View Source Evision.Text.OCRBeamSearchDecoder (Evision v0.1.28)

Link to this section Summary

Types

t()

Type that represents an Text.OCRBeamSearchDecoder struct.

Functions

Creates an instance of the OCRBeamSearchDecoder class. Initializes HMMDecoder.

Creates an instance of the OCRBeamSearchDecoder class. Initializes HMMDecoder.

Recognize text using Beam Search.

Link to this section Types

@type t() :: %Evision.Text.OCRBeamSearchDecoder{ref: reference()}

Type that represents an Text.OCRBeamSearchDecoder struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

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create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table)

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@spec create(
  Evision.Text.OCRBeamSearchDecoder.ClassifierCallback.t(),
  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.t().

    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.t().

    Table with transition probabilities between character pairs. cols == rows == vocabulary.size().

  • emission_probabilities_table: Evision.Mat.t().

    Table with observation emission probabilities. cols == rows == vocabulary.size().

Keyword Arguments
Return
  • retval: OCRBeamSearchDecoder

Python prototype (for reference only):

create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, beam_size]]) -> retval
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create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table, opts)

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@spec create(
  Evision.Text.OCRBeamSearchDecoder.ClassifierCallback.t(),
  binary(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) :: t() | {:error, String.t()}

Creates an instance of the OCRBeamSearchDecoder class. Initializes HMMDecoder.

Positional Arguments
  • classifier: Evision.Text.OCRBeamSearchDecoder.ClassifierCallback.t().

    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.t().

    Table with transition probabilities between character pairs. cols == rows == vocabulary.size().

  • emission_probabilities_table: Evision.Mat.t().

    Table with observation emission probabilities. cols == rows == vocabulary.size().

Keyword Arguments
Return
  • retval: OCRBeamSearchDecoder

Python prototype (for reference only):

create(classifier, vocabulary, transition_probabilities_table, emission_probabilities_table[, mode[, beam_size]]) -> retval
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run(self, image, min_confidence)

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@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.t().

    Input binary image CV_8UC1 with a single text line (or word).

  • min_confidence: int

Keyword Arguments
  • component_level: int.

    Only OCR_LEVEL_WORD is supported.

Return

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
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run(self, image, min_confidence, opts)

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@spec run(t(), Evision.Mat.maybe_mat_in(), integer(), [{atom(), 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.t()
  • mask: Evision.Mat.t()
  • min_confidence: int
Keyword Arguments
  • component_level: int.
Return

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.t().

    Input binary image CV_8UC1 with a single text line (or word).

  • min_confidence: int

Keyword Arguments
  • component_level: int.

    Only OCR_LEVEL_WORD is supported.

Return

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
Link to this function

run(self, image, mask, min_confidence, opts)

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@spec run(
  t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  integer(),
  [{atom(), term()}, ...] | nil
) :: binary() | {:error, String.t()}

run

Positional Arguments
  • self: Evision.Text.OCRBeamSearchDecoder.t()
  • image: Evision.Mat.t()
  • mask: Evision.Mat.t()
  • min_confidence: int
Keyword Arguments
  • component_level: int.
Return

Python prototype (for reference only):

run(image, mask, min_confidence[, component_level]) -> retval