Guide for Integrating Private AI DEID container with Azure OCR

This guide provides step-by-step instructions on setting up the Private AI container with the Azure OCR for processing images and documents. Both the cloud and containerized versions of Azure OCR are supported.

Please check out our Guide for OCR Modes Available with Private AI DEID Container to see the different modes of OCR supported by Private AI DEID Container.

Prerequisites

Docker Compose

Docker Compose installed on your system.

Access to Private AI's DEID container

See Grabbing the Image for more info on this.

Azure OCR Service

Setup one of the following Azure OCR service to use with Private AI DEID Container:

Azure Computer Vision Service

Navigate to the Azure Portal.
Create a new Azure Computer Vision service by following the link: https://portal.azure.com/#create/Microsoft.CognitiveServicesComputerVision . (You can also use your existing Azure Computer Vision service)
Note down your Computer Vision service endpoint and the key.

Azure Document Intelligence Service

Navigate to the Azure Portal.
Create a new Azure Document Intelligence service by following the link: https://portal.azure.com/#create/Microsoft.CognitiveServicesFormRecognizer . (You can also use your existing Azure Document Intelligence service)
Note down your Document Intelligence service endpoint and the key.

Deployment with Cloud Azure OCR Services

Private AI DEID container can be configured to use cloud or on-prem version of Azure OCR service. This section shows how to configure the DEID container with cloud Azure OCR service.

Create a docker-compose.yml file in your preferred text editor.
Configure the DEID service and Azure OCR using one of the options below.

Note

The instructions for running the container are provided in the form of a docker-compose.yml file for easy readability. Alternatively, you can choose to run the container using the docker run command.

For cloud-based Azure OCR services, the only changes required will be in the PAI_OCR_SYSTEM and PAI_AZ_* environment variables. If you are using the container version of Azure OCR services, you will need to run the Azure OCR container alongside the DEID container.

Note

For production deployments, we recommend using Kubernetes for managing the containers.

Option 1 - DEID Container with cloud Azure Computer Vision Service

This section shows how to configure the DEID container with cloud Azure Computer Vision Service.

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version: '3.8'
services:
  deid_service:
    image: crprivateaiprod.azurecr.io/deid:gpu
    shm_size: "4g"
    container_name: deid_container
    tty: true
    environment:
      - PAI_OCR_SYSTEM=azure_computer_vision
      - PAI_AZ_COMPUTER_VISION_URL=<Azure Computer Vision Service URL>
      - PAI_AZ_COMPUTER_VISION_KEY=<Azure Computer Vision Key>
      - PAI_OUTPUT_FILE_DIR=<output file directory>
    ports:
      - "8080:8080"
    # Input and output directories are only required when using URI file processing
    volumes:
      - <path to license.json>:/app/license/license.json
      - <input file directory>:<input file directory>
      - <output file directory>:<output file directory>  # This directory should be same as PAI_OUTPUT_FILE_DIR
    # comment out this section if you're using a CPU version of deid container.
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Replace placeholders (e.g., <Azure Computer Vision Service URL>) with your actual service details.

Option 2 - DEID Container with cloud Azure Document Intelligence Service

This section shows how to configure the DEID container with cloud Azure Document Intelligence Service.

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version: '3.8'
services:
  deid_service:
    image: crprivateaiprod.azurecr.io/deid:gpu
    shm_size: "4g"
    container_name: deid_container
    tty: true
    environment:
      - PAI_OCR_SYSTEM=azure_doc_intelligence
      - PAI_AZ_DOCUMENT_INTELLIGENCE_URL=<Azure Document Intelligence Service URL>
      - PAI_AZ_DOCUMENT_INTELLIGENCE_KEY=<Azure Document Intelligence Key>
      - PAI_OUTPUT_FILE_DIR=<output file directory>
    ports:
      - "8080:8080"
    # Input and output directories are only required when using URI file processing
    volumes:
      - <path to license.json>:/app/license/license.json
      - <input file directory>:<input file directory>
      - <output file directory>:<output file directory>  # This directory should be same as PAI_OUTPUT_FILE_DIR
    # comment out this section if you're using a CPU version of deid container.
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Replace placeholders (e.g., <Azure Document Intelligence Service URL>) with your actual service details.

Deployment with On-Premise Azure OCR Services

Note

Currently, only the Azure Computer Vision Service can be used on-premise with the Private AI DEID service.

Private AI DEID container can be configured to use cloud or on-prem version of Azure OCR service. This section shows how to configure the DEID container with on-premise Azure Computer Vision Service.

Create a docker-compose.yml file in your preferred text editor.
Configure the DEID service and Azure OCR using one of the options below.

Option 3 - DEID Container with On-Premise Azure Computer Vision Service

This section shows how to configure the DEID container with on-premise Azure Computer Vision Service.

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version: '3.8'
services:
  deid_service:
    image: crprivateaiprod.azurecr.io/deid:gpu
    shm_size: "4g"
    container_name: deid_container
    tty: true
    environment:
      - PAI_OCR_SYSTEM=azure
      - PAI_AZ_COMPUTER_VISION_URL=http://azure_vision_service:5000
      - PAI_AZ_COMPUTER_VISION_KEY=<Azure Computer Vision Key>
      - PAI_OUTPUT_FILE_DIR=<output file directory>
    ports:
      - "8080:8080"
    # Input and output directories are only required when using URI file processing
    volumes:
      - <path to license.json>:/app/license/license.json
      - <input file directory>:<input file directory>
      - <output file directory>:<output file directory>  # This directory should be same as PAI_OUTPUT_FILE_DIR
    # comment out this section if you're using a CPU version of deid container.
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

  azure_vision_service:
    image: mcr.microsoft.com/azure-cognitive-services/vision/read:latest
    container_name: azure_vision_container
    tty: true
    environment:
      - Eula=accept
      - Billing=<Azure Computer Vision Resource Endpoint>
      - ApiKey=<Azure Computer Vision Resource Key>
    deploy:
      resources:
        limits:
          memory: 16G
          cpus: '8'

For further details on the Azure Computer Vision container please refer to the Azure Documentation.

Start the Containers

Open a terminal and navigate to the directory containing your docker-compose.yml file. Run the following command to start the containers:

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docker compose -f docker-compose.yml up

Verify Operation

Once the containers are running, you can verify their operation by accessing the DEID service's exposed port (e.g., http://localhost:8080) and performing a test OCR operation on your documents or images.