class Aws::Textract::Client
An API client for Textract
. To construct a client, you need to configure a `:region` and `:credentials`.
client = Aws::Textract::Client.new( region: region_name, credentials: credentials, # ... )
For details on configuring region and credentials see the [developer guide](/sdk-for-ruby/v3/developer-guide/setup-config.html).
See {#initialize} for a full list of supported configuration options.
Attributes
@api private
Public Class Methods
@api private
# File lib/aws-sdk-textract/client.rb, line 1166 def errors_module Errors end
@overload initialize(options)
@param [Hash] options @option options [required, Aws::CredentialProvider] :credentials 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. @option options [required, String] :region 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` @option options [String] :access_key_id @option options [Boolean] :active_endpoint_cache (false) When set to `true`, a thread polling for endpoints will be running in the background every 60 secs (default). Defaults to `false`. @option options [Boolean] :adaptive_retry_wait_to_fill (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. @option options [Boolean] :client_side_monitoring (false) When `true`, client-side metrics will be collected for all API requests from this client. @option options [String] :client_side_monitoring_client_id ("") Allows you to provide an identifier for this client which will be attached to all generated client side metrics. Defaults to an empty string. @option options [String] :client_side_monitoring_host ("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. @option options [Integer] :client_side_monitoring_port (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. @option options [Aws::ClientSideMonitoring::Publisher] :client_side_monitoring_publisher (Aws::ClientSideMonitoring::Publisher) Allows you to provide a custom client-side monitoring publisher class. By default, will use the Client Side Monitoring Agent Publisher. @option options [Boolean] :convert_params (true) When `true`, an attempt is made to coerce request parameters into the required types. @option options [Boolean] :correct_clock_skew (true) Used only in `standard` and adaptive retry modes. Specifies whether to apply a clock skew correction and retry requests with skewed client clocks. @option options [Boolean] :disable_host_prefix_injection (false) Set to true to disable SDK automatically adding host prefix to default service endpoint when available. @option options [String] :endpoint 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. @option options [Integer] :endpoint_cache_max_entries (1000) Used for the maximum size limit of the LRU cache storing endpoints data for endpoint discovery enabled operations. Defaults to 1000. @option options [Integer] :endpoint_cache_max_threads (10) Used for the maximum threads in use for polling endpoints to be cached, defaults to 10. @option options [Integer] :endpoint_cache_poll_interval (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. @option options [Boolean] :endpoint_discovery (false) When set to `true`, endpoint discovery will be enabled for operations when available. @option options [Aws::Log::Formatter] :log_formatter (Aws::Log::Formatter.default) The log formatter. @option options [Symbol] :log_level (:info) The log level to send messages to the `:logger` at. @option options [Logger] :logger The Logger instance to send log messages to. If this option is not set, logging will be disabled. @option options [Integer] :max_attempts (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. @option options [String] :profile ("default") Used when loading credentials from the shared credentials file at HOME/.aws/credentials. When not specified, 'default' is used. @option options [Proc] :retry_backoff A proc or lambda used for backoff. Defaults to 2**retries * retry_base_delay. This option is only used in the `legacy` retry mode. @option options [Float] :retry_base_delay (0.3) The base delay in seconds used by the default backoff function. This option is only used in the `legacy` retry mode. @option options [Symbol] :retry_jitter (: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 @option options [Integer] :retry_limit (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. @option options [Integer] :retry_max_delay (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. @option options [String] :retry_mode ("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. @option options [String] :secret_access_key @option options [String] :session_token @option options [Boolean] :simple_json (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. @option options [Boolean] :stub_responses (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. @option options [Boolean] :validate_params (true) When `true`, request parameters are validated before sending the request. @option options [URI::HTTP,String] :http_proxy A proxy to send requests through. Formatted like 'http://proxy.com:123'. @option options [Float] :http_open_timeout (15) The number of seconds to wait when opening a HTTP session before raising a `Timeout::Error`. @option options [Integer] :http_read_timeout (60) The default number of seconds to wait for response data. This value can safely be set per-request on the session. @option options [Float] :http_idle_timeout (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. @option options [Float] :http_continue_timeout (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. @option options [Boolean] :http_wire_trace (false) When `true`, HTTP debug output will be sent to the `:logger`. @option options [Boolean] :ssl_verify_peer (true) When `true`, SSL peer certificates are verified when establishing a connection. @option options [String] :ssl_ca_bundle 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. @option options [String] :ssl_ca_directory 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.
# File lib/aws-sdk-textract/client.rb, line 334 def initialize(*args) super end
Public Instance Methods
Analyzes an input document for relationships between detected items.
The types of information returned are as follows:
-
Form data (key-value pairs). The related information is returned in two Block objects, each of type `KEY_VALUE_SET`: a KEY `Block` object and a VALUE `Block` object. For example, *Name: Ana Silva Carolina* contains a key and value. Name: is the key. *Ana Silva Carolina* is the value.
-
Table and table cell data. A TABLE `Block` object contains information about a detected table. A CELL `Block` object is returned for each cell in a table.
-
Lines and words of text. A LINE `Block` object contains one or more WORD `Block` objects. All lines and words that are detected in the document are returned (including text that doesn't have a relationship with the value of `FeatureTypes`).
Selection elements such as check boxes and option buttons (radio buttons) can be detected in form data and in tables. A SELECTION_ELEMENT `Block` object contains information about a selection element, including the selection status.
You can choose which type of analysis to perform by specifying the `FeatureTypes` list.
The output is returned in a list of `Block` objects.
`AnalyzeDocument` is a synchronous operation. To analyze documents asynchronously, use StartDocumentAnalysis.
For more information, see [Document Text Analysis].
[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html
@option params [required, Types::Document] :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.
@option params [required, Array<String>] :feature_types
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`).
@option params [Types::HumanLoopConfig] :human_loop_config
Sets the configuration for the human in the loop workflow for analyzing documents.
@return [Types::AnalyzeDocumentResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::AnalyzeDocumentResponse#document_metadata #document_metadata} => Types::DocumentMetadata * {Types::AnalyzeDocumentResponse#blocks #blocks} => Array<Types::Block> * {Types::AnalyzeDocumentResponse#human_loop_activation_output #human_loop_activation_output} => Types::HumanLoopActivationOutput * {Types::AnalyzeDocumentResponse#analyze_document_model_version #analyze_document_model_version} => String
@example Request syntax with placeholder values
resp = client.analyze_document({ document: { # required bytes: "data", s3_object: { bucket: "S3Bucket", name: "S3ObjectName", version: "S3ObjectVersion", }, }, feature_types: ["TABLES"], # required, accepts TABLES, FORMS human_loop_config: { human_loop_name: "HumanLoopName", # required flow_definition_arn: "FlowDefinitionArn", # required data_attributes: { content_classifiers: ["FreeOfPersonallyIdentifiableInformation"], # accepts FreeOfPersonallyIdentifiableInformation, FreeOfAdultContent }, }, })
@example Response structure
resp.document_metadata.pages #=> Integer resp.blocks #=> Array resp.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT" resp.blocks[0].confidence #=> Float resp.blocks[0].text #=> String resp.blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED" resp.blocks[0].row_index #=> Integer resp.blocks[0].column_index #=> Integer resp.blocks[0].row_span #=> Integer resp.blocks[0].column_span #=> Integer resp.blocks[0].geometry.bounding_box.width #=> Float resp.blocks[0].geometry.bounding_box.height #=> Float resp.blocks[0].geometry.bounding_box.left #=> Float resp.blocks[0].geometry.bounding_box.top #=> Float resp.blocks[0].geometry.polygon #=> Array resp.blocks[0].geometry.polygon[0].x #=> Float resp.blocks[0].geometry.polygon[0].y #=> Float resp.blocks[0].id #=> String resp.blocks[0].relationships #=> Array resp.blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES" resp.blocks[0].relationships[0].ids #=> Array resp.blocks[0].relationships[0].ids[0] #=> String resp.blocks[0].entity_types #=> Array resp.blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE" resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED" resp.blocks[0].page #=> Integer resp.human_loop_activation_output.human_loop_arn #=> String resp.human_loop_activation_output.human_loop_activation_reasons #=> Array resp.human_loop_activation_output.human_loop_activation_reasons[0] #=> String resp.human_loop_activation_output.human_loop_activation_conditions_evaluation_results #=> String resp.analyze_document_model_version #=> String
@see docs.aws.amazon.com/goto/WebAPI/textract-2018-06-27/AnalyzeDocument AWS API Documentation
@overload analyze_document
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-textract/client.rb, line 465 def analyze_document(params = {}, options = {}) req = build_request(:analyze_document, params) req.send_request(options) end
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.
@option params [required, Types::Document] :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.
@return [Types::AnalyzeExpenseResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::AnalyzeExpenseResponse#document_metadata #document_metadata} => Types::DocumentMetadata * {Types::AnalyzeExpenseResponse#expense_documents #expense_documents} => Array<Types::ExpenseDocument>
@example Request syntax with placeholder values
resp = client.analyze_expense({ document: { # required bytes: "data", s3_object: { bucket: "S3Bucket", name: "S3ObjectName", version: "S3ObjectVersion", }, }, })
@example Response structure
resp.document_metadata.pages #=> Integer resp.expense_documents #=> Array resp.expense_documents[0].expense_index #=> Integer resp.expense_documents[0].summary_fields #=> Array resp.expense_documents[0].summary_fields[0].type.text #=> String resp.expense_documents[0].summary_fields[0].type.confidence #=> Float resp.expense_documents[0].summary_fields[0].label_detection.text #=> String resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.width #=> Float resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.height #=> Float resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.left #=> Float resp.expense_documents[0].summary_fields[0].label_detection.geometry.bounding_box.top #=> Float resp.expense_documents[0].summary_fields[0].label_detection.geometry.polygon #=> Array resp.expense_documents[0].summary_fields[0].label_detection.geometry.polygon[0].x #=> Float resp.expense_documents[0].summary_fields[0].label_detection.geometry.polygon[0].y #=> Float resp.expense_documents[0].summary_fields[0].label_detection.confidence #=> Float resp.expense_documents[0].summary_fields[0].value_detection.text #=> String resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.width #=> Float resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.height #=> Float resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.left #=> Float resp.expense_documents[0].summary_fields[0].value_detection.geometry.bounding_box.top #=> Float resp.expense_documents[0].summary_fields[0].value_detection.geometry.polygon #=> Array resp.expense_documents[0].summary_fields[0].value_detection.geometry.polygon[0].x #=> Float resp.expense_documents[0].summary_fields[0].value_detection.geometry.polygon[0].y #=> Float resp.expense_documents[0].summary_fields[0].value_detection.confidence #=> Float resp.expense_documents[0].summary_fields[0].page_number #=> Integer resp.expense_documents[0].line_item_groups #=> Array resp.expense_documents[0].line_item_groups[0].line_item_group_index #=> Integer resp.expense_documents[0].line_item_groups[0].line_items #=> Array resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields #=> Array resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].type.text #=> String resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].type.confidence #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.text #=> String resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.width #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.height #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.left #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.bounding_box.top #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon #=> Array resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon[0].x #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.geometry.polygon[0].y #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].label_detection.confidence #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.text #=> String resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.width #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.height #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.left #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.bounding_box.top #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon #=> Array resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon[0].x #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.geometry.polygon[0].y #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].value_detection.confidence #=> Float resp.expense_documents[0].line_item_groups[0].line_items[0].line_item_expense_fields[0].page_number #=> Integer
@see docs.aws.amazon.com/goto/WebAPI/textract-2018-06-27/AnalyzeExpense AWS API Documentation
@overload analyze_expense
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-textract/client.rb, line 583 def analyze_expense(params = {}, options = {}) req = build_request(:analyze_expense, params) req.send_request(options) end
@param params ({}) @api private
# File lib/aws-sdk-textract/client.rb, line 1141 def build_request(operation_name, params = {}) handlers = @handlers.for(operation_name) context = Seahorse::Client::RequestContext.new( operation_name: operation_name, operation: config.api.operation(operation_name), client: self, params: params, config: config) context[:gem_name] = 'aws-sdk-textract' context[:gem_version] = '1.28.0' Seahorse::Client::Request.new(handlers, context) end
Detects text in the input document. Amazon Textract
can detect lines of text and the words that make up a line of text. The input document must be an image in JPEG or PNG format. `DetectDocumentText` returns the detected text in an array of Block objects.
Each document page has as an associated `Block` of type PAGE. Each PAGE `Block` object is the parent of LINE `Block` objects that represent the lines of detected text on a page. A LINE `Block` object is a parent for each word that makes up the line. Words are represented by `Block` objects of type WORD.
`DetectDocumentText` is a synchronous operation. To analyze documents asynchronously, use StartDocumentTextDetection.
For more information, see [Document Text Detection].
[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html
@option params [required, Types::Document] :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.
@return [Types::DetectDocumentTextResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::DetectDocumentTextResponse#document_metadata #document_metadata} => Types::DocumentMetadata * {Types::DetectDocumentTextResponse#blocks #blocks} => Array<Types::Block> * {Types::DetectDocumentTextResponse#detect_document_text_model_version #detect_document_text_model_version} => String
@example Request syntax with placeholder values
resp = client.detect_document_text({ document: { # required bytes: "data", s3_object: { bucket: "S3Bucket", name: "S3ObjectName", version: "S3ObjectVersion", }, }, })
@example Response structure
resp.document_metadata.pages #=> Integer resp.blocks #=> Array resp.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT" resp.blocks[0].confidence #=> Float resp.blocks[0].text #=> String resp.blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED" resp.blocks[0].row_index #=> Integer resp.blocks[0].column_index #=> Integer resp.blocks[0].row_span #=> Integer resp.blocks[0].column_span #=> Integer resp.blocks[0].geometry.bounding_box.width #=> Float resp.blocks[0].geometry.bounding_box.height #=> Float resp.blocks[0].geometry.bounding_box.left #=> Float resp.blocks[0].geometry.bounding_box.top #=> Float resp.blocks[0].geometry.polygon #=> Array resp.blocks[0].geometry.polygon[0].x #=> Float resp.blocks[0].geometry.polygon[0].y #=> Float resp.blocks[0].id #=> String resp.blocks[0].relationships #=> Array resp.blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES" resp.blocks[0].relationships[0].ids #=> Array resp.blocks[0].relationships[0].ids[0] #=> String resp.blocks[0].entity_types #=> Array resp.blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE" resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED" resp.blocks[0].page #=> Integer resp.detect_document_text_model_version #=> String
@see docs.aws.amazon.com/goto/WebAPI/textract-2018-06-27/DetectDocumentText AWS API Documentation
@overload detect_document_text
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-textract/client.rb, line 670 def detect_document_text(params = {}, options = {}) req = build_request(:detect_document_text, params) req.send_request(options) end
Gets the results for an Amazon Textract
asynchronous operation that analyzes text in a document.
You start asynchronous text analysis by calling StartDocumentAnalysis, which returns a job identifier (`JobId`). When the text analysis operation finishes, Amazon Textract
publishes a completion status to the Amazon Simple Notification Service (Amazon SNS) topic that's registered in the initial call to `StartDocumentAnalysis`. To get the results of the text-detection operation, first check that the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call `GetDocumentAnalysis`, and pass the job identifier (`JobId`) from the initial call to `StartDocumentAnalysis`.
`GetDocumentAnalysis` returns an array of Block objects. The following types of information are returned:
-
Form data (key-value pairs). The related information is returned in two Block objects, each of type `KEY_VALUE_SET`: a KEY `Block` object and a VALUE `Block` object. For example, *Name: Ana Silva Carolina* contains a key and value. Name: is the key. *Ana Silva Carolina* is the value.
-
Table and table cell data. A TABLE `Block` object contains information about a detected table. A CELL `Block` object is returned for each cell in a table.
-
Lines and words of text. A LINE `Block` object contains one or more WORD `Block` objects. All lines and words that are detected in the document are returned (including text that doesn't have a relationship with the value of the `StartDocumentAnalysis` `FeatureTypes` input parameter).
Selection elements such as check boxes and option buttons (radio buttons) can be detected in form data and in tables. A SELECTION_ELEMENT `Block` object contains information about a selection element, including the selection status.
Use the `MaxResults` parameter to limit the number of blocks that are returned. If there are more results than specified in `MaxResults`, the value of `NextToken` in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call `GetDocumentAnalysis`, and populate the `NextToken` request parameter with the token value that's returned from the previous call to `GetDocumentAnalysis`.
For more information, see [Document Text Analysis].
[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html
@option params [required, String] :job_id
A unique identifier for the text-detection job. The `JobId` is returned from `StartDocumentAnalysis`. A `JobId` value is only valid for 7 days.
@option params [Integer] :max_results
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.
@option params [String] :next_token
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.
@return [Types::GetDocumentAnalysisResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::GetDocumentAnalysisResponse#document_metadata #document_metadata} => Types::DocumentMetadata * {Types::GetDocumentAnalysisResponse#job_status #job_status} => String * {Types::GetDocumentAnalysisResponse#next_token #next_token} => String * {Types::GetDocumentAnalysisResponse#blocks #blocks} => Array<Types::Block> * {Types::GetDocumentAnalysisResponse#warnings #warnings} => Array<Types::Warning> * {Types::GetDocumentAnalysisResponse#status_message #status_message} => String * {Types::GetDocumentAnalysisResponse#analyze_document_model_version #analyze_document_model_version} => String
@example Request syntax with placeholder values
resp = client.get_document_analysis({ job_id: "JobId", # required max_results: 1, next_token: "PaginationToken", })
@example Response structure
resp.document_metadata.pages #=> Integer resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED", "PARTIAL_SUCCESS" resp.next_token #=> String resp.blocks #=> Array resp.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT" resp.blocks[0].confidence #=> Float resp.blocks[0].text #=> String resp.blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED" resp.blocks[0].row_index #=> Integer resp.blocks[0].column_index #=> Integer resp.blocks[0].row_span #=> Integer resp.blocks[0].column_span #=> Integer resp.blocks[0].geometry.bounding_box.width #=> Float resp.blocks[0].geometry.bounding_box.height #=> Float resp.blocks[0].geometry.bounding_box.left #=> Float resp.blocks[0].geometry.bounding_box.top #=> Float resp.blocks[0].geometry.polygon #=> Array resp.blocks[0].geometry.polygon[0].x #=> Float resp.blocks[0].geometry.polygon[0].y #=> Float resp.blocks[0].id #=> String resp.blocks[0].relationships #=> Array resp.blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES" resp.blocks[0].relationships[0].ids #=> Array resp.blocks[0].relationships[0].ids[0] #=> String resp.blocks[0].entity_types #=> Array resp.blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE" resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED" resp.blocks[0].page #=> Integer resp.warnings #=> Array resp.warnings[0].error_code #=> String resp.warnings[0].pages #=> Array resp.warnings[0].pages[0] #=> Integer resp.status_message #=> String resp.analyze_document_model_version #=> String
@see docs.aws.amazon.com/goto/WebAPI/textract-2018-06-27/GetDocumentAnalysis AWS API Documentation
@overload get_document_analysis
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-textract/client.rb, line 802 def get_document_analysis(params = {}, options = {}) req = build_request(:get_document_analysis, params) req.send_request(options) end
Gets the results for an Amazon Textract
asynchronous operation that detects text in a document. Amazon Textract
can detect lines of text and the words that make up a line of text.
You start asynchronous text detection by calling StartDocumentTextDetection, which returns a job identifier (`JobId`). When the text detection operation finishes, Amazon Textract
publishes a completion status to the Amazon Simple Notification Service (Amazon SNS) topic that's registered in the initial call to `StartDocumentTextDetection`. To get the results of the text-detection operation, first check that the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call `GetDocumentTextDetection`, and pass the job identifier (`JobId`) from the initial call to `StartDocumentTextDetection`.
`GetDocumentTextDetection` returns an array of Block objects.
Each document page has as an associated `Block` of type PAGE. Each PAGE `Block` object is the parent of LINE `Block` objects that represent the lines of detected text on a page. A LINE `Block` object is a parent for each word that makes up the line. Words are represented by `Block` objects of type WORD.
Use the MaxResults parameter to limit the number of blocks that are returned. If there are more results than specified in `MaxResults`, the value of `NextToken` in the operation response contains a pagination token for getting the next set of results. To get the next page of results, call `GetDocumentTextDetection`, and populate the `NextToken` request parameter with the token value that's returned from the previous call to `GetDocumentTextDetection`.
For more information, see [Document Text Detection].
[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html
@option params [required, String] :job_id
A unique identifier for the text detection job. The `JobId` is returned from `StartDocumentTextDetection`. A `JobId` value is only valid for 7 days.
@option params [Integer] :max_results
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.
@option params [String] :next_token
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.
@return [Types::GetDocumentTextDetectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::GetDocumentTextDetectionResponse#document_metadata #document_metadata} => Types::DocumentMetadata * {Types::GetDocumentTextDetectionResponse#job_status #job_status} => String * {Types::GetDocumentTextDetectionResponse#next_token #next_token} => String * {Types::GetDocumentTextDetectionResponse#blocks #blocks} => Array<Types::Block> * {Types::GetDocumentTextDetectionResponse#warnings #warnings} => Array<Types::Warning> * {Types::GetDocumentTextDetectionResponse#status_message #status_message} => String * {Types::GetDocumentTextDetectionResponse#detect_document_text_model_version #detect_document_text_model_version} => String
@example Request syntax with placeholder values
resp = client.get_document_text_detection({ job_id: "JobId", # required max_results: 1, next_token: "PaginationToken", })
@example Response structure
resp.document_metadata.pages #=> Integer resp.job_status #=> String, one of "IN_PROGRESS", "SUCCEEDED", "FAILED", "PARTIAL_SUCCESS" resp.next_token #=> String resp.blocks #=> Array resp.blocks[0].block_type #=> String, one of "KEY_VALUE_SET", "PAGE", "LINE", "WORD", "TABLE", "CELL", "SELECTION_ELEMENT" resp.blocks[0].confidence #=> Float resp.blocks[0].text #=> String resp.blocks[0].text_type #=> String, one of "HANDWRITING", "PRINTED" resp.blocks[0].row_index #=> Integer resp.blocks[0].column_index #=> Integer resp.blocks[0].row_span #=> Integer resp.blocks[0].column_span #=> Integer resp.blocks[0].geometry.bounding_box.width #=> Float resp.blocks[0].geometry.bounding_box.height #=> Float resp.blocks[0].geometry.bounding_box.left #=> Float resp.blocks[0].geometry.bounding_box.top #=> Float resp.blocks[0].geometry.polygon #=> Array resp.blocks[0].geometry.polygon[0].x #=> Float resp.blocks[0].geometry.polygon[0].y #=> Float resp.blocks[0].id #=> String resp.blocks[0].relationships #=> Array resp.blocks[0].relationships[0].type #=> String, one of "VALUE", "CHILD", "COMPLEX_FEATURES" resp.blocks[0].relationships[0].ids #=> Array resp.blocks[0].relationships[0].ids[0] #=> String resp.blocks[0].entity_types #=> Array resp.blocks[0].entity_types[0] #=> String, one of "KEY", "VALUE" resp.blocks[0].selection_status #=> String, one of "SELECTED", "NOT_SELECTED" resp.blocks[0].page #=> Integer resp.warnings #=> Array resp.warnings[0].error_code #=> String resp.warnings[0].pages #=> Array resp.warnings[0].pages[0] #=> Integer resp.status_message #=> String resp.detect_document_text_model_version #=> String
@see docs.aws.amazon.com/goto/WebAPI/textract-2018-06-27/GetDocumentTextDetection AWS API Documentation
@overload get_document_text_detection
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-textract/client.rb, line 920 def get_document_text_detection(params = {}, options = {}) req = build_request(:get_document_text_detection, params) req.send_request(options) end
Starts the asynchronous analysis of an input document for relationships between detected items such as key-value pairs, tables, and selection elements.
`StartDocumentAnalysis` can analyze text in documents that are in JPEG, PNG, and PDF format. The documents are stored in an Amazon S3 bucket. Use DocumentLocation to specify the bucket name and file name of the document.
`StartDocumentAnalysis` returns a job identifier (`JobId`) that you use to get the results of the operation. When text analysis is finished, Amazon Textract
publishes a completion status to the Amazon Simple Notification Service (Amazon SNS) topic that you specify in `NotificationChannel`. To get the results of the text analysis operation, first check that the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call GetDocumentAnalysis, and pass the job identifier (`JobId`) from the initial call to `StartDocumentAnalysis`.
For more information, see [Document Text Analysis].
[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html
@option params [required, Types::DocumentLocation] :document_location
The location of the document to be processed.
@option params [required, Array<String>] :feature_types
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`).
@option params [String] :client_request_token
The idempotent token that you use to identify the start request. If you use the same token with multiple `StartDocumentAnalysis` requests, the same `JobId` is returned. Use `ClientRequestToken` to prevent the same job from being accidentally started more than once. For more information, see [Calling Amazon Textract Asynchronous Operations][1]. [1]: https://docs.aws.amazon.com/textract/latest/dg/api-async.html
@option params [String] :job_tag
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).
@option params [Types::NotificationChannel] :notification_channel
The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to.
@option params [Types::OutputConfig] :output_config
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.
@option params [String] :kms_key_id
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.
@return [Types::StartDocumentAnalysisResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::StartDocumentAnalysisResponse#job_id #job_id} => String
@example Request syntax with placeholder values
resp = client.start_document_analysis({ document_location: { # required s3_object: { bucket: "S3Bucket", name: "S3ObjectName", version: "S3ObjectVersion", }, }, feature_types: ["TABLES"], # required, accepts TABLES, FORMS client_request_token: "ClientRequestToken", job_tag: "JobTag", notification_channel: { sns_topic_arn: "SNSTopicArn", # required role_arn: "RoleArn", # required }, output_config: { s3_bucket: "S3Bucket", # required s3_prefix: "S3ObjectName", }, kms_key_id: "KMSKeyId", })
@example Response structure
resp.job_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/textract-2018-06-27/StartDocumentAnalysis AWS API Documentation
@overload start_document_analysis
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-textract/client.rb, line 1030 def start_document_analysis(params = {}, options = {}) req = build_request(:start_document_analysis, params) req.send_request(options) end
Starts the asynchronous detection of text in a document. Amazon Textract
can detect lines of text and the words that make up a line of text.
`StartDocumentTextDetection` can analyze text in documents that are in JPEG, PNG, and PDF format. The documents are stored in an Amazon S3 bucket. Use DocumentLocation to specify the bucket name and file name of the document.
`StartTextDetection` returns a job identifier (`JobId`) that you use to get the results of the operation. When text detection is finished, Amazon Textract
publishes a completion status to the Amazon Simple Notification Service (Amazon SNS) topic that you specify in `NotificationChannel`. To get the results of the text detection operation, first check that the status value published to the Amazon SNS topic is `SUCCEEDED`. If so, call GetDocumentTextDetection, and pass the job identifier (`JobId`) from the initial call to `StartDocumentTextDetection`.
For more information, see [Document Text Detection].
[1]: docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html
@option params [required, Types::DocumentLocation] :document_location
The location of the document to be processed.
@option params [String] :client_request_token
The idempotent token that's used to identify the start request. If you use the same token with multiple `StartDocumentTextDetection` requests, the same `JobId` is returned. Use `ClientRequestToken` to prevent the same job from being accidentally started more than once. For more information, see [Calling Amazon Textract Asynchronous Operations][1]. [1]: https://docs.aws.amazon.com/textract/latest/dg/api-async.html
@option params [String] :job_tag
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).
@option params [Types::NotificationChannel] :notification_channel
The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to.
@option params [Types::OutputConfig] :output_config
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.
@option params [String] :kms_key_id
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.
@return [Types::StartDocumentTextDetectionResponse] Returns a {Seahorse::Client::Response response} object which responds to the following methods:
* {Types::StartDocumentTextDetectionResponse#job_id #job_id} => String
@example 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", })
@example Response structure
resp.job_id #=> String
@see docs.aws.amazon.com/goto/WebAPI/textract-2018-06-27/StartDocumentTextDetection AWS API Documentation
@overload start_document_text_detection
(params = {}) @param [Hash] params ({})
# File lib/aws-sdk-textract/client.rb, line 1132 def start_document_text_detection(params = {}, options = {}) req = build_request(:start_document_text_detection, params) req.send_request(options) end
@api private @deprecated
# File lib/aws-sdk-textract/client.rb, line 1156 def waiter_names [] end