aws

# WARNING: DO NOT EDIT, AUTO-GENERATED CODE!
# See https://github.com/aws-beam/aws-codegen for more details.

defmodule AWS.Textract do
  @moduledoc """
  Amazon Textract detects and analyzes text in documents and converts it into
  machine-readable text.

  This is the API reference documentation for Amazon Textract.
  """

  alias AWS.Client
  alias AWS.Request

  def metadata do
    %AWS.ServiceMetadata{
      abbreviation: nil,
      api_version: "2018-06-27",
      content_type: "application/x-amz-json-1.1",
      credential_scope: nil,
      endpoint_prefix: "textract",
      global?: false,
      protocol: "json",
      service_id: "Textract",
      signature_version: "v4",
      signing_name: "textract",
      target_prefix: "Textract"
    }
  end

  @doc """
  Analyzes an input document for relationships between detected items.

  The types of information returned are as follows:

    * Form data (key-value pairs). The related information is returned
  in two `Block` objects, each of type `KEY_VALUE_SET`: a KEY `Block` object and a
  VALUE `Block` object. For example, *Name: Ana Silva Carolina* contains a key and
  value. *Name:* is the key. *Ana Silva Carolina* is the value.

    * Table and table cell data. A TABLE `Block` object contains
  information about a detected table. A CELL `Block` object is returned for each
  cell in a table.

    * Lines and words of text. A LINE `Block` object contains one or
  more WORD `Block` objects. All lines and words that are detected in the document
  are returned (including text that doesn't have a relationship with the value of
  `FeatureTypes`).

  Selection elements such as check boxes and option buttons (radio buttons) can be
  detected in form data and in tables. A SELECTION_ELEMENT `Block` object contains
  information about a selection element, including the selection status.

  You can choose which type of analysis to perform by specifying the
  `FeatureTypes` list.

  The output is returned in a list of `Block` objects.

  `AnalyzeDocument` is a synchronous operation. To analyze documents
  asynchronously, use `StartDocumentAnalysis`.

  For more information, see [Document Text Analysis](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html).
  """
  def analyze_document(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "AnalyzeDocument", input, options)
  end

  @doc """
  Detects text in the input document.

  Amazon Textract can detect lines of text and the words that make up a line of
  text. The input document must be an image in JPEG or PNG format.
  `DetectDocumentText` returns the detected text in an array of `Block` objects.

  Each document page has as an associated `Block` of type PAGE. Each PAGE `Block`
  object is the parent of LINE `Block` objects that represent the lines of
  detected text on a page. A LINE `Block` object is a parent for each word that
  makes up the line. Words are represented by `Block` objects of type WORD.

  `DetectDocumentText` is a synchronous operation. To analyze documents
  asynchronously, use `StartDocumentTextDetection`.

  For more information, see [Document Text Detection](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html).
  """
  def detect_document_text(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "DetectDocumentText", input, options)
  end

  @doc """
  Gets the results for an Amazon Textract asynchronous operation that analyzes
  text in a document.

  You start asynchronous text analysis by calling `StartDocumentAnalysis`, which
  returns a job identifier (`JobId`). When the text analysis operation finishes,
  Amazon Textract publishes a completion status to the Amazon Simple Notification
  Service (Amazon SNS) topic that's registered in the initial call to
  `StartDocumentAnalysis`. To get the results of the text-detection operation,
  first check that the status value published to the Amazon SNS topic is
  `SUCCEEDED`. If so, call `GetDocumentAnalysis`, and pass the job identifier
  (`JobId`) from the initial call to `StartDocumentAnalysis`.

  `GetDocumentAnalysis` returns an array of `Block` objects. The following types
  of information are returned:

    * Form data (key-value pairs). The related information is returned
  in two `Block` objects, each of type `KEY_VALUE_SET`: a KEY `Block` object and a
  VALUE `Block` object. For example, *Name: Ana Silva Carolina* contains a key and
  value. *Name:* is the key. *Ana Silva Carolina* is the value.

    * Table and table cell data. A TABLE `Block` object contains
  information about a detected table. A CELL `Block` object is returned for each
  cell in a table.

    * Lines and words of text. A LINE `Block` object contains one or
  more WORD `Block` objects. All lines and words that are detected in the document
  are returned (including text that doesn't have a relationship with the value of
  the `StartDocumentAnalysis` `FeatureTypes` input parameter).

  Selection elements such as check boxes and option buttons (radio buttons) can be
  detected in form data and in tables. A SELECTION_ELEMENT `Block` object contains
  information about a selection element, including the selection status.

  Use the `MaxResults` parameter to limit the number of blocks that are returned.
  If there are more results than specified in `MaxResults`, the value of
  `NextToken` in the operation response contains a pagination token for getting
  the next set of results. To get the next page of results, call
  `GetDocumentAnalysis`, and populate the `NextToken` request parameter with the
  token value that's returned from the previous call to `GetDocumentAnalysis`.

  For more information, see [Document Text Analysis](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html).
  """
  def get_document_analysis(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "GetDocumentAnalysis", input, options)
  end

  @doc """
  Gets the results for an Amazon Textract asynchronous operation that detects text
  in a document.

  Amazon Textract can detect lines of text and the words that make up a line of
  text.

  You start asynchronous text detection by calling `StartDocumentTextDetection`,
  which returns a job identifier (`JobId`). When the text detection operation
  finishes, Amazon Textract publishes a completion status to the Amazon Simple
  Notification Service (Amazon SNS) topic that's registered in the initial call to
  `StartDocumentTextDetection`. To get the results of the text-detection
  operation, first check that the status value published to the Amazon SNS topic
  is `SUCCEEDED`. If so, call `GetDocumentTextDetection`, and pass the job
  identifier (`JobId`) from the initial call to `StartDocumentTextDetection`.

  `GetDocumentTextDetection` returns an array of `Block` objects.

  Each document page has as an associated `Block` of type PAGE. Each PAGE `Block`
  object is the parent of LINE `Block` objects that represent the lines of
  detected text on a page. A LINE `Block` object is a parent for each word that
  makes up the line. Words are represented by `Block` objects of type WORD.

  Use the MaxResults parameter to limit the number of blocks that are returned. If
  there are more results than specified in `MaxResults`, the value of `NextToken`
  in the operation response contains a pagination token for getting the next set
  of results. To get the next page of results, call `GetDocumentTextDetection`,
  and populate the `NextToken` request parameter with the token value that's
  returned from the previous call to `GetDocumentTextDetection`.

  For more information, see [Document Text Detection](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html).
  """
  def get_document_text_detection(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "GetDocumentTextDetection", input, options)
  end

  @doc """
  Starts the asynchronous analysis of an input document for relationships between
  detected items such as key-value pairs, tables, and selection elements.

  `StartDocumentAnalysis` can analyze text in documents that are in JPEG, PNG, and
  PDF format. The documents are stored in an Amazon S3 bucket. Use
  `DocumentLocation` to specify the bucket name and file name of the document.

  `StartDocumentAnalysis` returns a job identifier (`JobId`) that you use to get
  the results of the operation. When text analysis is finished, Amazon Textract
  publishes a completion status to the Amazon Simple Notification Service (Amazon
  SNS) topic that you specify in `NotificationChannel`. To get the results of the
  text analysis operation, first check that the status value published to the
  Amazon SNS topic is `SUCCEEDED`. If so, call `GetDocumentAnalysis`, and pass the
  job identifier (`JobId`) from the initial call to `StartDocumentAnalysis`.

  For more information, see [Document Text Analysis](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html).
  """
  def start_document_analysis(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "StartDocumentAnalysis", input, options)
  end

  @doc """
  Starts the asynchronous detection of text in a document.

  Amazon Textract can detect lines of text and the words that make up a line of
  text.

  `StartDocumentTextDetection` can analyze text in documents that are in JPEG,
  PNG, and PDF format. The documents are stored in an Amazon S3 bucket. Use
  `DocumentLocation` to specify the bucket name and file name of the document.

  `StartTextDetection` returns a job identifier (`JobId`) that you use to get the
  results of the operation. When text detection is finished, Amazon Textract
  publishes a completion status to the Amazon Simple Notification Service (Amazon
  SNS) topic that you specify in `NotificationChannel`. To get the results of the
  text detection operation, first check that the status value published to the
  Amazon SNS topic is `SUCCEEDED`. If so, call `GetDocumentTextDetection`, and
  pass the job identifier (`JobId`) from the initial call to
  `StartDocumentTextDetection`.

  For more information, see [Document Text Detection](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html).
  """
  def start_document_text_detection(%Client{} = client, input, options \\ []) do
    Request.request_post(client, metadata(), "StartDocumentTextDetection", input, options)
  end
end