View Source Evision.DNN.TextRecognitionModel (Evision v0.1.9)
Link to this section Summary
Functions
Raising version of dnn_TextRecognitionModel/1
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Raising version of dnn_TextRecognitionModel/2
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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
Raising version of getDecodeType/1
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Get the decoding method
Raising version of getVocabulary/1
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Get the vocabulary for recognition.
Raising version of recognize/2
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Raising version of recognize/3
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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
Raising version of setDecodeOptsCTCPrefixBeamSearch/2
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Raising version of setDecodeOptsCTCPrefixBeamSearch/3
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Set the decoding method options for "CTC-prefix-beam-search"
decode usage
Set the decoding method options for "CTC-prefix-beam-search"
decode usage
Raising version of setDecodeType/2
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Set the decoding method of translating the network output into string
Raising version of setVocabulary/2
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Set the vocabulary for recognition.
Link to this section Functions
Raising version of dnn_TextRecognitionModel/1
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Raising version of dnn_TextRecognitionModel/2
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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
Python prototype (for reference):
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
Positional Arguments
model:
string
.Binary file contains trained weights
Keyword Arguments
config:
string
.Text file contains network configuration
Python prototype (for reference):
TextRecognitionModel(model[, config]) -> <dnn_TextRecognitionModel object>
Raising version of getDecodeType/1
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Get the decoding method
@return the decoding method
Python prototype (for reference):
getDecodeType() -> retval
Raising version of getVocabulary/1
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Get the vocabulary for recognition.
@return vocabulary the associated vocabulary
Python prototype (for reference):
getVocabulary() -> retval
Raising version of recognize/2
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Raising version of recognize/3
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Given the @p input frame, create input blob, run net and return recognition result
Positional Arguments
frame:
Evision.Mat
.The input image
@return The text recognition result
Python prototype (for reference):
recognize(frame) -> retval
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
Raising version of setDecodeOptsCTCPrefixBeamSearch/2
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Raising version of setDecodeOptsCTCPrefixBeamSearch/3
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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.
Python prototype (for reference):
setDecodeOptsCTCPrefixBeamSearch(beamSize[, vocPruneSize]) -> retval
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.
Python prototype (for reference):
setDecodeOptsCTCPrefixBeamSearch(beamSize[, vocPruneSize]) -> retval
Raising version of setDecodeType/2
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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
Python prototype (for reference):
setDecodeType(decodeType) -> retval
Raising version of setVocabulary/2
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Set the vocabulary for recognition.
Positional Arguments
vocabulary:
[string]
.the associated vocabulary of the network.
Python prototype (for reference):
setVocabulary(vocabulary) -> retval