# WARNING: DO NOT EDIT, AUTO-GENERATED CODE!
# See https://github.com/aws-beam/aws-codegen for more details.
defmodule AWS.Textract do
@moduledoc """
Amazon Textract detects and analyzes text in documents and converts it
into machine-readable text.
This is the API reference documentation for
Amazon Textract.
"""
alias AWS.Client
alias AWS.Request
@typedoc """
## Example:
analyze_document_request() :: %{
optional("AdaptersConfig") => adapters_config(),
optional("HumanLoopConfig") => human_loop_config(),
optional("QueriesConfig") => queries_config(),
required("Document") => document(),
required("FeatureTypes") => list(list(any())())
}
"""
@type analyze_document_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
document_location() :: %{
"S3Object" => s3_object()
}
"""
@type document_location() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_adapter_request() :: %{
required("AdapterId") => String.t() | atom()
}
"""
@type delete_adapter_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
adapter_version_evaluation_metric() :: %{
"AdapterVersion" => evaluation_metric(),
"Baseline" => evaluation_metric(),
"FeatureType" => list(any())
}
"""
@type adapter_version_evaluation_metric() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
tag_resource_request() :: %{
required("ResourceARN") => String.t() | atom(),
required("Tags") => map()
}
"""
@type tag_resource_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
adapter_overview() :: %{
"AdapterId" => String.t() | atom(),
"AdapterName" => String.t() | atom(),
"CreationTime" => non_neg_integer(),
"FeatureTypes" => list(list(any())())
}
"""
@type adapter_overview() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_adapter_version_request() :: %{
required("AdapterId") => String.t() | atom(),
required("AdapterVersion") => String.t() | atom()
}
"""
@type delete_adapter_version_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
identity_document_field() :: %{
"Type" => analyze_id_detections(),
"ValueDetection" => analyze_id_detections()
}
"""
@type identity_document_field() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_document_text_detection_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
required("JobId") => String.t() | atom()
}
"""
@type get_document_text_detection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_lending_analysis_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_lending_analysis_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
geometry() :: %{
"BoundingBox" => bounding_box(),
"Polygon" => list(point()),
"RotationAngle" => float()
}
"""
@type geometry() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
untag_resource_response() :: %{}
"""
@type untag_resource_response() :: %{}
@typedoc """
## Example:
analyze_id_response() :: %{
"AnalyzeIDModelVersion" => String.t() | atom(),
"DocumentMetadata" => document_metadata(),
"IdentityDocuments" => list(identity_document())
}
"""
@type analyze_id_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_document_analysis_request() :: %{
optional("AdaptersConfig") => adapters_config(),
optional("ClientRequestToken") => String.t() | atom(),
optional("JobTag") => String.t() | atom(),
optional("KMSKeyId") => String.t() | atom(),
optional("NotificationChannel") => notification_channel(),
optional("OutputConfig") => output_config(),
optional("QueriesConfig") => queries_config(),
required("DocumentLocation") => document_location(),
required("FeatureTypes") => list(list(any())())
}
"""
@type start_document_analysis_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
bad_document_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type bad_document_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
lending_document() :: %{
"LendingFields" => list(lending_field()),
"SignatureDetections" => list(signature_detection())
}
"""
@type lending_document() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_expense_analysis_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_expense_analysis_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
unsupported_document_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type unsupported_document_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detected_signature() :: %{
"Page" => integer()
}
"""
@type detected_signature() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
analyze_id_request() :: %{
required("DocumentPages") => list(document())
}
"""
@type analyze_id_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_adapter_versions_request() :: %{
optional("AdapterId") => String.t() | atom(),
optional("AfterCreationTime") => non_neg_integer(),
optional("BeforeCreationTime") => non_neg_integer(),
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom()
}
"""
@type list_adapter_versions_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
relationship() :: %{
"Ids" => list(String.t() | atom()),
"Type" => list(any())
}
"""
@type relationship() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_expense_analysis_response() :: %{
"AnalyzeExpenseModelVersion" => String.t() | atom(),
"DocumentMetadata" => document_metadata(),
"ExpenseDocuments" => list(expense_document()),
"JobStatus" => list(any()),
"NextToken" => String.t() | atom(),
"StatusMessage" => String.t() | atom(),
"Warnings" => list(warning())
}
"""
@type get_expense_analysis_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_document_text_request() :: %{
required("Document") => document()
}
"""
@type detect_document_text_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_lending_analysis_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("JobTag") => String.t() | atom(),
optional("KMSKeyId") => String.t() | atom(),
optional("NotificationChannel") => notification_channel(),
optional("OutputConfig") => output_config(),
required("DocumentLocation") => document_location()
}
"""
@type start_lending_analysis_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
line_item_fields() :: %{
"LineItemExpenseFields" => list(expense_field())
}
"""
@type line_item_fields() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
adapter() :: %{
"AdapterId" => String.t() | atom(),
"Pages" => list(String.t() | atom()),
"Version" => String.t() | atom()
}
"""
@type adapter() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
s3_object() :: %{
"Bucket" => String.t() | atom(),
"Name" => String.t() | atom(),
"Version" => String.t() | atom()
}
"""
@type s3_object() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
analyze_expense_response() :: %{
"DocumentMetadata" => document_metadata(),
"ExpenseDocuments" => list(expense_document())
}
"""
@type analyze_expense_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
adapters_config() :: %{
"Adapters" => list(adapter())
}
"""
@type adapters_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
line_item_group() :: %{
"LineItemGroupIndex" => integer(),
"LineItems" => list(line_item_fields())
}
"""
@type line_item_group() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_lending_analysis_summary_request() :: %{
required("JobId") => String.t() | atom()
}
"""
@type get_lending_analysis_summary_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
analyze_document_response() :: %{
"AnalyzeDocumentModelVersion" => String.t() | atom(),
"Blocks" => list(block()),
"DocumentMetadata" => document_metadata(),
"HumanLoopActivationOutput" => human_loop_activation_output()
}
"""
@type analyze_document_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
untag_resource_request() :: %{
required("ResourceARN") => String.t() | atom(),
required("TagKeys") => list(String.t() | atom())
}
"""
@type untag_resource_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_document_text_detection_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("JobTag") => String.t() | atom(),
optional("KMSKeyId") => String.t() | atom(),
optional("NotificationChannel") => notification_channel(),
optional("OutputConfig") => output_config(),
required("DocumentLocation") => document_location()
}
"""
@type start_document_text_detection_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_document_text_detection_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_document_text_detection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
expense_group_property() :: %{
"Id" => String.t() | atom(),
"Types" => list(String.t() | atom())
}
"""
@type expense_group_property() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_lending_analysis_summary_response() :: %{
"AnalyzeLendingModelVersion" => String.t() | atom(),
"DocumentMetadata" => document_metadata(),
"JobStatus" => list(any()),
"StatusMessage" => String.t() | atom(),
"Summary" => lending_summary(),
"Warnings" => list(warning())
}
"""
@type get_lending_analysis_summary_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
expense_document() :: %{
"Blocks" => list(block()),
"ExpenseIndex" => integer(),
"LineItemGroups" => list(line_item_group()),
"SummaryFields" => list(expense_field())
}
"""
@type expense_document() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
normalized_value() :: %{
"Value" => String.t() | atom(),
"ValueType" => list(any())
}
"""
@type normalized_value() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_adapter_request() :: %{
optional("AutoUpdate") => list(any()),
optional("ClientRequestToken") => String.t() | atom(),
optional("Description") => String.t() | atom(),
optional("Tags") => map(),
required("AdapterName") => String.t() | atom(),
required("FeatureTypes") => list(list(any())())
}
"""
@type create_adapter_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
split_document() :: %{
"Index" => integer(),
"Pages" => list(integer())
}
"""
@type split_document() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
evaluation_metric() :: %{
"F1Score" => float(),
"Precision" => float(),
"Recall" => float()
}
"""
@type evaluation_metric() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_lending_analysis_response() :: %{
"AnalyzeLendingModelVersion" => String.t() | atom(),
"DocumentMetadata" => document_metadata(),
"JobStatus" => list(any()),
"NextToken" => String.t() | atom(),
"Results" => list(lending_result()),
"StatusMessage" => String.t() | atom(),
"Warnings" => list(warning())
}
"""
@type get_lending_analysis_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
notification_channel() :: %{
"RoleArn" => String.t() | atom(),
"SNSTopicArn" => String.t() | atom()
}
"""
@type notification_channel() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
point() :: %{
"X" => float(),
"Y" => float()
}
"""
@type point() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
human_loop_activation_output() :: %{
"HumanLoopActivationConditionsEvaluationResults" => String.t() | atom(),
"HumanLoopActivationReasons" => list(String.t() | atom()),
"HumanLoopArn" => String.t() | atom()
}
"""
@type human_loop_activation_output() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
provisioned_throughput_exceeded_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type provisioned_throughput_exceeded_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_adapter_version_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("KMSKeyId") => String.t() | atom(),
optional("Tags") => map(),
required("AdapterId") => String.t() | atom(),
required("DatasetConfig") => adapter_version_dataset_config(),
required("OutputConfig") => output_config()
}
"""
@type create_adapter_version_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
conflict_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type conflict_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
resource_not_found_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type resource_not_found_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
adapter_version_dataset_config() :: %{
"ManifestS3Object" => s3_object()
}
"""
@type adapter_version_dataset_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
adapter_version_overview() :: %{
"AdapterId" => String.t() | atom(),
"AdapterVersion" => String.t() | atom(),
"CreationTime" => non_neg_integer(),
"FeatureTypes" => list(list(any())()),
"Status" => list(any()),
"StatusMessage" => String.t() | atom()
}
"""
@type adapter_version_overview() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_adapter_response() :: %{
"AdapterId" => String.t() | atom(),
"AdapterName" => String.t() | atom(),
"AutoUpdate" => list(any()),
"CreationTime" => non_neg_integer(),
"Description" => String.t() | atom(),
"FeatureTypes" => list(list(any())()),
"Tags" => map()
}
"""
@type get_adapter_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
service_quota_exceeded_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type service_quota_exceeded_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_document_text_detection_response() :: %{
"Blocks" => list(block()),
"DetectDocumentTextModelVersion" => String.t() | atom(),
"DocumentMetadata" => document_metadata(),
"JobStatus" => list(any()),
"NextToken" => String.t() | atom(),
"StatusMessage" => String.t() | atom(),
"Warnings" => list(warning())
}
"""
@type get_document_text_detection_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
idempotent_parameter_mismatch_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type idempotent_parameter_mismatch_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_expense_analysis_request() :: %{
optional("ClientRequestToken") => String.t() | atom(),
optional("JobTag") => String.t() | atom(),
optional("KMSKeyId") => String.t() | atom(),
optional("NotificationChannel") => notification_channel(),
optional("OutputConfig") => output_config(),
required("DocumentLocation") => document_location()
}
"""
@type start_expense_analysis_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
lending_field() :: %{
"KeyDetection" => lending_detection(),
"Type" => String.t() | atom(),
"ValueDetections" => list(lending_detection())
}
"""
@type lending_field() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
undetected_signature() :: %{
"Page" => integer()
}
"""
@type undetected_signature() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_tags_for_resource_response() :: %{
"Tags" => map()
}
"""
@type list_tags_for_resource_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_adapter_request() :: %{
required("AdapterId") => String.t() | atom()
}
"""
@type get_adapter_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
queries_config() :: %{
"Queries" => list(query())
}
"""
@type queries_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_adapter_version_request() :: %{
required("AdapterId") => String.t() | atom(),
required("AdapterVersion") => String.t() | atom()
}
"""
@type get_adapter_version_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
signature_detection() :: %{
"Confidence" => float(),
"Geometry" => geometry()
}
"""
@type signature_detection() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_lending_analysis_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
required("JobId") => String.t() | atom()
}
"""
@type get_lending_analysis_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
invalid_job_id_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type invalid_job_id_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
invalid_kms_key_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type invalid_kms_key_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
expense_detection() :: %{
"Confidence" => float(),
"Geometry" => geometry(),
"Text" => String.t() | atom()
}
"""
@type expense_detection() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
human_loop_data_attributes() :: %{
"ContentClassifiers" => list(list(any())())
}
"""
@type human_loop_data_attributes() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
bounding_box() :: %{
"Height" => float(),
"Left" => float(),
"Top" => float(),
"Width" => float()
}
"""
@type bounding_box() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_expense_analysis_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
required("JobId") => String.t() | atom()
}
"""
@type get_expense_analysis_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
analyze_id_detections() :: %{
"Confidence" => float(),
"NormalizedValue" => normalized_value(),
"Text" => String.t() | atom()
}
"""
@type analyze_id_detections() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
expense_currency() :: %{
"Code" => String.t() | atom(),
"Confidence" => float()
}
"""
@type expense_currency() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_adapter_versions_response() :: %{
"AdapterVersions" => list(adapter_version_overview()),
"NextToken" => String.t() | atom()
}
"""
@type list_adapter_versions_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
expense_type() :: %{
"Confidence" => float(),
"Text" => String.t() | atom()
}
"""
@type expense_type() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
warning() :: %{
"ErrorCode" => String.t() | atom(),
"Pages" => list(integer())
}
"""
@type warning() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
internal_server_error() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type internal_server_error() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
access_denied_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type access_denied_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
invalid_parameter_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type invalid_parameter_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_document_analysis_request() :: %{
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom(),
required("JobId") => String.t() | atom()
}
"""
@type get_document_analysis_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
tag_resource_response() :: %{}
"""
@type tag_resource_response() :: %{}
@typedoc """
## Example:
output_config() :: %{
"S3Bucket" => String.t() | atom(),
"S3Prefix" => String.t() | atom()
}
"""
@type output_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_document_analysis_response() :: %{
"AnalyzeDocumentModelVersion" => String.t() | atom(),
"Blocks" => list(block()),
"DocumentMetadata" => document_metadata(),
"JobStatus" => list(any()),
"NextToken" => String.t() | atom(),
"StatusMessage" => String.t() | atom(),
"Warnings" => list(warning())
}
"""
@type get_document_analysis_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
lending_detection() :: %{
"Confidence" => float(),
"Geometry" => geometry(),
"SelectionStatus" => list(any()),
"Text" => String.t() | atom()
}
"""
@type lending_detection() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
validation_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type validation_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_tags_for_resource_request() :: %{
required("ResourceARN") => String.t() | atom()
}
"""
@type list_tags_for_resource_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_adapter_version_response() :: %{
"AdapterId" => String.t() | atom(),
"AdapterVersion" => String.t() | atom()
}
"""
@type create_adapter_version_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
analyze_expense_request() :: %{
required("Document") => document()
}
"""
@type analyze_expense_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
document_metadata() :: %{
"Pages" => integer()
}
"""
@type document_metadata() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
throttling_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type throttling_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
page_classification() :: %{
"PageNumber" => list(prediction()),
"PageType" => list(prediction())
}
"""
@type page_classification() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
create_adapter_response() :: %{
"AdapterId" => String.t() | atom()
}
"""
@type create_adapter_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
limit_exceeded_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type limit_exceeded_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
human_loop_quota_exceeded_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom(),
"QuotaCode" => String.t() | atom(),
"ResourceType" => String.t() | atom(),
"ServiceCode" => String.t() | atom()
}
"""
@type human_loop_quota_exceeded_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
extraction() :: %{
"ExpenseDocument" => expense_document(),
"IdentityDocument" => identity_document(),
"LendingDocument" => lending_document()
}
"""
@type extraction() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
document_group() :: %{
"DetectedSignatures" => list(detected_signature()),
"SplitDocuments" => list(split_document()),
"Type" => String.t() | atom(),
"UndetectedSignatures" => list(undetected_signature())
}
"""
@type document_group() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
expense_field() :: %{
"Currency" => expense_currency(),
"GroupProperties" => list(expense_group_property()),
"LabelDetection" => expense_detection(),
"PageNumber" => integer(),
"Type" => expense_type(),
"ValueDetection" => expense_detection()
}
"""
@type expense_field() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
query() :: %{
"Alias" => String.t() | atom(),
"Pages" => list(String.t() | atom()),
"Text" => String.t() | atom()
}
"""
@type query() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
get_adapter_version_response() :: %{
"AdapterId" => String.t() | atom(),
"AdapterVersion" => String.t() | atom(),
"CreationTime" => non_neg_integer(),
"DatasetConfig" => adapter_version_dataset_config(),
"EvaluationMetrics" => list(adapter_version_evaluation_metric()),
"FeatureTypes" => list(list(any())()),
"KMSKeyId" => String.t() | atom(),
"OutputConfig" => output_config(),
"Status" => list(any()),
"StatusMessage" => String.t() | atom(),
"Tags" => map()
}
"""
@type get_adapter_version_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
start_document_analysis_response() :: %{
"JobId" => String.t() | atom()
}
"""
@type start_document_analysis_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
detect_document_text_response() :: %{
"Blocks" => list(block()),
"DetectDocumentTextModelVersion" => String.t() | atom(),
"DocumentMetadata" => document_metadata()
}
"""
@type detect_document_text_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
identity_document() :: %{
"Blocks" => list(block()),
"DocumentIndex" => integer(),
"IdentityDocumentFields" => list(identity_document_field())
}
"""
@type identity_document() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
invalid_s3_object_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type invalid_s3_object_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
update_adapter_request() :: %{
optional("AdapterName") => String.t() | atom(),
optional("AutoUpdate") => list(any()),
optional("Description") => String.t() | atom(),
required("AdapterId") => String.t() | atom()
}
"""
@type update_adapter_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
block() :: %{
"BlockType" => list(any()),
"ColumnIndex" => integer(),
"ColumnSpan" => integer(),
"Confidence" => float(),
"EntityTypes" => list(list(any())()),
"Geometry" => geometry(),
"Id" => String.t() | atom(),
"Page" => integer(),
"Query" => query(),
"Relationships" => list(relationship()),
"RowIndex" => integer(),
"RowSpan" => integer(),
"SelectionStatus" => list(any()),
"Text" => String.t() | atom(),
"TextType" => list(any())
}
"""
@type block() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_adapters_response() :: %{
"Adapters" => list(adapter_overview()),
"NextToken" => String.t() | atom()
}
"""
@type list_adapters_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
document_too_large_exception() :: %{
"Code" => String.t() | atom(),
"Message" => String.t() | atom()
}
"""
@type document_too_large_exception() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
human_loop_config() :: %{
"DataAttributes" => human_loop_data_attributes(),
"FlowDefinitionArn" => String.t() | atom(),
"HumanLoopName" => String.t() | atom()
}
"""
@type human_loop_config() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_adapter_response() :: %{}
"""
@type delete_adapter_response() :: %{}
@typedoc """
## Example:
lending_summary() :: %{
"DocumentGroups" => list(document_group()),
"UndetectedDocumentTypes" => list(String.t() | atom())
}
"""
@type lending_summary() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
update_adapter_response() :: %{
"AdapterId" => String.t() | atom(),
"AdapterName" => String.t() | atom(),
"AutoUpdate" => list(any()),
"CreationTime" => non_neg_integer(),
"Description" => String.t() | atom(),
"FeatureTypes" => list(list(any())())
}
"""
@type update_adapter_response() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
prediction() :: %{
"Confidence" => float(),
"Value" => String.t() | atom()
}
"""
@type prediction() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
document() :: %{
"Bytes" => binary(),
"S3Object" => s3_object()
}
"""
@type document() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
list_adapters_request() :: %{
optional("AfterCreationTime") => non_neg_integer(),
optional("BeforeCreationTime") => non_neg_integer(),
optional("MaxResults") => integer(),
optional("NextToken") => String.t() | atom()
}
"""
@type list_adapters_request() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
lending_result() :: %{
"Extractions" => list(extraction()),
"Page" => integer(),
"PageClassification" => page_classification()
}
"""
@type lending_result() :: %{(String.t() | atom()) => any()}
@typedoc """
## Example:
delete_adapter_version_response() :: %{}
"""
@type delete_adapter_version_response() :: %{}
@type analyze_document_errors() ::
document_too_large_exception()
| invalid_s3_object_exception()
| human_loop_quota_exceeded_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| provisioned_throughput_exceeded_exception()
| unsupported_document_exception()
| bad_document_exception()
@type analyze_expense_errors() ::
document_too_large_exception()
| invalid_s3_object_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| provisioned_throughput_exceeded_exception()
| unsupported_document_exception()
| bad_document_exception()
@type analyze_id_errors() ::
document_too_large_exception()
| invalid_s3_object_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| provisioned_throughput_exceeded_exception()
| unsupported_document_exception()
| bad_document_exception()
@type create_adapter_errors() ::
limit_exceeded_exception()
| throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| idempotent_parameter_mismatch_exception()
| service_quota_exceeded_exception()
| conflict_exception()
| provisioned_throughput_exceeded_exception()
@type create_adapter_version_errors() ::
invalid_s3_object_exception()
| limit_exceeded_exception()
| throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| invalid_kms_key_exception()
| idempotent_parameter_mismatch_exception()
| service_quota_exceeded_exception()
| resource_not_found_exception()
| conflict_exception()
| provisioned_throughput_exceeded_exception()
@type delete_adapter_errors() ::
throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| resource_not_found_exception()
| conflict_exception()
| provisioned_throughput_exceeded_exception()
@type delete_adapter_version_errors() ::
throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| resource_not_found_exception()
| conflict_exception()
| provisioned_throughput_exceeded_exception()
@type detect_document_text_errors() ::
document_too_large_exception()
| invalid_s3_object_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| provisioned_throughput_exceeded_exception()
| unsupported_document_exception()
| bad_document_exception()
@type get_adapter_errors() ::
throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| resource_not_found_exception()
| provisioned_throughput_exceeded_exception()
@type get_adapter_version_errors() ::
throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| resource_not_found_exception()
| provisioned_throughput_exceeded_exception()
@type get_document_analysis_errors() ::
invalid_s3_object_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| invalid_kms_key_exception()
| invalid_job_id_exception()
| provisioned_throughput_exceeded_exception()
@type get_document_text_detection_errors() ::
invalid_s3_object_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| invalid_kms_key_exception()
| invalid_job_id_exception()
| provisioned_throughput_exceeded_exception()
@type get_expense_analysis_errors() ::
invalid_s3_object_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| invalid_kms_key_exception()
| invalid_job_id_exception()
| provisioned_throughput_exceeded_exception()
@type get_lending_analysis_errors() ::
invalid_s3_object_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| invalid_kms_key_exception()
| invalid_job_id_exception()
| provisioned_throughput_exceeded_exception()
@type get_lending_analysis_summary_errors() ::
invalid_s3_object_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| invalid_kms_key_exception()
| invalid_job_id_exception()
| provisioned_throughput_exceeded_exception()
@type list_adapter_versions_errors() ::
throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| resource_not_found_exception()
| provisioned_throughput_exceeded_exception()
@type list_adapters_errors() ::
throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| provisioned_throughput_exceeded_exception()
@type list_tags_for_resource_errors() ::
throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| resource_not_found_exception()
| provisioned_throughput_exceeded_exception()
@type start_document_analysis_errors() ::
document_too_large_exception()
| invalid_s3_object_exception()
| limit_exceeded_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| invalid_kms_key_exception()
| idempotent_parameter_mismatch_exception()
| provisioned_throughput_exceeded_exception()
| unsupported_document_exception()
| bad_document_exception()
@type start_document_text_detection_errors() ::
document_too_large_exception()
| invalid_s3_object_exception()
| limit_exceeded_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| invalid_kms_key_exception()
| idempotent_parameter_mismatch_exception()
| provisioned_throughput_exceeded_exception()
| unsupported_document_exception()
| bad_document_exception()
@type start_expense_analysis_errors() ::
document_too_large_exception()
| invalid_s3_object_exception()
| limit_exceeded_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| invalid_kms_key_exception()
| idempotent_parameter_mismatch_exception()
| provisioned_throughput_exceeded_exception()
| unsupported_document_exception()
| bad_document_exception()
@type start_lending_analysis_errors() ::
document_too_large_exception()
| invalid_s3_object_exception()
| limit_exceeded_exception()
| throttling_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| invalid_kms_key_exception()
| idempotent_parameter_mismatch_exception()
| provisioned_throughput_exceeded_exception()
| unsupported_document_exception()
| bad_document_exception()
@type tag_resource_errors() ::
throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| service_quota_exceeded_exception()
| resource_not_found_exception()
| provisioned_throughput_exceeded_exception()
@type untag_resource_errors() ::
throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| resource_not_found_exception()
| provisioned_throughput_exceeded_exception()
@type update_adapter_errors() ::
throttling_exception()
| validation_exception()
| invalid_parameter_exception()
| access_denied_exception()
| internal_server_error()
| resource_not_found_exception()
| conflict_exception()
| provisioned_throughput_exceeded_exception()
def metadata do
%{
api_version: "2018-06-27",
content_type: "application/x-amz-json-1.1",
credential_scope: nil,
endpoint_prefix: "textract",
global?: false,
hostname: nil,
protocol: "json",
service_id: "Textract",
signature_version: "v4",
signing_name: "textract",
target_prefix: "Textract"
}
end
@doc """
Analyzes an input document for relationships between detected items.
The types of information returned are as follows:
*
Form data (key-value pairs). The related information is returned in two `Block`
objects, each of type `KEY_VALUE_SET`: a KEY
`Block` object and a VALUE `Block` object. For example,
*Name: Ana Silva Carolina* contains a key and value.
*Name:* is the key. *Ana Silva Carolina* is
the value.
*
Table and table cell data. A TABLE `Block` object contains information
about a detected table. A CELL `Block` object is returned for each cell in
a table.
*
Lines and words of text. A LINE `Block` object contains one or more
WORD `Block` objects. All lines and words that are detected in the
document are returned (including text that doesn't have a relationship with the
value
of `FeatureTypes`).
*
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](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html).
"""
@spec analyze_document(map(), analyze_document_request(), list()) ::
{:ok, analyze_document_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, analyze_document_errors()}
def analyze_document(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "AnalyzeDocument", input, options)
end
@doc """
`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.
"""
@spec analyze_expense(map(), analyze_expense_request(), list()) ::
{:ok, analyze_expense_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, analyze_expense_errors()}
def analyze_expense(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "AnalyzeExpense", input, options)
end
@doc """
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.
"""
@spec analyze_id(map(), analyze_id_request(), list()) ::
{:ok, analyze_id_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, analyze_id_errors()}
def analyze_id(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "AnalyzeID", input, options)
end
@doc """
Creates an adapter, which can be fine-tuned for enhanced performance on user
provided
documents.
Takes an AdapterName and FeatureType. Currently the only supported feature type
is `QUERIES`. You can also provide a Description, Tags, and a
ClientRequestToken. You can choose whether or not the adapter should be
AutoUpdated with
the AutoUpdate argument. By default, AutoUpdate is set to DISABLED.
"""
@spec create_adapter(map(), create_adapter_request(), list()) ::
{:ok, create_adapter_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, create_adapter_errors()}
def create_adapter(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "CreateAdapter", input, options)
end
@doc """
Creates a new version of an adapter.
Operates on a provided AdapterId and a specified
dataset provided via the DatasetConfig argument. Requires that you
specify an Amazon S3 bucket with the OutputConfig argument. You can provide an
optional KMSKeyId,
an optional ClientRequestToken, and optional tags.
"""
@spec create_adapter_version(map(), create_adapter_version_request(), list()) ::
{:ok, create_adapter_version_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, create_adapter_version_errors()}
def create_adapter_version(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "CreateAdapterVersion", input, options)
end
@doc """
Deletes an Amazon Textract adapter.
Takes an AdapterId and deletes the adapter specified by the ID.
"""
@spec delete_adapter(map(), delete_adapter_request(), list()) ::
{:ok, delete_adapter_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, delete_adapter_errors()}
def delete_adapter(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "DeleteAdapter", input, options)
end
@doc """
Deletes an Amazon Textract adapter version.
Requires that you specify both an AdapterId and a
AdapterVersion. Deletes the adapter version specified by the AdapterId and the
AdapterVersion.
"""
@spec delete_adapter_version(map(), delete_adapter_version_request(), list()) ::
{:ok, delete_adapter_version_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, delete_adapter_version_errors()}
def delete_adapter_version(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "DeleteAdapterVersion", input, options)
end
@doc """
Detects text in the input document.
Amazon Textract can detect lines of text and the
words that make up a line of text. The input document must be 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](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html).
"""
@spec detect_document_text(map(), detect_document_text_request(), list()) ::
{:ok, detect_document_text_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, detect_document_text_errors()}
def detect_document_text(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "DetectDocumentText", input, options)
end
@doc """
Gets configuration information for an adapter specified by an AdapterId,
returning information on AdapterName, Description,
CreationTime, AutoUpdate status, and FeatureTypes.
"""
@spec get_adapter(map(), get_adapter_request(), list()) ::
{:ok, get_adapter_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_adapter_errors()}
def get_adapter(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "GetAdapter", input, options)
end
@doc """
Gets configuration information for the specified adapter version, including:
AdapterId, AdapterVersion, FeatureTypes, Status, StatusMessage, DatasetConfig,
KMSKeyId, OutputConfig, Tags and EvaluationMetrics.
"""
@spec get_adapter_version(map(), get_adapter_version_request(), list()) ::
{:ok, get_adapter_version_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_adapter_version_errors()}
def get_adapter_version(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "GetAdapterVersion", input, options)
end
@doc """
Gets the results for an Amazon Textract asynchronous operation that analyzes
text in a
document.
You start asynchronous text analysis by calling `StartDocumentAnalysis`,
which returns a job identifier (`JobId`). When the text analysis operation
finishes, Amazon Textract publishes a completion status to the Amazon Simple
Notification Service (Amazon SNS) topic
that's registered in the initial call to `StartDocumentAnalysis`. To get the
results of the text-detection operation, first check that the status value
published to the
Amazon SNS topic is `SUCCEEDED`. If so, call `GetDocumentAnalysis`, and
pass the job identifier (`JobId`) from the initial call to
`StartDocumentAnalysis`.
`GetDocumentAnalysis` returns an array of `Block` objects.
The following types of information are returned:
*
Form data (key-value pairs). The related information is returned in two `Block`
objects, each of type `KEY_VALUE_SET`: a KEY
`Block` object and a VALUE `Block` object. For example,
*Name: Ana Silva Carolina* contains a key and value.
*Name:* is the key. *Ana Silva Carolina* is
the value.
*
Table and table cell data. A TABLE `Block` object contains information
about a detected table. A CELL `Block` object is returned for each cell in
a table.
*
Lines and words of text. A LINE `Block` object contains one or more
WORD `Block` objects. All lines and words that are detected in the
document are returned (including text that doesn't have a relationship with the
value
of the `StartDocumentAnalysis`
`FeatureTypes` input parameter).
*
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](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html).
"""
@spec get_document_analysis(map(), get_document_analysis_request(), list()) ::
{:ok, get_document_analysis_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_document_analysis_errors()}
def get_document_analysis(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "GetDocumentAnalysis", input, options)
end
@doc """
Gets the results for an Amazon Textract asynchronous operation that detects text
in a document.
Amazon Textract can detect lines of text and the words that make up a line of
text.
You start asynchronous text detection by calling `StartDocumentTextDetection`,
which returns a job identifier
(`JobId`). When the text detection operation finishes, Amazon Textract publishes
a
completion status to the Amazon Simple Notification Service (Amazon SNS) topic
that's registered in the initial call to
`StartDocumentTextDetection`. To get the results of the text-detection
operation, first check that the status value published to the Amazon SNS topic
is
`SUCCEEDED`. If so, call `GetDocumentTextDetection`, and pass the
job identifier (`JobId`) from the initial call to
`StartDocumentTextDetection`.
`GetDocumentTextDetection` returns an array of `Block`
objects.
Each document page has as an associated `Block` of type PAGE. Each PAGE `Block`
object
is the parent of LINE `Block` objects that represent the lines of detected text
on a page. A LINE `Block` object is
a parent for each word that makes up the line. Words are represented by `Block`
objects of type WORD.
Use the MaxResults parameter to limit the number of blocks that are returned. If
there
are more results than specified in `MaxResults`, the value of
`NextToken` in the operation response contains a pagination token for getting
the next set of results. To get the next page of results, call
`GetDocumentTextDetection`, and populate the `NextToken` request
parameter with the token value that's returned from the previous call to
`GetDocumentTextDetection`.
For more information, see [Document Text Detection](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html).
"""
@spec get_document_text_detection(map(), get_document_text_detection_request(), list()) ::
{:ok, get_document_text_detection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_document_text_detection_errors()}
def get_document_text_detection(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "GetDocumentTextDetection", input, options)
end
@doc """
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](https://docs.aws.amazon.com/textract/latest/dg/invoices-receipts.html).
"""
@spec get_expense_analysis(map(), get_expense_analysis_request(), list()) ::
{:ok, get_expense_analysis_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_expense_analysis_errors()}
def get_expense_analysis(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "GetExpenseAnalysis", input, options)
end
@doc """
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`.
"""
@spec get_lending_analysis(map(), get_lending_analysis_request(), list()) ::
{:ok, get_lending_analysis_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_lending_analysis_errors()}
def get_lending_analysis(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "GetLendingAnalysis", input, options)
end
@doc """
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`.
"""
@spec get_lending_analysis_summary(map(), get_lending_analysis_summary_request(), list()) ::
{:ok, get_lending_analysis_summary_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, get_lending_analysis_summary_errors()}
def get_lending_analysis_summary(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "GetLendingAnalysisSummary", input, options)
end
@doc """
List all version of an adapter that meet the specified filtration criteria.
"""
@spec list_adapter_versions(map(), list_adapter_versions_request(), list()) ::
{:ok, list_adapter_versions_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_adapter_versions_errors()}
def list_adapter_versions(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "ListAdapterVersions", input, options)
end
@doc """
Lists all adapters that match the specified filtration criteria.
"""
@spec list_adapters(map(), list_adapters_request(), list()) ::
{:ok, list_adapters_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_adapters_errors()}
def list_adapters(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "ListAdapters", input, options)
end
@doc """
Lists all tags for an Amazon Textract resource.
"""
@spec list_tags_for_resource(map(), list_tags_for_resource_request(), list()) ::
{:ok, list_tags_for_resource_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, list_tags_for_resource_errors()}
def list_tags_for_resource(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "ListTagsForResource", input, options)
end
@doc """
Starts the asynchronous analysis of an input document for relationships between
detected
items such as key-value pairs, tables, and selection elements.
`StartDocumentAnalysis` can analyze text in documents that are in JPEG, PNG,
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](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html).
"""
@spec start_document_analysis(map(), start_document_analysis_request(), list()) ::
{:ok, start_document_analysis_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_document_analysis_errors()}
def start_document_analysis(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "StartDocumentAnalysis", input, options)
end
@doc """
Starts the asynchronous detection of text in a document.
Amazon Textract can detect lines of
text and the words that make up a line of text.
`StartDocumentTextDetection` can analyze text in documents that are in JPEG,
PNG, 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.
`StartDocumentTextDetection` returns a job identifier
(`JobId`) that you use to get the results of the operation. When text
detection is finished, Amazon Textract publishes a completion status to the
Amazon Simple Notification Service (Amazon SNS)
topic that you specify in `NotificationChannel`. To get the results of the text
detection operation, first check that the status value published to the Amazon
SNS topic is
`SUCCEEDED`. If so, call `GetDocumentTextDetection`, and
pass the job identifier (`JobId`) from the initial call to
`StartDocumentTextDetection`.
For more information, see [Document Text Detection](https://docs.aws.amazon.com/textract/latest/dg/how-it-works-detecting.html).
"""
@spec start_document_text_detection(map(), start_document_text_detection_request(), list()) ::
{:ok, start_document_text_detection_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_document_text_detection_errors()}
def start_document_text_detection(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "StartDocumentTextDetection", input, options)
end
@doc """
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](https://docs.aws.amazon.com/textract/latest/dg/invoice-receipts.html).
"""
@spec start_expense_analysis(map(), start_expense_analysis_request(), list()) ::
{:ok, start_expense_analysis_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_expense_analysis_errors()}
def start_expense_analysis(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "StartExpenseAnalysis", input, options)
end
@doc """
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)
"""
@spec start_lending_analysis(map(), start_lending_analysis_request(), list()) ::
{:ok, start_lending_analysis_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, start_lending_analysis_errors()}
def start_lending_analysis(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "StartLendingAnalysis", input, options)
end
@doc """
Adds one or more tags to the specified resource.
"""
@spec tag_resource(map(), tag_resource_request(), list()) ::
{:ok, tag_resource_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, tag_resource_errors()}
def tag_resource(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "TagResource", input, options)
end
@doc """
Removes any tags with the specified keys from the specified resource.
"""
@spec untag_resource(map(), untag_resource_request(), list()) ::
{:ok, untag_resource_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, untag_resource_errors()}
def untag_resource(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "UntagResource", input, options)
end
@doc """
Update the configuration for an adapter.
FeatureTypes configurations cannot be updated.
At least one new parameter must be specified as an argument.
"""
@spec update_adapter(map(), update_adapter_request(), list()) ::
{:ok, update_adapter_response(), any()}
| {:error, {:unexpected_response, any()}}
| {:error, term()}
| {:error, update_adapter_errors()}
def update_adapter(%Client{} = client, input, options \\ []) do
meta = metadata()
Request.request_post(client, meta, "UpdateAdapter", input, options)
end
end
