Class: Aws::Textract::Client

Inherits:
Seahorse::Client::Base
  • Object
show all
Includes:
ClientStubs
Defined in:
lib/aws-sdk-textract/client.rb

Overview

An API client for Textract. To construct a client, you need to configure a ‘:region` and `:credentials`.

client = Aws::Textract::Client.new(
  region: region_name,
  credentials: credentials,
  # ...
)

For details on configuring region and credentials see the [developer guide](/sdk-for-ruby/v3/developer-guide/setup-config.html).

See #initialize for a full list of supported configuration options.

Class Attribute Summary collapse

API Operations collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(options) ⇒ Client

Returns a new instance of Client.

Parameters:

  • options (Hash)

Options Hash (options):

  • :credentials (required, Aws::CredentialProvider) —

    Your AWS credentials. This can be an instance of any one of the following classes:

    • ‘Aws::Credentials` - Used for configuring static, non-refreshing credentials.

    • ‘Aws::SharedCredentials` - Used for loading static credentials from a shared file, such as `~/.aws/config`.

    • ‘Aws::AssumeRoleCredentials` - Used when you need to assume a role.

    • ‘Aws::AssumeRoleWebIdentityCredentials` - Used when you need to assume a role after providing credentials via the web.

    • ‘Aws::SSOCredentials` - Used for loading credentials from AWS SSO using an access token generated from `aws login`.

    • ‘Aws::ProcessCredentials` - Used for loading credentials from a process that outputs to stdout.

    • ‘Aws::InstanceProfileCredentials` - Used for loading credentials from an EC2 IMDS on an EC2 instance.

    • ‘Aws::ECSCredentials` - Used for loading credentials from instances running in ECS.

    • ‘Aws::CognitoIdentityCredentials` - Used for loading credentials from the Cognito Identity service.

    When ‘:credentials` are not configured directly, the following locations will be searched for credentials:

    • ‘Aws.config`

    • The ‘:access_key_id`, `:secret_access_key`, and `:session_token` options.

    • ENV, ENV

    • ‘~/.aws/credentials`

    • ‘~/.aws/config`

    • EC2/ECS IMDS instance profile - When used by default, the timeouts are very aggressive. Construct and pass an instance of ‘Aws::InstanceProfileCredentails` or `Aws::ECSCredentials` to enable retries and extended timeouts.

  • :region (required, String) —

    The AWS region to connect to. The configured ‘:region` is used to determine the service `:endpoint`. When not passed, a default `:region` is searched for in the following locations:

  • :access_key_id (String)
  • :active_endpoint_cache (Boolean) — default: false —

    When set to ‘true`, a thread polling for endpoints will be running in the background every 60 secs (default). Defaults to `false`.

  • :adaptive_retry_wait_to_fill (Boolean) — default: true —

    Used only in ‘adaptive` retry mode. When true, the request will sleep until there is sufficent client side capacity to retry the request. When false, the request will raise a `RetryCapacityNotAvailableError` and will not retry instead of sleeping.

  • :client_side_monitoring (Boolean) — default: false —

    When ‘true`, client-side metrics will be collected for all API requests from this client.

  • :client_side_monitoring_client_id (String) — default: "" —

    Allows you to provide an identifier for this client which will be attached to all generated client side metrics. Defaults to an empty string.

  • :client_side_monitoring_host (String) — default: "127.0.0.1" —

    Allows you to specify the DNS hostname or IPv4 or IPv6 address that the client side monitoring agent is running on, where client metrics will be published via UDP.

  • :client_side_monitoring_port (Integer) — default: 31000 —

    Required for publishing client metrics. The port that the client side monitoring agent is running on, where client metrics will be published via UDP.

  • :client_side_monitoring_publisher (Aws::ClientSideMonitoring::Publisher) — default: Aws::ClientSideMonitoring::Publisher —

    Allows you to provide a custom client-side monitoring publisher class. By default, will use the Client Side Monitoring Agent Publisher.

  • :convert_params (Boolean) — default: true —

    When ‘true`, an attempt is made to coerce request parameters into the required types.

  • :correct_clock_skew (Boolean) — default: true —

    Used only in ‘standard` and adaptive retry modes. Specifies whether to apply a clock skew correction and retry requests with skewed client clocks.

  • :disable_host_prefix_injection (Boolean) — default: false —

    Set to true to disable SDK automatically adding host prefix to default service endpoint when available.

  • :endpoint (String) —

    The client endpoint is normally constructed from the ‘:region` option. You should only configure an `:endpoint` when connecting to test or custom endpoints. This should be a valid HTTP(S) URI.

  • :endpoint_cache_max_entries (Integer) — default: 1000 —

    Used for the maximum size limit of the LRU cache storing endpoints data for endpoint discovery enabled operations. Defaults to 1000.

  • :endpoint_cache_max_threads (Integer) — default: 10 —

    Used for the maximum threads in use for polling endpoints to be cached, defaults to 10.

  • :endpoint_cache_poll_interval (Integer) — default: 60 —

    When :endpoint_discovery and :active_endpoint_cache is enabled, Use this option to config the time interval in seconds for making requests fetching endpoints information. Defaults to 60 sec.

  • :endpoint_discovery (Boolean) — default: false —

    When set to ‘true`, endpoint discovery will be enabled for operations when available.

  • :log_formatter (Aws::Log::Formatter) — default: Aws::Log::Formatter.default —

    The log formatter.

  • :log_level (Symbol) — default: :info —

    The log level to send messages to the ‘:logger` at.

  • :logger (Logger) —

    The Logger instance to send log messages to. If this option is not set, logging will be disabled.

  • :max_attempts (Integer) — default: 3 —

    An integer representing the maximum number attempts that will be made for a single request, including the initial attempt. For example, setting this value to 5 will result in a request being retried up to 4 times. Used in ‘standard` and `adaptive` retry modes.

  • :profile (String) — default: "default" —

    Used when loading credentials from the shared credentials file at HOME/.aws/credentials. When not specified, ‘default’ is used.

  • :retry_backoff (Proc) —

    A proc or lambda used for backoff. Defaults to 2**retries * retry_base_delay. This option is only used in the ‘legacy` retry mode.

  • :retry_base_delay (Float) — default: 0.3 —

    The base delay in seconds used by the default backoff function. This option is only used in the ‘legacy` retry mode.

  • :retry_jitter (Symbol) — default: :none —

    A delay randomiser function used by the default backoff function. Some predefined functions can be referenced by name - :none, :equal, :full, otherwise a Proc that takes and returns a number. This option is only used in the ‘legacy` retry mode.

    @see www.awsarchitectureblog.com/2015/03/backoff.html

  • :retry_limit (Integer) — default: 3 —

    The maximum number of times to retry failed requests. Only ~ 500 level server errors and certain ~ 400 level client errors are retried. Generally, these are throttling errors, data checksum errors, networking errors, timeout errors, auth errors, endpoint discovery, and errors from expired credentials. This option is only used in the ‘legacy` retry mode.

  • :retry_max_delay (Integer) — default: 0 —

    The maximum number of seconds to delay between retries (0 for no limit) used by the default backoff function. This option is only used in the ‘legacy` retry mode.

  • :retry_mode (String) — default: "legacy" —

    Specifies which retry algorithm to use. Values are:

    • ‘legacy` - The pre-existing retry behavior. This is default value if no retry mode is provided.

    • ‘standard` - A standardized set of retry rules across the AWS SDKs. This includes support for retry quotas, which limit the number of unsuccessful retries a client can make.

    • ‘adaptive` - An experimental retry mode that includes all the functionality of `standard` mode along with automatic client side throttling. This is a provisional mode that may change behavior in the future.

  • :secret_access_key (String)
  • :session_token (String)
  • :simple_json (Boolean) — default: false —

    Disables request parameter conversion, validation, and formatting. Also disable response data type conversions. This option is useful when you want to ensure the highest level of performance by avoiding overhead of walking request parameters and response data structures.

    When ‘:simple_json` is enabled, the request parameters hash must be formatted exactly as the DynamoDB API expects.

  • :stub_responses (Boolean) — default: false —

    Causes the client to return stubbed responses. By default fake responses are generated and returned. You can specify the response data to return or errors to raise by calling ClientStubs#stub_responses. See ClientStubs for more information.

    ** Please note ** When response stubbing is enabled, no HTTP requests are made, and retries are disabled.

  • :use_dualstack_endpoint (Boolean) —

    When set to ‘true`, dualstack enabled endpoints (with `.aws` TLD) will be used if available.

  • :use_fips_endpoint (Boolean) —

    When set to ‘true`, fips compatible endpoints will be used if available. When a `fips` region is used, the region is normalized and this config is set to `true`.

  • :validate_params (Boolean) — default: true —

    When ‘true`, request parameters are validated before sending the request.

  • :http_proxy (URI::HTTP, String) —

    A proxy to send requests through. Formatted like ‘proxy.com:123’.

  • :http_open_timeout (Float) — default: 15 —

    The number of seconds to wait when opening a HTTP session before raising a ‘Timeout::Error`.

  • :http_read_timeout (Integer) — default: 60 —

    The default number of seconds to wait for response data. This value can safely be set per-request on the session.

  • :http_idle_timeout (Float) — default: 5 —

    The number of seconds a connection is allowed to sit idle before it is considered stale. Stale connections are closed and removed from the pool before making a request.

  • :http_continue_timeout (Float) — default: 1 —

    The number of seconds to wait for a 100-continue response before sending the request body. This option has no effect unless the request has “Expect” header set to “100-continue”. Defaults to ‘nil` which disables this behaviour. This value can safely be set per request on the session.

  • :http_wire_trace (Boolean) — default: false —

    When ‘true`, HTTP debug output will be sent to the `:logger`.

  • :ssl_verify_peer (Boolean) — default: true —

    When ‘true`, SSL peer certificates are verified when establishing a connection.

  • :ssl_ca_bundle (String) —

    Full path to the SSL certificate authority bundle file that should be used when verifying peer certificates. If you do not pass ‘:ssl_ca_bundle` or `:ssl_ca_directory` the the system default will be used if available.

  • :ssl_ca_directory (String) —

    Full path of the directory that contains the unbundled SSL certificate authority files for verifying peer certificates. If you do not pass ‘:ssl_ca_bundle` or `:ssl_ca_directory` the the system default will be used if available.



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# File 'lib/aws-sdk-textract/client.rb', line 343

def initialize(*args)
  super
end

Class Attribute Details

.identifier ⇒ Object (readonly)

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future. 



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# File 'lib/aws-sdk-textract/client.rb', line 1461

def identifier
  @identifier
end

Class Method Details

.errors_module ⇒ Object

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future. 



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# File 'lib/aws-sdk-textract/client.rb', line 1464

def errors_module
  Errors
end

Instance Method Details

#analyze_document(params = {}) ⇒ Types::AnalyzeDocumentResponse

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].

[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html

Examples:

Request syntax with placeholder values


resp = client.analyze_document({
  document: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  feature_types: ["TABLES"], # required, accepts TABLES, FORMS
  human_loop_config: {
    human_loop_name: "HumanLoopName", # required
    flow_definition_arn: "FlowDefinitionArn", # required
    data_attributes: {
      content_classifiers: ["FreeOfPersonallyIdentifiableInformation"], # accepts FreeOfPersonallyIdentifiableInformation, FreeOfAdultContent
    },
  },
})

Response structure


resp.document_metadata.pages #=> Integer
resp.blocks #=> Array
resp.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT"
resp.blocks[0].confidence #=> Float
resp.blocks[0].text #=> String
resp.blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED"
resp.blocks[0].row_index #=> Integer
resp.blocks[0].column_index #=> Integer
resp.blocks[0].row_span #=> Integer
resp.blocks[0].column_span #=> Integer
resp.blocks[0].geometry.bounding_box.width #=> Float
resp.blocks[0].geometry.bounding_box.height #=> Float
resp.blocks[0].geometry.bounding_box.left #=> Float
resp.blocks[0].geometry.bounding_box.top #=> Float
resp.blocks[0].geometry.polygon #=> Array
resp.blocks[0].geometry.polygon[0].x #=> Float
resp.blocks[0].geometry.polygon[0].y #=> Float
resp.blocks[0].id #=> String
resp.blocks[0].relationships #=> Array
resp.blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES"
resp.blocks[0].relationships[0].ids #=> Array
resp.blocks[0].relationships[0].ids[0] #=> String
resp.blocks[0].entity_types #=> Array
resp.blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE"
resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.blocks[0].page #=> Integer
resp.human_loop_activation_output.human_loop_arn #=> String
resp.human_loop_activation_output.human_loop_activation_reasons #=> Array
resp.human_loop_activation_output.human_loop_activation_reasons[0] #=> String
resp.human_loop_activation_output.human_loop_activation_conditions_evaluation_results #=> String
resp.analyze_document_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :document (required, Types::Document) —

    The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can’t pass image bytes. The document must be an image in JPEG or PNG format.

    If you’re using an AWS SDK to call Amazon Textract, you might not need to base64-encode image bytes that are passed using the ‘Bytes` field.

  • :feature_types (required, Array<String>) —

    A list of the types of analysis to perform. Add TABLES to the list to return information about the tables that are detected in the input document. Add FORMS to return detected form data. To perform both types of analysis, add TABLES and FORMS to ‘FeatureTypes`. All lines and words detected in the document are included in the response (including text that isn’t related to the value of ‘FeatureTypes`).

  • :human_loop_config (Types::HumanLoopConfig) —

    Sets the configuration for the human in the loop workflow for analyzing documents.

Returns:

See Also:



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# File 'lib/aws-sdk-textract/client.rb', line 474

def analyze_document(params = {}, options = {})
  req = build_request(:analyze_document, params)
  req.send_request(options)
end

#analyze_expense(params = {}) ⇒ Types::AnalyzeExpenseResponse

‘AnalyzeExpense` synchronously analyzes an input document for financially related relationships between text.

Information is returned as ‘ExpenseDocuments` and seperated as follows.

  • ‘LineItemGroups`- A data set containing `LineItems` which store information about the lines of text, such as an item purchased and its price on a receipt.

  • ‘SummaryFields`- Contains all other information a receipt, such as header information or the vendors name.

Examples:

Request syntax with placeholder values


resp = client.analyze_expense({
  document: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
})

Response structure


resp.document_metadata.pages #=> Integer
resp.expense_documents #=> Array
resp.expense_documents[0].expense_index #=> Integer
resp.expense_documents[0].summary_fields #=> Array
resp.expense_documents[0].summary_fields[0].type.text #=> String
resp.expense_documents[0].summary_fields[0].type.confidence #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.text #=> String
resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.width #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.height #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.left #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.top #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.geometry.polygon #=> Array
resp.expense_documents[0].summary_fields[0].label_detection.geometry.polygon[0].x #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.geometry.polygon[0].y #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.confidence #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.text #=> String
resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.width #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.height #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.left #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.top #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.geometry.polygon #=> Array
resp.expense_documents[0].summary_fields[0].value_detection.geometry.polygon[0].x #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.geometry.polygon[0].y #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.confidence #=> Float
resp.expense_documents[0].summary_fields[0].page_number #=> Integer
resp.expense_documents[0].line_item_groups #=> Array
resp.expense_documents[0].line_item_groups[0].line_item_group_index #=> Integer
resp.expense_documents[0].line_item_groups[0].line_items #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].type.text #=> String
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].type.confidence #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.text #=> String
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.width #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.height #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.left #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.top #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon[0].x #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon[0].y #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.confidence #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.text #=> String
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.width #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.height #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.left #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.top #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon[0].x #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon[0].y #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.confidence #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].page_number #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :document (required, Types::Document) —

    The input document, either as bytes or as an S3 object.

    You pass image bytes to an Amazon Textract API operation by using the ‘Bytes` property. For example, you would use the `Bytes` property to pass a document loaded from a local file system. Image bytes passed by using the `Bytes` property must be base64 encoded. Your code might not need to encode document file bytes if you’re using an AWS SDK to call Amazon Textract API operations.

    You pass images stored in an S3 bucket to an Amazon Textract API operation by using the ‘S3Object` property. Documents stored in an S3 bucket don’t need to be base64 encoded.

    The AWS Region for the S3 bucket that contains the S3 object must match the AWS Region that you use for Amazon Textract operations.

    If you use the AWS CLI to call Amazon Textract operations, passing image bytes using the Bytes property isn’t supported. You must first upload the document to an Amazon S3 bucket, and then call the operation using the S3Object property.

    For Amazon Textract to process an S3 object, the user must have permission to access the S3 object.

Returns:

See Also:



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# File 'lib/aws-sdk-textract/client.rb', line 592

def analyze_expense(params = {}, options = {})
  req = build_request(:analyze_expense, params)
  req.send_request(options)
end

#analyze_id(params = {}) ⇒ Types::AnalyzeIDResponse

Analyzes identity documents for relevant information. This information is extracted and returned as ‘IdentityDocumentFields`, which records both the normalized field and value of the extracted text.

Examples:

Request syntax with placeholder values


resp = client.analyze_id({
  document_pages: [ # required
    {
      bytes: "data",
      s3_object: {
        bucket: "S3Bucket",
        name: "S3ObjectName",
        version: "S3ObjectVersion",
      },
    },
  ],
})

Response structure


resp.identity_documents #=> Array
resp.identity_documents[0].document_index #=> Integer
resp.identity_documents[0].identity_document_fields #=> Array
resp.identity_documents[0].identity_document_fields[0].type.text #=> String
resp.identity_documents[0].identity_document_fields[0].type.normalized_value.value #=> String
resp.identity_documents[0].identity_document_fields[0].type.normalized_value.value_type #=> String, one of "DATE"
resp.identity_documents[0].identity_document_fields[0].type.confidence #=> Float
resp.identity_documents[0].identity_document_fields[0].value_detection.text #=> String
resp.identity_documents[0].identity_document_fields[0].value_detection.normalized_value.value #=> String
resp.identity_documents[0].identity_document_fields[0].value_detection.normalized_value.value_type #=> String, one of "DATE"
resp.identity_documents[0].identity_document_fields[0].value_detection.confidence #=> Float
resp.document_metadata.pages #=> Integer
resp.analyze_id_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :document_pages (required, Array<Types::Document>) —

    The document being passed to AnalyzeID.

Returns:

See Also:



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# File 'lib/aws-sdk-textract/client.rb', line 645

def analyze_id(params = {}, options = {})
  req = build_request(:analyze_id, params)
  req.send_request(options)
end

#build_request(operation_name, params = {}) ⇒ Object

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.

Parameters:

  • params ({}) (defaults to: {}) 


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# File 'lib/aws-sdk-textract/client.rb', line 1439

def build_request(operation_name, params = {})
  handlers = @handlers.for(operation_name)
  context = Seahorse::Client::RequestContext.new(
    operation_name: operation_name,
    operation: config.api.operation(operation_name),
    client: self,
    params: params,
    config: config)
  context[:gem_name] = 'aws-sdk-textract'
  context[:gem_version] = '1.32.0'
  Seahorse::Client::Request.new(handlers, context)
end

#detect_document_text(params = {}) ⇒ Types::DetectDocumentTextResponse

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].

[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html

Examples:

Request syntax with placeholder values


resp = client.detect_document_text({
  document: { # required
    bytes: "data",
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
})

Response structure


resp.document_metadata.pages #=> Integer
resp.blocks #=> Array
resp.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT"
resp.blocks[0].confidence #=> Float
resp.blocks[0].text #=> String
resp.blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED"
resp.blocks[0].row_index #=> Integer
resp.blocks[0].column_index #=> Integer
resp.blocks[0].row_span #=> Integer
resp.blocks[0].column_span #=> Integer
resp.blocks[0].geometry.bounding_box.width #=> Float
resp.blocks[0].geometry.bounding_box.height #=> Float
resp.blocks[0].geometry.bounding_box.left #=> Float
resp.blocks[0].geometry.bounding_box.top #=> Float
resp.blocks[0].geometry.polygon #=> Array
resp.blocks[0].geometry.polygon[0].x #=> Float
resp.blocks[0].geometry.polygon[0].y #=> Float
resp.blocks[0].id #=> String
resp.blocks[0].relationships #=> Array
resp.blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES"
resp.blocks[0].relationships[0].ids #=> Array
resp.blocks[0].relationships[0].ids[0] #=> String
resp.blocks[0].entity_types #=> Array
resp.blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE"
resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.blocks[0].page #=> Integer
resp.detect_document_text_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :document (required, Types::Document) —

    The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can’t pass image bytes. The document must be an image in JPEG or PNG format.

    If you’re using an AWS SDK to call Amazon Textract, you might not need to base64-encode image bytes that are passed using the ‘Bytes` field.

Returns:

See Also:



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# File 'lib/aws-sdk-textract/client.rb', line 732

def detect_document_text(params = {}, options = {})
  req = build_request(:detect_document_text, params)
  req.send_request(options)
end

#get_document_analysis(params = {}) ⇒ Types::GetDocumentAnalysisResponse

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].

[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html

Examples:

Request syntax with placeholder values


resp = client.get_document_analysis({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
})

Response structure


resp.document_metadata.pages #=> Integer
resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED", "PARTIAL_SUCCESS"
resp.next_token #=> String
resp.blocks #=> Array
resp.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT"
resp.blocks[0].confidence #=> Float
resp.blocks[0].text #=> String
resp.blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED"
resp.blocks[0].row_index #=> Integer
resp.blocks[0].column_index #=> Integer
resp.blocks[0].row_span #=> Integer
resp.blocks[0].column_span #=> Integer
resp.blocks[0].geometry.bounding_box.width #=> Float
resp.blocks[0].geometry.bounding_box.height #=> Float
resp.blocks[0].geometry.bounding_box.left #=> Float
resp.blocks[0].geometry.bounding_box.top #=> Float
resp.blocks[0].geometry.polygon #=> Array
resp.blocks[0].geometry.polygon[0].x #=> Float
resp.blocks[0].geometry.polygon[0].y #=> Float
resp.blocks[0].id #=> String
resp.blocks[0].relationships #=> Array
resp.blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES"
resp.blocks[0].relationships[0].ids #=> Array
resp.blocks[0].relationships[0].ids[0] #=> String
resp.blocks[0].entity_types #=> Array
resp.blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE"
resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.blocks[0].page #=> Integer
resp.warnings #=> Array
resp.warnings[0].error_code #=> String
resp.warnings[0].pages #=> Array
resp.warnings[0].pages[0] #=> Integer
resp.status_message #=> String
resp.analyze_document_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_id (required, String) —

    A unique identifier for the text-detection job. The ‘JobId` is returned from `StartDocumentAnalysis`. A `JobId` value is only valid for 7 days.

  • :max_results (Integer) —

    The maximum number of results to return per paginated call. The largest value that you can specify is 1,000. If you specify a value greater than 1,000, a maximum of 1,000 results is returned. The default value is 1,000.

  • :next_token (String) —

    If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.

Returns:

See Also:



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# File 'lib/aws-sdk-textract/client.rb', line 864

def get_document_analysis(params = {}, options = {})
  req = build_request(:get_document_analysis, params)
  req.send_request(options)
end

#get_document_text_detection(params = {}) ⇒ Types::GetDocumentTextDetectionResponse

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].

[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html

Examples:

Request syntax with placeholder values


resp = client.get_document_text_detection({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
})

Response structure


resp.document_metadata.pages #=> Integer
resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED", "PARTIAL_SUCCESS"
resp.next_token #=> String
resp.blocks #=> Array
resp.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT"
resp.blocks[0].confidence #=> Float
resp.blocks[0].text #=> String
resp.blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED"
resp.blocks[0].row_index #=> Integer
resp.blocks[0].column_index #=> Integer
resp.blocks[0].row_span #=> Integer
resp.blocks[0].column_span #=> Integer
resp.blocks[0].geometry.bounding_box.width #=> Float
resp.blocks[0].geometry.bounding_box.height #=> Float
resp.blocks[0].geometry.bounding_box.left #=> Float
resp.blocks[0].geometry.bounding_box.top #=> Float
resp.blocks[0].geometry.polygon #=> Array
resp.blocks[0].geometry.polygon[0].x #=> Float
resp.blocks[0].geometry.polygon[0].y #=> Float
resp.blocks[0].id #=> String
resp.blocks[0].relationships #=> Array
resp.blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES"
resp.blocks[0].relationships[0].ids #=> Array
resp.blocks[0].relationships[0].ids[0] #=> String
resp.blocks[0].entity_types #=> Array
resp.blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE"
resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.blocks[0].page #=> Integer
resp.warnings #=> Array
resp.warnings[0].error_code #=> String
resp.warnings[0].pages #=> Array
resp.warnings[0].pages[0] #=> Integer
resp.status_message #=> String
resp.detect_document_text_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_id (required, String) —

    A unique identifier for the text detection job. The ‘JobId` is returned from `StartDocumentTextDetection`. A `JobId` value is only valid for 7 days.

  • :max_results (Integer) —

    The maximum number of results to return per paginated call. The largest value you can specify is 1,000. If you specify a value greater than 1,000, a maximum of 1,000 results is returned. The default value is 1,000.

  • :next_token (String) —

    If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.

Returns:

See Also:



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# File 'lib/aws-sdk-textract/client.rb', line 982

def get_document_text_detection(params = {}, options = {})
  req = build_request(:get_document_text_detection, params)
  req.send_request(options)
end

#get_expense_analysis(params = {}) ⇒ Types::GetExpenseAnalysisResponse

Gets the results for an Amazon Textract asynchronous operation that analyzes invoices and receipts. Amazon Textract finds contact information, items purchased, and vendor name, from input invoices and receipts.

You start asynchronous invoice/receipt analysis by calling StartExpenseAnalysis, which returns a job identifier (‘JobId`). Upon completion of the invoice/receipt analysis, Amazon Textract publishes the completion status to the Amazon Simple Notification Service (Amazon SNS) topic. This topic must be registered in the initial call to `StartExpenseAnalysis`. To get the results of the invoice/receipt analysis operation, first ensure that the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call `GetExpenseAnalysis`, and pass the job identifier (`JobId`) from the initial call to `StartExpenseAnalysis`.

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 `GetExpenseAnalysis`, and populate the `NextToken` request parameter with the token value that’s returned from the previous call to ‘GetExpenseAnalysis`.

For more information, see [Analyzing Invoices and Receipts].

[1]: docs.aws.amazon.com/textract/latest/dg/invoices-receipts.html

Examples:

Request syntax with placeholder values


resp = client.get_expense_analysis({
  job_id: "JobId", # required
  max_results: 1,
  next_token: "PaginationToken",
})

Response structure


resp.document_metadata.pages #=> Integer
resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED", "PARTIAL_SUCCESS"
resp.next_token #=> String
resp.expense_documents #=> Array
resp.expense_documents[0].expense_index #=> Integer
resp.expense_documents[0].summary_fields #=> Array
resp.expense_documents[0].summary_fields[0].type.text #=> String
resp.expense_documents[0].summary_fields[0].type.confidence #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.text #=> String
resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.width #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.height #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.left #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.top #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.geometry.polygon #=> Array
resp.expense_documents[0].summary_fields[0].label_detection.geometry.polygon[0].x #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.geometry.polygon[0].y #=> Float
resp.expense_documents[0].summary_fields[0].label_detection.confidence #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.text #=> String
resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.width #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.height #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.left #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.top #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.geometry.polygon #=> Array
resp.expense_documents[0].summary_fields[0].value_detection.geometry.polygon[0].x #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.geometry.polygon[0].y #=> Float
resp.expense_documents[0].summary_fields[0].value_detection.confidence #=> Float
resp.expense_documents[0].summary_fields[0].page_number #=> Integer
resp.expense_documents[0].line_item_groups #=> Array
resp.expense_documents[0].line_item_groups[0].line_item_group_index #=> Integer
resp.expense_documents[0].line_item_groups[0].line_items #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].type.text #=> String
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].type.confidence #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.text #=> String
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.width #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.height #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.left #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.top #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon[0].x #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon[0].y #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.confidence #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.text #=> String
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.width #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.height #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.left #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.top #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon[0].x #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon[0].y #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.confidence #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].page_number #=> Integer
resp.warnings #=> Array
resp.warnings[0].error_code #=> String
resp.warnings[0].pages #=> Array
resp.warnings[0].pages[0] #=> Integer
resp.status_message #=> String
resp.analyze_expense_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_id (required, String) —

    A unique identifier for the text detection job. The ‘JobId` is returned from `StartExpenseAnalysis`. A `JobId` value is only valid for 7 days.

  • :max_results (Integer) —

    The maximum number of results to return per paginated call. The largest value you can specify is 20. If you specify a value greater than 20, a maximum of 20 results is returned. The default value is 20.

  • :next_token (String) —

    If the previous response was incomplete (because there are more blocks to retrieve), Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of blocks.

Returns:

See Also:



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# File 'lib/aws-sdk-textract/client.rb', line 1116

def get_expense_analysis(params = {}, options = {})
  req = build_request(:get_expense_analysis, params)
  req.send_request(options)
end

#start_document_analysis(params = {}) ⇒ Types::StartDocumentAnalysisResponse

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, TIFF, 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].

[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html

Examples:

Request syntax with placeholder values


resp = client.start_document_analysis({
  document_location: { # required
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  feature_types: ["TABLES"], # required, accepts TABLES, FORMS
  client_request_token: "ClientRequestToken",
  job_tag: "JobTag",
  notification_channel: {
    sns_topic_arn: "SNSTopicArn", # required
    role_arn: "RoleArn", # required
  },
  output_config: {
    s3_bucket: "S3Bucket", # required
    s3_prefix: "S3ObjectName",
  },
  kms_key_id: "KMSKeyId",
})

Response structure


resp.job_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :document_location (required, Types::DocumentLocation) —

    The location of the document to be processed.

  • :feature_types (required, Array<String>) —

    A list of the types of analysis to perform. Add TABLES to the list to return information about the tables that are detected in the input document. Add FORMS to return detected form data. To perform both types of analysis, add TABLES and FORMS to ‘FeatureTypes`. All lines and words detected in the document are included in the response (including text that isn’t related to the value of ‘FeatureTypes`).

  • :client_request_token (String) —

    The idempotent token that you use to identify the start request. If you use the same token with multiple ‘StartDocumentAnalysis` requests, the same `JobId` is returned. Use `ClientRequestToken` to prevent the same job from being accidentally started more than once. For more information, see [Calling Amazon Textract Asynchronous Operations].

    [1]: docs.aws.amazon.com/textract/latest/dg/api-async.html

  • :job_tag (String) —

    An identifier that you specify that’s included in the completion notification published to the Amazon SNS topic. For example, you can use ‘JobTag` to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).

  • :notification_channel (Types::NotificationChannel) —

    The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to.

  • :output_config (Types::OutputConfig) —

    Sets if the output will go to a customer defined bucket. By default, Amazon Textract will save the results internally to be accessed by the GetDocumentAnalysis operation.

  • :kms_key_id (String) —

    The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.

Returns:

See Also:



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# File 'lib/aws-sdk-textract/client.rb', line 1226

def start_document_analysis(params = {}, options = {})
  req = build_request(:start_document_analysis, params)
  req.send_request(options)
end

#start_document_text_detection(params = {}) ⇒ Types::StartDocumentTextDetectionResponse

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, TIFF, 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].

[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html

Examples:

Request syntax with placeholder values


resp = client.start_document_text_detection({
  document_location: { # required
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  client_request_token: "ClientRequestToken",
  job_tag: "JobTag",
  notification_channel: {
    sns_topic_arn: "SNSTopicArn", # required
    role_arn: "RoleArn", # required
  },
  output_config: {
    s3_bucket: "S3Bucket", # required
    s3_prefix: "S3ObjectName",
  },
  kms_key_id: "KMSKeyId",
})

Response structure


resp.job_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :document_location (required, Types::DocumentLocation) —

    The location of the document to be processed.

  • :client_request_token (String) —

    The idempotent token that’s used to identify the start request. If you use the same token with multiple ‘StartDocumentTextDetection` requests, the same `JobId` is returned. Use `ClientRequestToken` to prevent the same job from being accidentally started more than once. For more information, see [Calling Amazon Textract Asynchronous Operations].

    [1]: docs.aws.amazon.com/textract/latest/dg/api-async.html

  • :job_tag (String) —

    An identifier that you specify that’s included in the completion notification published to the Amazon SNS topic. For example, you can use ‘JobTag` to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).

  • :notification_channel (Types::NotificationChannel) —

    The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to.

  • :output_config (Types::OutputConfig) —

    Sets if the output will go to a customer defined bucket. By default Amazon Textract will save the results internally to be accessed with the GetDocumentTextDetection operation.

  • :kms_key_id (String) —

    The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.

Returns:

See Also:



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# File 'lib/aws-sdk-textract/client.rb', line 1328

def start_document_text_detection(params = {}, options = {})
  req = build_request(:start_document_text_detection, params)
  req.send_request(options)
end

#start_expense_analysis(params = {}) ⇒ Types::StartExpenseAnalysisResponse

Starts the asynchronous analysis of invoices or receipts for data like contact information, items purchased, and vendor names.

‘StartExpenseAnalysis` can analyze text in documents that are in JPEG, PNG, and PDF format. The documents must be stored in an Amazon S3 bucket. Use the DocumentLocation parameter to specify the name of your S3 bucket and the name of the document in that bucket.

‘StartExpenseAnalysis` returns a job identifier (`JobId`) that you will provide to `GetExpenseAnalysis` to retrieve the results of the operation. When the analysis of the input invoices/receipts is finished, Amazon Textract publishes a completion status to the Amazon Simple Notification Service (Amazon SNS) topic that you provide to the `NotificationChannel`. To obtain the results of the invoice and receipt analysis operation, ensure that the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call GetExpenseAnalysis, and pass the job identifier (`JobId`) that was returned by your call to `StartExpenseAnalysis`.

For more information, see [Analyzing Invoices and Receipts].

[1]: docs.aws.amazon.com/textract/latest/dg/invoice-receipts.html

Examples:

Request syntax with placeholder values


resp = client.start_expense_analysis({
  document_location: { # required
    s3_object: {
      bucket: "S3Bucket",
      name: "S3ObjectName",
      version: "S3ObjectVersion",
    },
  },
  client_request_token: "ClientRequestToken",
  job_tag: "JobTag",
  notification_channel: {
    sns_topic_arn: "SNSTopicArn", # required
    role_arn: "RoleArn", # required
  },
  output_config: {
    s3_bucket: "S3Bucket", # required
    s3_prefix: "S3ObjectName",
  },
  kms_key_id: "KMSKeyId",
})

Response structure


resp.job_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :document_location (required, Types::DocumentLocation) —

    The location of the document to be processed.

  • :client_request_token (String) —

    The idempotent token that’s used to identify the start request. If you use the same token with multiple ‘StartDocumentTextDetection` requests, the same `JobId` is returned. Use `ClientRequestToken` to prevent the same job from being accidentally started more than once. For more information, see [Calling Amazon Textract Asynchronous Operations]

    [1]: docs.aws.amazon.com/textract/latest/dg/api-async.html

  • :job_tag (String) —

    An identifier you specify that’s included in the completion notification published to the Amazon SNS topic. For example, you can use ‘JobTag` to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).

  • :notification_channel (Types::NotificationChannel) —

    The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to.

  • :output_config (Types::OutputConfig) —

    Sets if the output will go to a customer defined bucket. By default, Amazon Textract will save the results internally to be accessed by the ‘GetExpenseAnalysis` operation.

  • :kms_key_id (String) —

    The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.

Returns:

See Also:



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# File 'lib/aws-sdk-textract/client.rb', line 1430

def start_expense_analysis(params = {}, options = {})
  req = build_request(:start_expense_analysis, params)
  req.send_request(options)
end

#waiter_names ⇒ Object

This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.

Deprecated. 


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# File 'lib/aws-sdk-textract/client.rb', line 1454

def waiter_names
  []
end