run()#
String cv::text::OCRBeamSearchDecoder::run(
InputArray image,
InputArray mask,
int min_confidence,
int component_level = 0 )
Python:
cv.text.OCRBeamSearchDecoder.run(image, min_confidence[, component_level]) -> retval
cv.text.OCRBeamSearchDecoder.run(image, mask, min_confidence[, component_level]) -> retval
run()#
String cv::text::OCRBeamSearchDecoder::run(
InputArray image,
int min_confidence,
int component_level = 0 )
Python:
cv.text.OCRBeamSearchDecoder.run(image, min_confidence[, component_level]) -> retval
cv.text.OCRBeamSearchDecoder.run(image, mask, min_confidence[, component_level]) -> retval
run()#
void cv::text::OCRBeamSearchDecoder::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.OCRBeamSearchDecoder.run(image, min_confidence[, component_level]) -> retval
cv.text.OCRBeamSearchDecoder.run(image, mask, min_confidence[, component_level]) -> retval
run()#
void cv::text::OCRBeamSearchDecoder::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.OCRBeamSearchDecoder.run(image, min_confidence[, component_level]) -> retval
cv.text.OCRBeamSearchDecoder.run(image, mask, min_confidence[, component_level]) -> retval
Recognize text using Beam Search.
Takes 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< OCRBeamSearchDecoder > cv::text::OCRBeamSearchDecoder::create(
const Ptr< OCRBeamSearchDecoder::ClassifierCallback > classifier,
const std::string & vocabulary,
InputArray transition_probabilities_table,
InputArray emission_probabilities_table,
text::decoder_mode mode = OCR_DECODER_VITERBI,
int beam_size = 500 )
Python:
cv.text.OCRBeamSearchDecoder.create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, beam_size]]) -> retval
cv.text.OCRBeamSearchDecoder_create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, beam_size]]) -> retval
Creates an instance of the OCRBeamSearchDecoder 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).beam_size— Size of the beam in Beam Search algorithm.
create()#
static Ptr< OCRBeamSearchDecoder > cv::text::OCRBeamSearchDecoder::create(
const String & filename,
const String & vocabulary,
InputArray transition_probabilities_table,
InputArray emission_probabilities_table,
text::decoder_mode mode = OCR_DECODER_VITERBI,
int beam_size = 500 )
Python:
cv.text.OCRBeamSearchDecoder.create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, beam_size]]) -> retval
cv.text.OCRBeamSearchDecoder_create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, beam_size]]) -> retval
Creates an instance of the OCRBeamSearchDecoder class. 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#
beam_size#
int cv::text::OCRBeamSearchDecoder::beam_size
classifier#
Ptr< OCRBeamSearchDecoder::ClassifierCallback > cv::text::OCRBeamSearchDecoder::classifier
emission_p#
Mat cv::text::OCRBeamSearchDecoder::emission_p
mode#
decoder_mode cv::text::OCRBeamSearchDecoder::mode
transition_p#
Mat cv::text::OCRBeamSearchDecoder::transition_p
vocabulary#
std::string cv::text::OCRBeamSearchDecoder::vocabulary
Source file#
The documentation for this class was generated from the following file:
opencv2/text/ocr.hpp