Class cv::dnn::TextRecognitionModel#

This class represents high-level API for text recognition networks. View details

#include <opencv2/dnn/dnn.hpp>

Collaboration diagram for cv::dnn::TextRecognitionModel:

Public Member Functions#

Public Member Functions inherited from cv::dnn::Model

Return	Name	Description
	`Model()`
	`Model(const Model &)`
	`Model(const Net & network)`	Create model from deep learning network.
	`Model(` `CV_WRAP_FILE_PATH const String & model,` `CV_WRAP_FILE_PATH const String & config = "" )`	Create model from deep learning network represented in one of the supported formats. An order of model and config arguments does not matter.
	`Model(Model &&)`
`Model &`	`enableWinograd(bool useWinograd)`
`Impl *`	`getImpl()`
`Impl &`	`getImplRef()`
`Net &`	`getNetwork_()`
`Net &`	`getNetwork_()`
	`operator Net &()`
`Model &`	`operator=(const Model &)`
`Model &`	`operator=(Model &&)`
`void`	`predict(` `InputArray frame,` `OutputArrayOfArrays outs )`	Given the input frame, create input blob, run net and return the output blobs.
`Model &`	`setInputCrop(bool crop)`	Set flag crop for frame.
`Model &`	`setInputMean(const Scalar & mean)`	Set mean value for frame.
`void`	`setInputParams(` `double scale = 1.0,` `const Size & size = Size(),` `const Scalar & mean = Scalar(),` `bool swapRB = false,` `bool crop = false )`	Set preprocessing parameters for frame.
`Model &`	`setInputScale(const Scalar & scale)`	Set scalefactor value for frame.
`Model &`	`setInputSize(const Size & size)`	Set input size for frame.
`Model &`	`setInputSize(` `int width,` `int height )`
`Model &`	`setInputSwapRB(bool swapRB)`	Set flag swapRB for frame.
`Model &`	`setOutputNames(const std::vector< String > & outNames)`	Set output names for frame.
`Model &`	`setPreferableBackend(dnn::Backend backendId)`
`Model &`	`setPreferableTarget(dnn::Target targetId)`

Additional Inherited Members#

Detailed Description#

This class represents high-level API for text recognition networks.

TextRecognitionModel allows to set params for preprocessing input image. TextRecognitionModel creates net from file with trained weights and config, sets preprocessing input, runs forward pass and return recognition result. For TextRecognitionModel, CRNN-CTC is supported.

Examples: samples/dnn/text_detection.cpp.

Constructor & Destructor Documentation#

TextRecognitionModel()#

cv::dnn::TextRecognitionModel::TextRecognitionModel()

Python:

cv.dnn.TextRecognitionModel(network) -> <dnn_TextRecognitionModel object>

cv.dnn.TextRecognitionModel(model[, config]) -> <dnn_TextRecognitionModel object>

TextRecognitionModel()#

cv::dnn::TextRecognitionModel::TextRecognitionModel(const Net & network)

Python:

cv.dnn.TextRecognitionModel(network) -> <dnn_TextRecognitionModel object>

cv.dnn.TextRecognitionModel(model[, config]) -> <dnn_TextRecognitionModel object>

Create Text Recognition model from deep learning network Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method.

Parameters

network — Net object

TextRecognitionModel()#

cv::dnn::TextRecognitionModel::TextRecognitionModel(
CV_WRAP_FILE_PATH const std::string & model,
CV_WRAP_FILE_PATH const std::string & config = “” )

Python:

cv.dnn.TextRecognitionModel(network) -> <dnn_TextRecognitionModel object>

cv.dnn.TextRecognitionModel(model[, config]) -> <dnn_TextRecognitionModel object>

Create text recognition model from network represented in one of the supported formats Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method.

Parameters

model — Binary file contains trained weights
config — Text file contains network configuration

Here is the call graph for this function:

Member Function Documentation#

getDecodeType()#

const std::string & cv::dnn::TextRecognitionModel::getDecodeType()

Python:

cv.dnn.TextRecognitionModel.getDecodeType() -> retval

Get the decoding method.

Returns

the decoding method

getVocabulary()#

const std::vector< std::string > & cv::dnn::TextRecognitionModel::getVocabulary()

Python:

cv.dnn.TextRecognitionModel.getVocabulary() -> retval

Get the vocabulary for recognition.

Returns

vocabulary the associated vocabulary

recognize()#

std::string cv::dnn::TextRecognitionModel::recognize(InputArray frame)

Python:

cv.dnn.TextRecognitionModel.recognize(frame) -> retval

cv.dnn.TextRecognitionModel.recognize(frame, roiRects) -> results

Given the input frame, create input blob, run net and return recognition result.

Parameters

frame — The input image

Returns

The text recognition result

recognize()#

void cv::dnn::TextRecognitionModel::recognize(
InputArray frame,
InputArrayOfArrays roiRects,
std::vector< std::string > & results )

Python:

cv.dnn.TextRecognitionModel.recognize(frame) -> retval

cv.dnn.TextRecognitionModel.recognize(frame, roiRects) -> results

Given the input frame, create input blob, run net and return recognition result.

Parameters

frame — The input image
roiRects — List of text detection regions of interest (cv::Rect, CV_32SC4). ROIs is be cropped as the network inputs
results — A set of text recognition results.

setDecodeOptsCTCPrefixBeamSearch()#

TextRecognitionModel & cv::dnn::TextRecognitionModel::setDecodeOptsCTCPrefixBeamSearch(
int beamSize,
int vocPruneSize = 0 )

Python:

cv.dnn.TextRecognitionModel.setDecodeOptsCTCPrefixBeamSearch(beamSize[, vocPruneSize]) -> retval

Set the decoding method options for “CTC-prefix-beam-search” decode usage.

Parameters

beamSize — Beam size for search
vocPruneSize — Parameter to optimize big vocabulary search, only take top vocPruneSize tokens in each search step, vocPruneSize <= 0 stands for disable this prune.

setDecodeType()#

TextRecognitionModel & cv::dnn::TextRecognitionModel::setDecodeType(const std::string & decodeType)

Python:

cv.dnn.TextRecognitionModel.setDecodeType(decodeType) -> retval

Set the decoding method of translating the network output into string.

Parameters

decodeType — The decoding method of translating the network output into string, currently supported type:
- "CTC-greedy" greedy decoding for the output of CTC-based methods
- "CTC-prefix-beam-search" Prefix beam search decoding for the output of CTC-based methods

setVocabulary()#

TextRecognitionModel & cv::dnn::TextRecognitionModel::setVocabulary(const std::vector< std::string > & vocabulary)

Python:

cv.dnn.TextRecognitionModel.setVocabulary(vocabulary) -> retval

Set the vocabulary for recognition.

Parameters

vocabulary — the associated vocabulary of the network.

Source file#

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

opencv2/dnn/dnn.hpp