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  • Tonic Textual guide
  • Getting started with Textual
  • Previewing Textual detection and redaction
  • Entity types that Textual detects
    • Built-in entity types
    • Managing custom entity types
  • Language support in Textual
  • Datasets - Create redacted files
    • Datasets workflow for text redaction
    • Creating and managing datasets
    • Assigning tags to datasets
    • Displaying the file manager
    • Adding and removing dataset files
    • Reviewing the sensitivity detection results
    • Configuring the redaction
      • Configuring added and excluded values for built-in entity types
      • Working with custom entity types
      • Selecting the handling option for entity types
      • Configuring synthesis options
      • Configuring handling of file components
    • Adding manual overrides to PDF files
      • Editing an individual PDF file
      • Creating templates to apply to PDF files
    • Sharing dataset access
    • Previewing the original and redacted data in a file
    • Downloading redacted data
  • Pipelines - Prepare LLM content
    • Pipelines workflow for LLM preparation
    • Viewing pipeline lists and details
    • Assigning tags to pipelines
    • Setting up pipelines
      • Creating and editing pipelines
      • Supported file types for pipelines
      • Creating custom entity types from a pipeline
      • Configuring file synthesis for a pipeline
      • Configuring an Amazon S3 pipeline
      • Configuring a Databricks pipeline
      • Configuring an Azure pipeline
      • Configuring a Sharepoint pipeline
      • Selecting files for an uploaded file pipeline
    • Starting a pipeline run
    • Sharing pipeline access
    • Viewing pipeline results
      • Viewing pipeline files, runs, and statistics
      • Displaying details for a processed file
      • Structure of the pipeline output file JSON
    • Downloading and using pipeline output
  • Textual Python SDK
    • Installing the Textual SDK
    • Creating and revoking Textual API keys
    • Obtaining JWT tokens for authentication
    • Instantiating the SDK client
    • Datasets and redaction
      • Create and manage datasets
      • Redact individual strings
      • Redact individual files
      • Transcribe and redact an audio file
      • Configure entity type handling for redaction
      • Record and review redaction requests
    • Pipelines and parsing
      • Create and manage pipelines
      • Parse individual files
  • Textual REST API
    • About the Textual REST API
    • REST API authentication
    • Redaction
      • Redact text strings
  • Datasets
    • Manage datasets
    • Manage dataset files
  • Snowflake Native App and SPCS
    • About the Snowflake Native App
    • Setting up the app
    • Using the app
    • Using Textual with Snowpark Container Services directly
  • Install and administer Textual
    • Textual architecture
    • Setting up and managing a Textual Cloud pay-as-you-go subscription
    • Deploying a self-hosted instance
      • System requirements
      • Deploying with Docker Compose
      • Deploying on Kubernetes with Helm
    • Configuring Textual
      • How to configure Textual environment variables
      • Configuring the number of textual-ml workers
      • Configuring the number of jobs to run concurrently
      • Configuring the format of Textual logs
      • Setting a custom certificate
      • Configuring endpoint URLs for calls to AWS
      • Enabling PDF and image processing
      • Setting the S3 bucket for file uploads and redactions
      • Required IAM role permissions for Amazon S3
      • Configuring model preferences
    • Viewing model specifications
    • Managing user access to Textual
      • Textual organizations
      • Creating a new account in an existing organization
      • Single sign-on (SSO)
        • Viewing the list of SSO groups in Textual
        • Azure
        • GitHub
        • Google
        • Keycloak
        • Okta
      • Managing Textual users
      • Managing permissions
        • About permissions and permission sets
        • Built-in permission sets and available permissions
        • Viewing the lists of permission sets
        • Configuring custom permission sets
        • Configuring access to global permission sets
        • Setting initial access to all global permissions
    • Textual monitoring
      • Downloading a usage report
      • Tracking user access to Textual
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On this page
  • Azure AI Document Intelligence
  • Docker
  • Kubernetes
  • Amazon Textract
  • Tesseract

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  1. Install and administer Textual
  2. Configuring Textual

Enabling PDF and image processing

To process PDF and image files, Tonic Textual uses optical character recognition (OCR). Textual supports the following OCR models:

  • Azure AI Document Intelligence

  • Amazon Textract

  • Tesseract

For the best performance, we recommend that you use either Azure AI Document Intelligence or Amazon Textract.

If you cannot use either of those - for example because you run Textual on-premises and cannot access third-party services - then you can use Tesseract.

Azure AI Document Intelligence

To use Azure AI Document Intelligence to process PDF image files, Textual requires the Azure AI Document Intelligence key and endpoint.

Docker

In .env, uncomment and provide values for the following settings:

SOLAR_AZURE_DOC_INTELLIGENCE_KEY=#

SOLAR_AZURE_DOC_INTELLIGENCE_ENDPOINT=#

Kubernetes

In values.yaml, uncomment and provide values for the following settings:

azureDocIntelligenceKey:

azureDocIntelligenceEndpoint:

Amazon Textract

If the Azure-specific environment variables are not configured, then Textual attempts to use Amazon Textract.

We recommend that you use the AmazonTextractFullAccess policy, but you can also choose to use a more restricted policy.

Here is an example policy that provides the minimum required permissions:

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Sid": "VisualEditor0",
			"Effect": "Allow",
			"Action": [
				"textract:StartDocumentAnalysis",
				"textract:AnalyzeDocument",
				"textract:GetDocumentAnalysis"
			],
			"Resource": "*"
		}
	]
}

After the policy is attached to an IAM user or a role, it must be made accessible to Textual. To do this, either:

  • Assign an instance profile

  • Provide the AWS key, secret, and Region in the following environment variables:

AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
AWS_DEFAULT_REGION

Tesseract

If neither Azure AI Document Intelligence nor Amazon Textract is configured, then Textual uses Tesseract, which is automatically available in your Textual installation.

Tesseract does not require any external access.

Last updated 6 months ago

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To use Amazon Textract, Textual requires access to an IAM role that has sufficient permissions. You must also . The configured S3 bucket is required for uploaded file pipelines, and is also used to store dataset files and individual files that are redacted using the SDK.

configure an S3 bucket to use to store files