View Source Evision.Text.OCRBeamSearchDecoder (Evision v0.2.9)
Summary
Functions
Creates an instance of the OCRBeamSearchDecoder class. Initializes HMMDecoder.
Creates an instance of the OCRBeamSearchDecoder class. Initializes HMMDecoder.
Recognize text using Beam Search.
Variant 1:
run
Types
@type t() :: %Evision.Text.OCRBeamSearchDecoder{ref: reference()}
Type that represents an Text.OCRBeamSearchDecoder
struct.
ref.
reference()
The underlying erlang resource variable.
Functions
create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table)
View Source@spec create(term(), binary(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: t() | {:error, String.t()}
Creates an instance of the OCRBeamSearchDecoder class. Initializes HMMDecoder.
Positional Arguments
classifier:
Evision.Text.OCRBeamSearchDecoder.ClassifierCallback
.The character classifier with built in feature extractor.
vocabulary:
string
.The language vocabulary (chars when ASCII English text). vocabulary.size() must be equal to the number of classes of the classifier.
transition_probabilities_table:
Evision.Mat
.Table with transition probabilities between character pairs. cols == rows == vocabulary.size().
emission_probabilities_table:
Evision.Mat
.Table with observation emission probabilities. cols == rows == vocabulary.size().
Keyword Arguments
mode:
text_decoder_mode
.HMM Decoding algorithm. Only OCR_DECODER_VITERBI is available for the moment (http://en.wikipedia.org/wiki/Viterbi_algorithm).
beam_size:
integer()
.Size of the beam in Beam Search algorithm.
Return
- retval:
OCRBeamSearchDecoder
Python prototype (for reference only):
create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, beam_size]]) -> retval
create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table, opts)
View Source@spec create( term(), binary(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), [beam_size: term(), mode: term()] | nil ) :: t() | {:error, String.t()}
Creates an instance of the OCRBeamSearchDecoder class. Initializes HMMDecoder.
Positional Arguments
classifier:
Evision.Text.OCRBeamSearchDecoder.ClassifierCallback
.The character classifier with built in feature extractor.
vocabulary:
string
.The language vocabulary (chars when ASCII English text). vocabulary.size() must be equal to the number of classes of the classifier.
transition_probabilities_table:
Evision.Mat
.Table with transition probabilities between character pairs. cols == rows == vocabulary.size().
emission_probabilities_table:
Evision.Mat
.Table with observation emission probabilities. cols == rows == vocabulary.size().
Keyword Arguments
mode:
text_decoder_mode
.HMM Decoding algorithm. Only OCR_DECODER_VITERBI is available for the moment (http://en.wikipedia.org/wiki/Viterbi_algorithm).
beam_size:
integer()
.Size of the beam in Beam Search algorithm.
Return
- retval:
OCRBeamSearchDecoder
Python prototype (for reference only):
create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, beam_size]]) -> retval
@spec run(t(), Evision.Mat.maybe_mat_in(), integer()) :: binary() | {:error, String.t()}
Recognize text using Beam Search.
Positional Arguments
self:
Evision.Text.OCRBeamSearchDecoder.t()
image:
Evision.Mat
.Input binary image CV_8UC1 with a single text line (or word).
min_confidence:
integer()
Keyword Arguments
component_level:
integer()
.Only OCR_LEVEL_WORD is supported.
Return
- retval:
String
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.
Python prototype (for reference only):
run(image, min_confidence[, component_level]) -> retval
@spec run( t(), Evision.Mat.maybe_mat_in(), integer(), [{:component_level, term()}] | nil ) :: binary() | {:error, String.t()}
@spec run(t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer()) :: binary() | {:error, String.t()}
Variant 1:
run
Positional Arguments
- self:
Evision.Text.OCRBeamSearchDecoder.t()
- image:
Evision.Mat
- mask:
Evision.Mat
- min_confidence:
integer()
Keyword Arguments
- component_level:
integer()
.
Return
- retval:
String
Python prototype (for reference only):
run(image, mask, min_confidence[, component_level]) -> retval
Variant 2:
Recognize text using Beam Search.
Positional Arguments
self:
Evision.Text.OCRBeamSearchDecoder.t()
image:
Evision.Mat
.Input binary image CV_8UC1 with a single text line (or word).
min_confidence:
integer()
Keyword Arguments
component_level:
integer()
.Only OCR_LEVEL_WORD is supported.
Return
- retval:
String
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.
Python prototype (for reference only):
run(image, min_confidence[, component_level]) -> retval
@spec run( t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer(), [{:component_level, term()}] | nil ) :: binary() | {:error, String.t()}
run
Positional Arguments
- self:
Evision.Text.OCRBeamSearchDecoder.t()
- image:
Evision.Mat
- mask:
Evision.Mat
- min_confidence:
integer()
Keyword Arguments
- component_level:
integer()
.
Return
- retval:
String
Python prototype (for reference only):
run(image, mask, min_confidence[, component_level]) -> retval