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Moving OCR out of an application can help control where image processing runs, but it does not by itself make the whole system’s egress zero. That depends on the OCR deployment and every other network dependency. A local OCR container can process documents on premises; a managed OCR service can be reached through a private network endpoint. The latter keeps a particular path off the public internet, but still sends requests to a cloud service.
What “moving OCR out of the app” changes
OCR placement is an architecture decision: the application can call a separate worker, or it can send documents to a managed service. Separating OCR from the application process can establish a clearer boundary for deployment, permissions, and network policy. It does not automatically prevent the application or worker from making other outbound connections.
“Zero egress” needs a defined scope. It could mean no public-internet route from a workload, no outbound traffic from a particular subnet, or no data leaving an organization’s premises. Those are different requirements. A private connection to a cloud OCR service may satisfy a no-public-internet-path requirement while not satisfying a requirement that data never leave the organization’s environment.
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Two ways to place the OCR work
| Pattern | What it means | What to evaluate |
|---|---|---|
| Local or on-premises OCR container | OCR runs in a container deployed within your environment. Microsoft documents an on-premises Read OCR container for security and data-governance needs. | Processing location, supported formats and languages, container version and lifecycle, operational ownership, and whether the worker has outbound dependencies. |
| Managed OCR over private connectivity | Your application sends requests to a cloud OCR service over a private network path. AWS documents Textract interface endpoints using AWS PrivateLink. | Region and endpoint support, identity and endpoint policies, data handling, request mode, network path, and egress from the rest of the application. |
Run OCR locally
A local container puts the OCR execution environment on premises or otherwise within infrastructure you control. Microsoft says its Azure AI containers do not send analyzed customer data to Microsoft. The documented Read container accepts JPEG, PNG, BMP, PDF, and TIFF input.
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The specific container documented is Azure Vision Read OCR 3.2 GA. Microsoft’s OCR overview warns against using the legacy OCR API v3.2 and RecognizeText API v2.1. Do not treat that warning as a recommendation to adopt the 3.2 container without checking its current lifecycle and support status: the container and the legacy cloud APIs are distinct offerings. The team operating a local container also takes responsibility for deployment, version review, and its runtime dependencies.
Sources: Microsoft OCR overview and Read container installation documentation.
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Call managed OCR through a private endpoint
AWS says a Textract interface VPC endpoint can provide access without an internet gateway, NAT device, VPN, or Direct Connect; instances do not need public IP addresses. AWS describes the boundary precisely: “Traffic between your VPC and Amazon Textract does not leave the AWS network.” This statement concerns that VPC-to-Textract path, not every dependency in your application and not a guarantee that data stays on premises.
The request is still made to a managed cloud service. Private connectivity changes the network route; it does not turn the service into local processing. Check endpoint and regional support, endpoint policies, identity permissions, and service-specific data handling. Textract clients must support TLS 1.2, and AWS recommends TLS 1.3. Textract communicates through HTTPS endpoints. AWS also notes that free-form text fields may be collected for diagnostic logs and documents handling for custom-query adapter training content, so review the service’s data-protection terms for your use case.
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Sources: AWS interface VPC endpoint documentation, AWS infrastructure security, and AWS data protection.
OCR may be only one step in document processing
Many document workflows do more than recognize text. AWS’s illustrative intelligent document processing guidance shows storage triggering asynchronous OCR, followed by classification or enrichment, validation that may include human review, and storage of verified results for downstream applications. Its guidance is explicitly not intended for production or critical-data accounts, so use it as a workflow example rather than a production blueprint.
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That distinction matters for network boundaries: moving OCR may leave storage, queues, classification, validation tools, logging, or downstream systems untouched. Decide whether the requirement applies only to the OCR worker’s route or to the complete document lifecycle.
For Textract, AWS describes synchronous analysis for single-page documents and directs multipage workflows to asynchronous processing. Choose the mode to fit the document workflow, then account for the associated storage, event, and result-handling paths.
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Sources: AWS intelligent document processing guidance and AWS synchronous processing documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to verify a strict no-egress design
- Define the boundary. Specify whether “zero egress” means no public internet access, no outbound traffic from a workload or subnet, or no data leaving a physical or administrative environment.
- Inventory every outbound dependency. Include the application, OCR worker, storage, queues, identity systems, logging, metrics, license checks, update mechanisms, and any downstream processors. Record destinations and whether traffic carries document content or metadata.
- Review routing and name resolution. Confirm that DNS resolves the intended endpoint and that route tables, firewall rules, endpoint policies, and security controls direct traffic along the approved path. Do not infer the path merely from a service being called “private.”
- Test the intended restriction. If the goal is a workload with no public route, remove public routes or otherwise enforce the target policy in a controlled test, then exercise the full document workflow. Check for failed calls and unexpected destinations as well as successful OCR.
- Account for operations over time. Confirm how logs, metrics, updates, container pulls, license validation, storage, and recovery procedures work under the same egress policy. A deployment that works only while unrestricted may not meet the intended boundary.
This is an engineering verification checklist, not a claim that the cited OCR products automatically audit every dependency or prove end-to-end zero egress. The available documentation does not establish comparative accuracy, throughput, price, or an end-to-end egress measurement for these patterns.
Quick Recap
Choosing the pattern
- Choose local processing when the requirement is for OCR execution and document analysis to remain within infrastructure you operate, and you can own the container lifecycle and operational dependencies.
- Consider managed OCR with a private endpoint when cloud processing is acceptable but the service path must avoid public internet routing. Validate the endpoint path and all other application egress separately.
- Design the whole workflow when documents move through multiple services or human review. The OCR boundary alone does not establish the network behavior of those other stages.
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