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 inNotificationChannel
. To get the results of the text analysis operation, first check that the status value published to the Amazon SNS topic isSUCCEEDED
. If so, call GetDocumentAnalysis , and pass the job identifier (JobId
) from the initial call toStartDocumentAnalysis
.
For more information, see Document Text Analysis .
See also: AWS API Documentation
See ‘aws help’ for descriptions of global parameters.
start-document-analysis
--document-location <value>
--feature-types <value>
[--client-request-token <value>]
[--job-tag <value>]
[--notification-channel <value>]
[--output-config <value>]
[--kms-key-id <value>]
[--queries-config <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
--document-location
(structure)
The location of the document to be processed.
S3Object -> (structure)
The Amazon S3 bucket that contains the input document.
Bucket -> (string)
The name of the S3 bucket. Note that the # character is not valid in the file name.
Name -> (string)
The file name of the input document. Synchronous operations can use image files that are in JPEG or PNG format. Asynchronous operations also support PDF and TIFF format files.
Version -> (string)
If the bucket has versioning enabled, you can specify the object version.
Shorthand Syntax:
S3Object={Bucket=string,Name=string,Version=string}
JSON Syntax:
{
"S3Object": {
"Bucket": "string",
"Name": "string",
"Version": "string"
}
}
--feature-types
(list)
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 ofFeatureTypes
).(string)
Syntax:
"string" "string" ...
Where valid values are:
TABLES
FORMS
QUERIES
--client-request-token
(string)
The idempotent token that you use to identify the start request. If you use the same token with multiple
StartDocumentAnalysis
requests, the sameJobId
is returned. UseClientRequestToken
to prevent the same job from being accidentally started more than once. For more information, see Calling Amazon Textract Asynchronous Operations .
--job-tag
(string)
An identifier that you specify that’s included in the completion notification published to the Amazon SNS topic. For example, you can use
JobTag
to identify the type of document that the completion notification corresponds to (such as a tax form or a receipt).
--notification-channel
(structure)
The Amazon SNS topic ARN that you want Amazon Textract to publish the completion status of the operation to.
SNSTopicArn -> (string)
The Amazon SNS topic that Amazon Textract posts the completion status to.
RoleArn -> (string)
The Amazon Resource Name (ARN) of an IAM role that gives Amazon Textract publishing permissions to the Amazon SNS topic.
Shorthand Syntax:
SNSTopicArn=string,RoleArn=string
JSON Syntax:
{
"SNSTopicArn": "string",
"RoleArn": "string"
}
--output-config
(structure)
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.
S3Bucket -> (string)
The name of the bucket your output will go to.
S3Prefix -> (string)
The prefix of the object key that the output will be saved to. When not enabled, the prefix will be “textract_output”.
Shorthand Syntax:
S3Bucket=string,S3Prefix=string
JSON Syntax:
{
"S3Bucket": "string",
"S3Prefix": "string"
}
--kms-key-id
(string)
The KMS key used to encrypt the inference results. This can be in either Key ID or Key Alias format. When a KMS key is provided, the KMS key will be used for server-side encryption of the objects in the customer bucket. When this parameter is not enabled, the result will be encrypted server side,using SSE-S3.
--queries-config
(structure)
Queries -> (list)
(structure)
Each query contains the question you want to ask in the Text and the alias you want to associate.
Text -> (string)
Question that Amazon Textract will apply to the document. An example would be “What is the customer’s SSN?”
Alias -> (string)
Alias attached to the query, for ease of location.
Pages -> (list)
List of pages associated with the query. The following is a list of rules for using this parameter.
If a page is not specified, it is set to
["1"]
by default.The following characters are allowed in the parameter’s string:
0 1 2 3 4 5 6 7 8 9 - *
. No whitespace is allowed.When using
*
to indicate all pages, it must be the only element in the string.You can use page intervals, such as
[“1-3”, “1-1”, “4-*”]
. Where*
indicates last page of document.Specified pages must be greater than 0 and less than or equal to the number of pages in the document.
(string)
JSON Syntax:
{
"Queries": [
{
"Text": "string",
"Alias": "string",
"Pages": ["string", ...]
}
...
]
}
--cli-input-json
| --cli-input-yaml
(string)
Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton
. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml
.
--generate-cli-skeleton
(string)
Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input
, prints a sample input JSON that can be used as an argument for --cli-input-json
. Similarly, if provided yaml-input
it will print a sample input YAML that can be used with --cli-input-yaml
. If provided with the value output
, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.
See ‘aws help’ for descriptions of global parameters.
Note
To use the following examples, you must have the AWS CLI installed and configured. See the Getting started guide in the AWS CLI User Guide for more information.
Unless otherwise stated, all examples have unix-like quotation rules. These examples will need to be adapted to your terminal’s quoting rules. See Using quotation marks with strings in the AWS CLI User Guide .
To start analyzing text in a multi-page document
The following start-document-analysis
example shows how to start asynchronous analysis of text in a multi-page document.
Linux/macOS:
aws textract start-document-analysis \
--document-location '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \
--feature-types '["TABLES","FORMS"]' \
--notification-channel "SNSTopicArn=arn:snsTopic,RoleArn=roleArn"
Windows:
aws textract start-document-analysis \
--document-location "{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"document\"}}" \
--feature-types "[\"TABLES\", \"FORMS\"]" \
--region region-name \
--notification-channel "SNSTopicArn=arn:snsTopic,RoleArn=roleArn"
Output:
{
"JobId": "df7cf32ebbd2a5de113535fcf4d921926a701b09b4e7d089f3aebadb41e0712b"
}
For more information, see Detecting and Analyzing Text in Multi-Page Documents in the Amazon Textract Developers Guide
JobId -> (string)
The identifier for the document text detection job. Use
JobId
to identify the job in a subsequent call toGetDocumentAnalysis
. AJobId
value is only valid for 7 days.