View Source Evision.DNN.TextRecognitionModel (Evision v0.1.15)
Link to this section Summary
Types
Type that represents an Evision.DNN.TextRecognitionModel
struct.
Functions
Get the decoding method
Get the vocabulary for recognition.
Given the @p input frame, create input blob, run net and return recognition result
Given the @p input frame, create input blob, run net and return recognition result
Set the decoding method options for "CTC-prefix-beam-search"
decode usage
Set the decoding method options for "CTC-prefix-beam-search"
decode usage
Set the decoding method of translating the network output into string
Set the vocabulary for recognition.
Variant 1:
Create text recognition model from network represented in one of the supported formats Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method
Create text recognition model from network represented in one of the supported formats Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method
Link to this section Types
@type t() :: %Evision.DNN.TextRecognitionModel{ref: reference()}
Type that represents an Evision.DNN.TextRecognitionModel
struct.
ref.
reference()
The underlying erlang resource variable.
Link to this section Functions
Get the decoding method
Return
- retval:
String
@return the decoding method
Python prototype (for reference):
getDecodeType() -> retval
Get the vocabulary for recognition.
Return
- retval:
std::vector<std::string>
@return vocabulary the associated vocabulary
Python prototype (for reference):
getVocabulary() -> retval
@spec recognize(t(), Evision.Mat.maybe_mat_in()) :: binary() | {:error, String.t()}
Given the @p input frame, create input blob, run net and return recognition result
Positional Arguments
frame:
Evision.Mat
.The input image
Return
- retval:
String
@return The text recognition result
Python prototype (for reference):
recognize(frame) -> retval
@spec recognize(t(), Evision.Mat.maybe_mat_in(), [Evision.Mat.maybe_mat_in()]) :: [binary()] | {:error, String.t()}
Given the @p input frame, create input blob, run net and return recognition result
Positional Arguments
frame:
Evision.Mat
.The input image
roiRects:
[Evision.Mat]
.List of text detection regions of interest (cv::Rect, CV_32SC4). ROIs is be cropped as the network inputs
Return
results:
[string]
.A set of text recognition results.
Python prototype (for reference):
recognize(frame, roiRects) -> results
Set the decoding method options for "CTC-prefix-beam-search"
decode usage
Positional Arguments
beamSize:
int
.Beam size for search
Keyword Arguments
vocPruneSize:
int
.Parameter to optimize big vocabulary search, only take top @p vocPruneSize tokens in each search step, @p vocPruneSize <= 0 stands for disable this prune.
Return
- retval:
Evision.DNN.TextRecognitionModel
Python prototype (for reference):
setDecodeOptsCTCPrefixBeamSearch(beamSize[, vocPruneSize]) -> retval
@spec setDecodeOptsCTCPrefixBeamSearch(t(), integer(), [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}
Set the decoding method options for "CTC-prefix-beam-search"
decode usage
Positional Arguments
beamSize:
int
.Beam size for search
Keyword Arguments
vocPruneSize:
int
.Parameter to optimize big vocabulary search, only take top @p vocPruneSize tokens in each search step, @p vocPruneSize <= 0 stands for disable this prune.
Return
- retval:
Evision.DNN.TextRecognitionModel
Python prototype (for reference):
setDecodeOptsCTCPrefixBeamSearch(beamSize[, vocPruneSize]) -> retval
Set the decoding method of translating the network output into string
Positional Arguments
- decodeType:
string
.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
Return
- retval:
Evision.DNN.TextRecognitionModel
Python prototype (for reference):
setDecodeType(decodeType) -> retval
Set the vocabulary for recognition.
Positional Arguments
vocabulary:
[string]
.the associated vocabulary of the network.
Return
- retval:
Evision.DNN.TextRecognitionModel
Python prototype (for reference):
setVocabulary(vocabulary) -> retval
@spec textRecognitionModel(binary()) :: t() | {:error, String.t()}
@spec textRecognitionModel(Evision.DNN.Net.t()) :: t() | {:error, String.t()}
Variant 1:
Create text recognition model from network represented in one of the supported formats Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method
Positional Arguments
model:
string
.Binary file contains trained weights
Keyword Arguments
config:
string
.Text file contains network configuration
Return
Python prototype (for reference):
TextRecognitionModel(model[, config]) -> <dnn_TextRecognitionModel object>
Variant 2:
Create Text Recognition model from deep learning network Call setDecodeType() and setVocabulary() after constructor to initialize the decoding method
Positional Arguments
network:
Evision.DNN.Net
.Net object
Return
Python prototype (for reference):
TextRecognitionModel(network) -> <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
Positional Arguments
model:
string
.Binary file contains trained weights
Keyword Arguments
config:
string
.Text file contains network configuration
Return
Python prototype (for reference):
TextRecognitionModel(model[, config]) -> <dnn_TextRecognitionModel object>