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Class: Aws::Textract::Types::AnalyzeDocumentRequest
- Inherits:
-
Struct
- Object
- Struct
- Aws::Textract::Types::AnalyzeDocumentRequest
- Defined in:
- (unknown)
Overview
When passing AnalyzeDocumentRequest as input to an Aws::Client method, you can use a vanilla Hash:
{
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
},
},
}
Instance Attribute Summary collapse
-
#document ⇒ Types::Document
The input document as base64-encoded bytes or an Amazon S3 object.
-
#feature_types ⇒ Array<String>
A list of the types of analysis to perform.
-
#human_loop_config ⇒ Types::HumanLoopConfig
Sets the configuration for the human in the loop workflow for analyzing documents.
Instance Attribute Details
#document ⇒ Types::Document
The input document as base64-encoded bytes or an Amazon S3 object. If you use the AWS CLI to call Amazon Textract operations, you can\'t pass image bytes. The document must be an image in JPEG or PNG format.
If you\'re using an AWS SDK to call Amazon Textract, you might not need
to base64-encode image bytes that are passed using the Bytes
field.
#feature_types ⇒ Array<String>
A list of the types of analysis to perform. Add TABLES to the list to
return information about the tables that are detected in the input
document. Add FORMS to return detected form data. To perform both types
of analysis, add TABLES and FORMS to FeatureTypes
. All lines and words
detected in the document are included in the response (including text
that isn\'t related to the value of FeatureTypes
).
#human_loop_config ⇒ Types::HumanLoopConfig
Sets the configuration for the human in the loop workflow for analyzing documents.