Class: Aws::Textract::Client

Inherits:
Seahorse::Client::Base show all
Includes:
ClientStubs
Defined in:
gems/aws-sdk-textract/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.

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

Instance Attribute Summary

Attributes inherited from Seahorse::Client::Base

#config, #handlers

API Operations collapse

Instance Method Summary collapse

Methods included from ClientStubs

#api_requests, #stub_data, #stub_responses

Methods inherited from Seahorse::Client::Base

add_plugin, api, clear_plugins, define, new, #operation_names, plugins, remove_plugin, set_api, set_plugins

Methods included from Seahorse::Client::HandlerBuilder

#handle, #handle_request, #handle_response

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[:credentials]
    • The :access_key_id, :secret_access_key, and :session_token options.
    • ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY']
    • ~/.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. Instance profile credential fetching can be disabled by setting ENV['AWS_EC2_METADATA_DISABLED'] to true.
  • :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:

    • Aws.config[:region]
    • ENV['AWS_REGION']
    • ENV['AMAZON_REGION']
    • ENV['AWS_DEFAULT_REGION']
    • ~/.aws/credentials
    • ~/.aws/config
  • :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.

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

    See DefaultsModeConfiguration for a list of the accepted modes and the configuration defaults that are included.

  • :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 https://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.

  • :token_provider (Aws::TokenProvider)

    A Bearer Token Provider. This can be an instance of any one of the following classes:

    • Aws::StaticTokenProvider - Used for configuring static, non-refreshing tokens.

    • Aws::SSOTokenProvider - Used for loading tokens from AWS SSO using an access token generated from aws login.

    When :token_provider is not configured directly, the Aws::TokenProviderChain will be used to search for tokens configured for your profile in shared configuration files.

  • :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.

  • :endpoint_provider (Aws::Textract::EndpointProvider)

    The endpoint provider used to resolve endpoints. Any object that responds to #resolve_endpoint(parameters) where parameters is a Struct similar to Aws::Textract::EndpointParameters

  • :http_proxy (URI::HTTP, String)

    A proxy to send requests through. Formatted like 'http://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 (Float) — 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.

  • :ssl_timeout (Float) — default: nil

    Sets the SSL timeout in seconds.

  • :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 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 375

def initialize(*args)
  super
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).

  • Signatures. A SIGNATURE Block object contains the location information of a signature in a document. If used in conjunction with forms or tables, a signature can be given a Key-Value pairing or be detected in the cell of a table.

  • Query. A QUERY Block object contains the query text, alias and link to the associated Query results block object.

  • Query Result. A QUERY_RESULT Block object contains the answer to the query and an ID that connects it to the query asked. This Block also contains a confidence score.

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.

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, QUERIES, SIGNATURES
  human_loop_config: {
    human_loop_name: "HumanLoopName", # required
    flow_definition_arn: "FlowDefinitionArn", # required
    data_attributes: {
      content_classifiers: ["FreeOfPersonallyIdentifiableInformation"], # accepts FreeOfPersonallyIdentifiableInformation, FreeOfAdultContent
    },
  },
  queries_config: {
    queries: [ # required
      {
        text: "QueryInput", # required
        alias: "QueryInput",
        pages: ["QueryPage"],
      },
    ],
  },
})

Response structure


resp..pages #=> Integer
resp.blocks #=> Array
resp.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT", "MERGED_CELL", "TITLE", "QUERY", "QUERY_RESULT", "SIGNATURE"
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", "MERGED_CELL", "TITLE", "ANSWER"
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", "COLUMN_HEADER"
resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.blocks[0].page #=> Integer
resp.blocks[0].query.text #=> String
resp.blocks[0].query.alias #=> String
resp.blocks[0].query.pages #=> Array
resp.blocks[0].query.pages[0] #=> String
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, PNG, PDF, or TIFF 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. Add SIGNATURES to return the locations of detected signatures. To perform both forms and table analysis, add TABLES and FORMS to FeatureTypes. To detect signatures within form data and table data, add SIGNATURES to either TABLES or FORMS. 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.

  • :queries_config (Types::QueriesConfig)

    Contains Queries and the alias for those Queries, as determined by the input.

Returns:

See Also:



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# File 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 539

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..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].summary_fields[0].currency.code #=> String
resp.expense_documents[0].summary_fields[0].currency.confidence #=> Float
resp.expense_documents[0].summary_fields[0].group_properties #=> Array
resp.expense_documents[0].summary_fields[0].group_properties[0].types #=> Array
resp.expense_documents[0].summary_fields[0].group_properties[0].types[0] #=> String
resp.expense_documents[0].summary_fields[0].group_properties[0].id #=> String
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.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].currency.code #=> String
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].currency.confidence #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties[0].types #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties[0].types[0] #=> String
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties[0].id #=> String
resp.expense_documents[0].blocks #=> Array
resp.expense_documents[0].blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT", "MERGED_CELL", "TITLE", "QUERY", "QUERY_RESULT", "SIGNATURE"
resp.expense_documents[0].blocks[0].confidence #=> Float
resp.expense_documents[0].blocks[0].text #=> String
resp.expense_documents[0].blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED"
resp.expense_documents[0].blocks[0].row_index #=> Integer
resp.expense_documents[0].blocks[0].column_index #=> Integer
resp.expense_documents[0].blocks[0].row_span #=> Integer
resp.expense_documents[0].blocks[0].column_span #=> Integer
resp.expense_documents[0].blocks[0].geometry.bounding_box.width #=> Float
resp.expense_documents[0].blocks[0].geometry.bounding_box.height #=> Float
resp.expense_documents[0].blocks[0].geometry.bounding_box.left #=> Float
resp.expense_documents[0].blocks[0].geometry.bounding_box.top #=> Float
resp.expense_documents[0].blocks[0].geometry.polygon #=> Array
resp.expense_documents[0].blocks[0].geometry.polygon[0].x #=> Float
resp.expense_documents[0].blocks[0].geometry.polygon[0].y #=> Float
resp.expense_documents[0].blocks[0].id #=> String
resp.expense_documents[0].blocks[0].relationships #=> Array
resp.expense_documents[0].blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES", "MERGED_CELL", "TITLE", "ANSWER"
resp.expense_documents[0].blocks[0].relationships[0].ids #=> Array
resp.expense_documents[0].blocks[0].relationships[0].ids[0] #=> String
resp.expense_documents[0].blocks[0].entity_types #=> Array
resp.expense_documents[0].blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE", "COLUMN_HEADER"
resp.expense_documents[0].blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.expense_documents[0].blocks[0].page #=> Integer
resp.expense_documents[0].blocks[0].query.text #=> String
resp.expense_documents[0].blocks[0].query.alias #=> String
resp.expense_documents[0].blocks[0].query.pages #=> Array
resp.expense_documents[0].blocks[0].query.pages[0] #=> String

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 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 698

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.Unlike other Amazon Textract operations, AnalyzeID doesn't return any Geometry data.

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.identity_documents[0].blocks #=> Array
resp.identity_documents[0].blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT", "MERGED_CELL", "TITLE", "QUERY", "QUERY_RESULT", "SIGNATURE"
resp.identity_documents[0].blocks[0].confidence #=> Float
resp.identity_documents[0].blocks[0].text #=> String
resp.identity_documents[0].blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED"
resp.identity_documents[0].blocks[0].row_index #=> Integer
resp.identity_documents[0].blocks[0].column_index #=> Integer
resp.identity_documents[0].blocks[0].row_span #=> Integer
resp.identity_documents[0].blocks[0].column_span #=> Integer
resp.identity_documents[0].blocks[0].geometry.bounding_box.width #=> Float
resp.identity_documents[0].blocks[0].geometry.bounding_box.height #=> Float
resp.identity_documents[0].blocks[0].geometry.bounding_box.left #=> Float
resp.identity_documents[0].blocks[0].geometry.bounding_box.top #=> Float
resp.identity_documents[0].blocks[0].geometry.polygon #=> Array
resp.identity_documents[0].blocks[0].geometry.polygon[0].x #=> Float
resp.identity_documents[0].blocks[0].geometry.polygon[0].y #=> Float
resp.identity_documents[0].blocks[0].id #=> String
resp.identity_documents[0].blocks[0].relationships #=> Array
resp.identity_documents[0].blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES", "MERGED_CELL", "TITLE", "ANSWER"
resp.identity_documents[0].blocks[0].relationships[0].ids #=> Array
resp.identity_documents[0].blocks[0].relationships[0].ids[0] #=> String
resp.identity_documents[0].blocks[0].entity_types #=> Array
resp.identity_documents[0].blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE", "COLUMN_HEADER"
resp.identity_documents[0].blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.identity_documents[0].blocks[0].page #=> Integer
resp.identity_documents[0].blocks[0].query.text #=> String
resp.identity_documents[0].blocks[0].query.alias #=> String
resp.identity_documents[0].blocks[0].query.pages #=> Array
resp.identity_documents[0].blocks[0].query.pages[0] #=> String
resp..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 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 782

def analyze_id(params = {}, options = {})
  req = build_request(:analyze_id, params)
  req.send_request(options)
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 in one of the following image formats: JPEG, PNG, PDF, or TIFF. 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.

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..pages #=> Integer
resp.blocks #=> Array
resp.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT", "MERGED_CELL", "TITLE", "QUERY", "QUERY_RESULT", "SIGNATURE"
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", "MERGED_CELL", "TITLE", "ANSWER"
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", "COLUMN_HEADER"
resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.blocks[0].page #=> Integer
resp.blocks[0].query.text #=> String
resp.blocks[0].query.alias #=> String
resp.blocks[0].query.pages #=> Array
resp.blocks[0].query.pages[0] #=> String
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 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 874

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).

  • Query. A QUERY Block object contains the query text, alias and link to the associated Query results block object.

  • Query Results. A QUERY_RESULT Block object contains the answer to the query and an ID that connects it to the query asked. This Block also contains a confidence score.

While processing a document with queries, look out for INVALID_REQUEST_PARAMETERS output. This indicates that either the per page query limit has been exceeded or that the operation is trying to query a page in the document which doesn’t exist.

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.

Examples:

Request syntax with placeholder values


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

Response structure


resp..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", "MERGED_CELL", "TITLE", "QUERY", "QUERY_RESULT", "SIGNATURE"
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", "MERGED_CELL", "TITLE", "ANSWER"
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", "COLUMN_HEADER"
resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.blocks[0].page #=> Integer
resp.blocks[0].query.text #=> String
resp.blocks[0].query.alias #=> String
resp.blocks[0].query.pages #=> Array
resp.blocks[0].query.pages[0] #=> String
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 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 1024

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.

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..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", "MERGED_CELL", "TITLE", "QUERY", "QUERY_RESULT", "SIGNATURE"
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", "MERGED_CELL", "TITLE", "ANSWER"
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", "COLUMN_HEADER"
resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.blocks[0].page #=> Integer
resp.blocks[0].query.text #=> String
resp.blocks[0].query.alias #=> String
resp.blocks[0].query.pages #=> Array
resp.blocks[0].query.pages[0] #=> String
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 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 1146

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.

Examples:

Request syntax with placeholder values


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

Response structure


resp..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].summary_fields[0].currency.code #=> String
resp.expense_documents[0].summary_fields[0].currency.confidence #=> Float
resp.expense_documents[0].summary_fields[0].group_properties #=> Array
resp.expense_documents[0].summary_fields[0].group_properties[0].types #=> Array
resp.expense_documents[0].summary_fields[0].group_properties[0].types[0] #=> String
resp.expense_documents[0].summary_fields[0].group_properties[0].id #=> String
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.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].currency.code #=> String
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].currency.confidence #=> Float
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties[0].types #=> Array
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties[0].types[0] #=> String
resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties[0].id #=> String
resp.expense_documents[0].blocks #=> Array
resp.expense_documents[0].blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT", "MERGED_CELL", "TITLE", "QUERY", "QUERY_RESULT", "SIGNATURE"
resp.expense_documents[0].blocks[0].confidence #=> Float
resp.expense_documents[0].blocks[0].text #=> String
resp.expense_documents[0].blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED"
resp.expense_documents[0].blocks[0].row_index #=> Integer
resp.expense_documents[0].blocks[0].column_index #=> Integer
resp.expense_documents[0].blocks[0].row_span #=> Integer
resp.expense_documents[0].blocks[0].column_span #=> Integer
resp.expense_documents[0].blocks[0].geometry.bounding_box.width #=> Float
resp.expense_documents[0].blocks[0].geometry.bounding_box.height #=> Float
resp.expense_documents[0].blocks[0].geometry.bounding_box.left #=> Float
resp.expense_documents[0].blocks[0].geometry.bounding_box.top #=> Float
resp.expense_documents[0].blocks[0].geometry.polygon #=> Array
resp.expense_documents[0].blocks[0].geometry.polygon[0].x #=> Float
resp.expense_documents[0].blocks[0].geometry.polygon[0].y #=> Float
resp.expense_documents[0].blocks[0].id #=> String
resp.expense_documents[0].blocks[0].relationships #=> Array
resp.expense_documents[0].blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES", "MERGED_CELL", "TITLE", "ANSWER"
resp.expense_documents[0].blocks[0].relationships[0].ids #=> Array
resp.expense_documents[0].blocks[0].relationships[0].ids[0] #=> String
resp.expense_documents[0].blocks[0].entity_types #=> Array
resp.expense_documents[0].blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE", "COLUMN_HEADER"
resp.expense_documents[0].blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.expense_documents[0].blocks[0].page #=> Integer
resp.expense_documents[0].blocks[0].query.text #=> String
resp.expense_documents[0].blocks[0].query.alias #=> String
resp.expense_documents[0].blocks[0].query.pages #=> Array
resp.expense_documents[0].blocks[0].query.pages[0] #=> String
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 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 1321

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

#get_lending_analysis(params = {}) ⇒ Types::GetLendingAnalysisResponse

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

You start asynchronous text analysis by calling StartLendingAnalysis, 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 StartLendingAnalysis.

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 GetLendingAnalysis, and pass the job identifier (JobId) from the initial call to StartLendingAnalysis.

Examples:

Request syntax with placeholder values


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

Response structure


resp..pages #=> Integer
resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED", "PARTIAL_SUCCESS"
resp.next_token #=> String
resp.results #=> Array
resp.results[0].page #=> Integer
resp.results[0].page_classification.page_type #=> Array
resp.results[0].page_classification.page_type[0].value #=> String
resp.results[0].page_classification.page_type[0].confidence #=> Float
resp.results[0].page_classification.page_number #=> Array
resp.results[0].page_classification.page_number[0].value #=> String
resp.results[0].page_classification.page_number[0].confidence #=> Float
resp.results[0].extractions #=> Array
resp.results[0].extractions[0].lending_document.lending_fields #=> Array
resp.results[0].extractions[0].lending_document.lending_fields[0].type #=> String
resp.results[0].extractions[0].lending_document.lending_fields[0].key_detection.text #=> String
resp.results[0].extractions[0].lending_document.lending_fields[0].key_detection.selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.results[0].extractions[0].lending_document.lending_fields[0].key_detection.geometry.bounding_box.width #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].key_detection.geometry.bounding_box.height #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].key_detection.geometry.bounding_box.left #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].key_detection.geometry.bounding_box.top #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].key_detection.geometry.polygon #=> Array
resp.results[0].extractions[0].lending_document.lending_fields[0].key_detection.geometry.polygon[0].x #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].key_detection.geometry.polygon[0].y #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].key_detection.confidence #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections #=> Array
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections[0].text #=> String
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections[0].geometry.bounding_box.width #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections[0].geometry.bounding_box.height #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections[0].geometry.bounding_box.left #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections[0].geometry.bounding_box.top #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections[0].geometry.polygon #=> Array
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections[0].geometry.polygon[0].x #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections[0].geometry.polygon[0].y #=> Float
resp.results[0].extractions[0].lending_document.lending_fields[0].value_detections[0].confidence #=> Float
resp.results[0].extractions[0].lending_document.signature_detections #=> Array
resp.results[0].extractions[0].lending_document.signature_detections[0].confidence #=> Float
resp.results[0].extractions[0].lending_document.signature_detections[0].geometry.bounding_box.width #=> Float
resp.results[0].extractions[0].lending_document.signature_detections[0].geometry.bounding_box.height #=> Float
resp.results[0].extractions[0].lending_document.signature_detections[0].geometry.bounding_box.left #=> Float
resp.results[0].extractions[0].lending_document.signature_detections[0].geometry.bounding_box.top #=> Float
resp.results[0].extractions[0].lending_document.signature_detections[0].geometry.polygon #=> Array
resp.results[0].extractions[0].lending_document.signature_detections[0].geometry.polygon[0].x #=> Float
resp.results[0].extractions[0].lending_document.signature_detections[0].geometry.polygon[0].y #=> Float
resp.results[0].extractions[0].expense_document.expense_index #=> Integer
resp.results[0].extractions[0].expense_document.summary_fields #=> Array
resp.results[0].extractions[0].expense_document.summary_fields[0].type.text #=> String
resp.results[0].extractions[0].expense_document.summary_fields[0].type.confidence #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].label_detection.text #=> String
resp.results[0].extractions[0].expense_document.summary_fields[0].label_detection.geometry.bounding_box.width #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].label_detection.geometry.bounding_box.height #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].label_detection.geometry.bounding_box.left #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].label_detection.geometry.bounding_box.top #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].label_detection.geometry.polygon #=> Array
resp.results[0].extractions[0].expense_document.summary_fields[0].label_detection.geometry.polygon[0].x #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].label_detection.geometry.polygon[0].y #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].label_detection.confidence #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].value_detection.text #=> String
resp.results[0].extractions[0].expense_document.summary_fields[0].value_detection.geometry.bounding_box.width #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].value_detection.geometry.bounding_box.height #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].value_detection.geometry.bounding_box.left #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].value_detection.geometry.bounding_box.top #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].value_detection.geometry.polygon #=> Array
resp.results[0].extractions[0].expense_document.summary_fields[0].value_detection.geometry.polygon[0].x #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].value_detection.geometry.polygon[0].y #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].value_detection.confidence #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].page_number #=> Integer
resp.results[0].extractions[0].expense_document.summary_fields[0].currency.code #=> String
resp.results[0].extractions[0].expense_document.summary_fields[0].currency.confidence #=> Float
resp.results[0].extractions[0].expense_document.summary_fields[0].group_properties #=> Array
resp.results[0].extractions[0].expense_document.summary_fields[0].group_properties[0].types #=> Array
resp.results[0].extractions[0].expense_document.summary_fields[0].group_properties[0].types[0] #=> String
resp.results[0].extractions[0].expense_document.summary_fields[0].group_properties[0].id #=> String
resp.results[0].extractions[0].expense_document.line_item_groups #=> Array
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_item_group_index #=> Integer
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items #=> Array
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields #=> Array
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].type.text #=> String
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].type.confidence #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.text #=> String
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.width #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.height #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.left #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.top #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon #=> Array
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon[0].x #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon[0].y #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.confidence #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.text #=> String
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.width #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.height #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.left #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.top #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon #=> Array
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon[0].x #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon[0].y #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.confidence #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].page_number #=> Integer
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].currency.code #=> String
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].currency.confidence #=> Float
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties #=> Array
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties[0].types #=> Array
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties[0].types[0] #=> String
resp.results[0].extractions[0].expense_document.line_item_groups[0].line_items[0].line_item_expense_fields[0].group_properties[0].id #=> String
resp.results[0].extractions[0].expense_document.blocks #=> Array
resp.results[0].extractions[0].expense_document.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT", "MERGED_CELL", "TITLE", "QUERY", "QUERY_RESULT", "SIGNATURE"
resp.results[0].extractions[0].expense_document.blocks[0].confidence #=> Float
resp.results[0].extractions[0].expense_document.blocks[0].text #=> String
resp.results[0].extractions[0].expense_document.blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED"
resp.results[0].extractions[0].expense_document.blocks[0].row_index #=> Integer
resp.results[0].extractions[0].expense_document.blocks[0].column_index #=> Integer
resp.results[0].extractions[0].expense_document.blocks[0].row_span #=> Integer
resp.results[0].extractions[0].expense_document.blocks[0].column_span #=> Integer
resp.results[0].extractions[0].expense_document.blocks[0].geometry.bounding_box.width #=> Float
resp.results[0].extractions[0].expense_document.blocks[0].geometry.bounding_box.height #=> Float
resp.results[0].extractions[0].expense_document.blocks[0].geometry.bounding_box.left #=> Float
resp.results[0].extractions[0].expense_document.blocks[0].geometry.bounding_box.top #=> Float
resp.results[0].extractions[0].expense_document.blocks[0].geometry.polygon #=> Array
resp.results[0].extractions[0].expense_document.blocks[0].geometry.polygon[0].x #=> Float
resp.results[0].extractions[0].expense_document.blocks[0].geometry.polygon[0].y #=> Float
resp.results[0].extractions[0].expense_document.blocks[0].id #=> String
resp.results[0].extractions[0].expense_document.blocks[0].relationships #=> Array
resp.results[0].extractions[0].expense_document.blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES", "MERGED_CELL", "TITLE", "ANSWER"
resp.results[0].extractions[0].expense_document.blocks[0].relationships[0].ids #=> Array
resp.results[0].extractions[0].expense_document.blocks[0].relationships[0].ids[0] #=> String
resp.results[0].extractions[0].expense_document.blocks[0].entity_types #=> Array
resp.results[0].extractions[0].expense_document.blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE", "COLUMN_HEADER"
resp.results[0].extractions[0].expense_document.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.results[0].extractions[0].expense_document.blocks[0].page #=> Integer
resp.results[0].extractions[0].expense_document.blocks[0].query.text #=> String
resp.results[0].extractions[0].expense_document.blocks[0].query.alias #=> String
resp.results[0].extractions[0].expense_document.blocks[0].query.pages #=> Array
resp.results[0].extractions[0].expense_document.blocks[0].query.pages[0] #=> String
resp.results[0].extractions[0].identity_document.document_index #=> Integer
resp.results[0].extractions[0].identity_document.identity_document_fields #=> Array
resp.results[0].extractions[0].identity_document.identity_document_fields[0].type.text #=> String
resp.results[0].extractions[0].identity_document.identity_document_fields[0].type.normalized_value.value #=> String
resp.results[0].extractions[0].identity_document.identity_document_fields[0].type.normalized_value.value_type #=> String, one of "DATE"
resp.results[0].extractions[0].identity_document.identity_document_fields[0].type.confidence #=> Float
resp.results[0].extractions[0].identity_document.identity_document_fields[0].value_detection.text #=> String
resp.results[0].extractions[0].identity_document.identity_document_fields[0].value_detection.normalized_value.value #=> String
resp.results[0].extractions[0].identity_document.identity_document_fields[0].value_detection.normalized_value.value_type #=> String, one of "DATE"
resp.results[0].extractions[0].identity_document.identity_document_fields[0].value_detection.confidence #=> Float
resp.results[0].extractions[0].identity_document.blocks #=> Array
resp.results[0].extractions[0].identity_document.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT", "MERGED_CELL", "TITLE", "QUERY", "QUERY_RESULT", "SIGNATURE"
resp.results[0].extractions[0].identity_document.blocks[0].confidence #=> Float
resp.results[0].extractions[0].identity_document.blocks[0].text #=> String
resp.results[0].extractions[0].identity_document.blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED"
resp.results[0].extractions[0].identity_document.blocks[0].row_index #=> Integer
resp.results[0].extractions[0].identity_document.blocks[0].column_index #=> Integer
resp.results[0].extractions[0].identity_document.blocks[0].row_span #=> Integer
resp.results[0].extractions[0].identity_document.blocks[0].column_span #=> Integer
resp.results[0].extractions[0].identity_document.blocks[0].geometry.bounding_box.width #=> Float
resp.results[0].extractions[0].identity_document.blocks[0].geometry.bounding_box.height #=> Float
resp.results[0].extractions[0].identity_document.blocks[0].geometry.bounding_box.left #=> Float
resp.results[0].extractions[0].identity_document.blocks[0].geometry.bounding_box.top #=> Float
resp.results[0].extractions[0].identity_document.blocks[0].geometry.polygon #=> Array
resp.results[0].extractions[0].identity_document.blocks[0].geometry.polygon[0].x #=> Float
resp.results[0].extractions[0].identity_document.blocks[0].geometry.polygon[0].y #=> Float
resp.results[0].extractions[0].identity_document.blocks[0].id #=> String
resp.results[0].extractions[0].identity_document.blocks[0].relationships #=> Array
resp.results[0].extractions[0].identity_document.blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES", "MERGED_CELL", "TITLE", "ANSWER"
resp.results[0].extractions[0].identity_document.blocks[0].relationships[0].ids #=> Array
resp.results[0].extractions[0].identity_document.blocks[0].relationships[0].ids[0] #=> String
resp.results[0].extractions[0].identity_document.blocks[0].entity_types #=> Array
resp.results[0].extractions[0].identity_document.blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE", "COLUMN_HEADER"
resp.results[0].extractions[0].identity_document.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED"
resp.results[0].extractions[0].identity_document.blocks[0].page #=> Integer
resp.results[0].extractions[0].identity_document.blocks[0].query.text #=> String
resp.results[0].extractions[0].identity_document.blocks[0].query.alias #=> String
resp.results[0].extractions[0].identity_document.blocks[0].query.pages #=> Array
resp.results[0].extractions[0].identity_document.blocks[0].query.pages[0] #=> String
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_lending_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    A unique identifier for the lending or text-detection job. The JobId is returned from StartLendingAnalysis. 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 30. If you specify a value greater than 30, a maximum of 30 results is returned. The default value is 30.

  • :next_token (String)

    If the previous response was incomplete, Amazon Textract returns a pagination token in the response. You can use this pagination token to retrieve the next set of lending results.

Returns:

See Also:



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# File 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 1560

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

#get_lending_analysis_summary(params = {}) ⇒ Types::GetLendingAnalysisSummaryResponse

Gets summarized results for the StartLendingAnalysis operation, which analyzes text in a lending document. The returned summary consists of information about documents grouped together by a common document type. Information like detected signatures, page numbers, and split documents is returned with respect to the type of grouped document.

You start asynchronous text analysis by calling StartLendingAnalysis, 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 StartLendingAnalysis.

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 GetLendingAnalysisSummary, and pass the job identifier (JobId) from the initial call to StartLendingAnalysis.

Examples:

Request syntax with placeholder values


resp = client.get_lending_analysis_summary({
  job_id: "JobId", # required
})

Response structure


resp..pages #=> Integer
resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED", "PARTIAL_SUCCESS"
resp.summary.document_groups #=> Array
resp.summary.document_groups[0].type #=> String
resp.summary.document_groups[0].split_documents #=> Array
resp.summary.document_groups[0].split_documents[0].index #=> Integer
resp.summary.document_groups[0].split_documents[0].pages #=> Array
resp.summary.document_groups[0].split_documents[0].pages[0] #=> Integer
resp.summary.document_groups[0].detected_signatures #=> Array
resp.summary.document_groups[0].detected_signatures[0].page #=> Integer
resp.summary.document_groups[0].undetected_signatures #=> Array
resp.summary.document_groups[0].undetected_signatures[0].page #=> Integer
resp.summary.undetected_document_types #=> Array
resp.summary.undetected_document_types[0] #=> String
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_lending_model_version #=> String

Parameters:

  • params (Hash) (defaults to: {})

    ({})

Options Hash (params):

  • :job_id (required, String)

    A unique identifier for the lending or text-detection job. The JobId is returned from StartLendingAnalysis. A JobId value is only valid for 7 days.

Returns:

See Also:



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# File 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 1631

def get_lending_analysis_summary(params = {}, options = {})
  req = build_request(:get_lending_analysis_summary, 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.

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, QUERIES, SIGNATURES
  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",
  queries_config: {
    queries: [ # required
      {
        text: "QueryInput", # required
        alias: "QueryInput",
        pages: ["QueryPage"],
      },
    ],
  },
})

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.

  • :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.

  • :queries_config (Types::QueriesConfig)

Returns:

See Also:



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# File 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 1752

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.

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.

  • :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 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 1854

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.

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

  • :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 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 1956

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

#start_lending_analysis(params = {}) ⇒ Types::StartLendingAnalysisResponse

Starts the classification and analysis of an input document. StartLendingAnalysis initiates the classification and analysis of a packet of lending documents. StartLendingAnalysis operates on a document file located in an Amazon S3 bucket.

StartLendingAnalysis can analyze text in documents that are in one of the following formats: JPEG, PNG, TIFF, PDF. Use DocumentLocation to specify the bucket name and the file name of the document.

StartLendingAnalysis returns a job identifier (JobId) that you use to get the results of the operation. When the 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 the status is SUCCEEDED you can call either GetLendingAnalysis or GetLendingAnalysisSummary and provide the JobId to obtain the results of the analysis.

If using OutputConfig to specify an Amazon S3 bucket, the output will be contained within the specified prefix in a directory labeled with the job-id. In the directory there are 3 sub-directories:

  • detailedResponse (contains the GetLendingAnalysis response)

  • summaryResponse (for the GetLendingAnalysisSummary response)

  • splitDocuments (documents split across logical boundaries)

Examples:

Request syntax with placeholder values


resp = client.start_lending_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 Amazon S3 bucket that contains the document to be processed. It's used by asynchronous operations.

    The input document can be an image file in JPEG or PNG format. It can also be a file in PDF format.

  • :client_request_token (String)

    The idempotent token that you use to identify the start request. If you use the same token with multiple StartLendingAnalysis 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.

  • :job_tag (String)

    An identifier that you specify to be 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 Simple Notification Service (Amazon SNS) topic to which Amazon Textract publishes the completion status of an asynchronous document operation.

  • :output_config (Types::OutputConfig)

    Sets whether or not your output will go to a user created bucket. Used to set the name of the bucket, and the prefix on the output file.

    OutputConfig is an optional parameter which lets you adjust where your output will be placed. By default, Amazon Textract will store the results internally and can only be accessed by the Get API operations. With OutputConfig enabled, you can set the name of the bucket the output will be sent to the file prefix of the results where you can download your results. Additionally, you can set the KMSKeyID parameter to a customer master key (CMK) to encrypt your output. Without this parameter set Amazon Textract will encrypt server-side using the AWS managed CMK for Amazon S3.

    Decryption of Customer Content is necessary for processing of the documents by Amazon Textract. If your account is opted out under an AI services opt out policy then all unencrypted Customer Content is immediately and permanently deleted after the Customer Content has been processed by the service. No copy of of the output is retained by Amazon Textract. For information about how to opt out, see Managing AI services opt-out policy.

    For more information on data privacy, see the Data Privacy FAQ.

  • :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 'gems/aws-sdk-textract/lib/aws-sdk-textract/client.rb', line 2090

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