Install Read OCR Docker containers


Starting September 22nd 2020, most gated containers are hosted on the Microsoft Container Registry, and downloading them doesn't require you to use the docker login command. You will still need to complete an online request to run the container. See the Request approval to run the container section later in the article for more information.

Containers enable you to run the Computer Vision APIs in your own environment. Containers are great for specific security and data governance requirements. In this article you'll learn how to download, install, and run Computer Vision containers.

The Read OCR container allows you to extract printed and handwritten text from images and documents with support for JPEG, PNG, BMP, PDF, and TIFF file formats. For more information, see the Read API how-to guide.

Read 3.2 container

The Read 3.2 OCR container provides:

  • New models for enhanced accuracy.
  • Support for multiple languages within the same document.
  • Support for a total of 73 languages. See the full list of OCR-supported languages.
  • A single operation for both documents and images.
  • Support for larger documents and images.
  • Confidence scores.
  • Support for documents with both print and handwritten text.
  • Ability to extract text from only selected page(s) in a document.
  • Choose text line output order from default to a more natural reading order for Latin languages only.
  • Text line classification as handwritten style or not for Latin languages only.

If you're using Read 2.0 containers today, see the migration guide to learn about changes in the new versions.


You must meet the following prerequisites before using the containers:

Required Purpose
Docker Engine You need the Docker Engine installed on a host computer. Docker provides packages that configure the Docker environment on macOS, Windows, and Linux. For a primer on Docker and container basics, see the Docker overview.

Docker must be configured to allow the containers to connect with and send billing data to Azure.

On Windows, Docker must also be configured to support Linux containers.

Familiarity with Docker You should have a basic understanding of Docker concepts, like registries, repositories, containers, and container images, as well as knowledge of basic docker commands.
Computer Vision resource In order to use the container, you must have:

An Azure Computer Vision resource and the associated API key the endpoint URI. Both values are available on the Overview and Keys pages for the resource and are required to start the container.

{API_KEY}: One of the two available resource keys on the Keys page

{ENDPOINT_URI}: The endpoint as provided on the Overview page

If you don't have an Azure subscription, create a free account before you begin.

Request approval to run the container

Fill out and submit the request form to request approval to run the container.

The form requests information about you, your company, and the user scenario for which you'll use the container. After you submit the form, the Azure Cognitive Services team will review it and email you with a decision.


  • On the form, you must use an email address associated with an Azure subscription ID.
  • The Azure resource you use to run the container must have been created with the approved Azure subscription ID.
  • Check your email (both inbox and junk folders) for updates on the status of your application from Microsoft.

After you're approved, you will be able to run the container after downloading it from the Microsoft Container Registry (MCR), described later in the article.

You won't be able to run the container if your Azure subscription has not been approved.

Gathering required parameters

There are three primary parameters for all Cognitive Services' containers that are required. The end-user license agreement (EULA) must be present with a value of accept. Additionally, both an Endpoint URL and API Key are needed.


The Endpoint URI value is available on the Azure portal Overview page of the corresponding Cognitive Service resource. Navigate to the Overview page, hover over the Endpoint, and a Copy to clipboard icon will appear. Copy and use where needed.

Gather the endpoint uri for later use

Keys {API_KEY}

This key is used to start the container, and is available on the Azure portal's Keys page of the corresponding Cognitive Service resource. Navigate to the Keys page, and click on the Copy to clipboard icon.

Get one of the two keys for later use


These subscription keys are used to access your Cognitive Service API. Do not share your keys. Store them securely, for example, using Azure Key Vault. We also recommend regenerating these keys regularly. Only one key is necessary to make an API call. When regenerating the first key, you can use the second key for continued access to the service.

The host computer

The host is a x64-based computer that runs the Docker container. It can be a computer on your premises or a Docker hosting service in Azure, such as:

Advanced Vector Extension support

The host computer is the computer that runs the docker container. The host must support Advanced Vector Extensions (AVX2). You can check for AVX2 support on Linux hosts with the following command:

grep -q avx2 /proc/cpuinfo && echo AVX2 supported || echo No AVX2 support detected


The host computer is required to support AVX2. The container will not function correctly without AVX2 support.

Container requirements and recommendations


The requirements and recommendations are based on benchmarks with a single request per second, using a 523-KB image of a scanned business letter that contains 29 lines and a total of 803 characters. The recommended configuration resulted in approximately 2x faster response compared with the minimum configuration.

The following table describes the minimum and recommended allocation of resources for each Read OCR container.

Container Minimum Recommended
Read 2.0-preview 1 core, 8-GB memory 8 cores, 16-GB memory
Read 3.2 4 cores, 16-GB memory 8 cores, 24-GB memory
  • Each core must be at least 2.6 gigahertz (GHz) or faster.

Core and memory correspond to the --cpus and --memory settings, which are used as part of the docker run command.

Get the container image with docker pull

Container images for Read are available.

Container Container Registry / Repository / Image Name
Read 2.0-preview
Read 3.2

Use the docker pull command to download a container image.

Docker pull for the Read OCR container

docker pull


You can use the docker images command to list your downloaded container images. For example, the following command lists the ID, repository, and tag of each downloaded container image, formatted as a table:

docker images --format "table {{.ID}}\t{{.Repository}}\t{{.Tag}}"

IMAGE ID         REPOSITORY                TAG
<image-id>       <repository-path/name>    <tag-name>

How to use the container

Once the container is on the host computer, use the following process to work with the container.

  1. Run the container, with the required billing settings. More examples of the docker run command are available.
  2. Query the container's prediction endpoint.

Run the container with docker run

Use the docker run command to run the container. Refer to gathering required parameters for details on how to get the {ENDPOINT_URI} and {API_KEY} values.

Examples of the docker run command are available.

docker run --rm -it -p 5000:5000 --memory 18g --cpus 8 \ \
Eula=accept \
Billing={ENDPOINT_URI} \

This command:

  • Runs the Read OCR container from the container image.
  • Allocates 8 CPU core and 18 gigabytes (GB) of memory.
  • Exposes TCP port 5000 and allocates a pseudo-TTY for the container.
  • Automatically removes the container after it exits. The container image is still available on the host computer.

You can alternatively run the container using environment variables:

docker run --rm -it -p 5000:5000 --memory 18g --cpus 8 \
--env Eula=accept \
--env Billing={ENDPOINT_URI} \
--env ApiKey={API_KEY} \

More examples of the docker run command are available.


The Eula, Billing, and ApiKey options must be specified to run the container; otherwise, the container won't start. For more information, see Billing.

If you need higher throughput (for example, when processing multi-page files), consider deploying multiple containers on a Kubernetes cluster, using Azure Storage and Azure Queue.

If you're using Azure Storage to store images for processing, you can create a connection string to use when calling the container.

To find your connection string:

  1. Navigate to Storage accounts on the Azure portal, and find your account.
  2. Click on Access keys in the left navigation list.
  3. Your connection string will be located below Connection string

Run multiple containers on the same host

If you intend to run multiple containers with exposed ports, make sure to run each container with a different exposed port. For example, run the first container on port 5000 and the second container on port 5001.

You can have this container and a different Azure Cognitive Services container running on the HOST together. You also can have multiple containers of the same Cognitive Services container running.

Validate that a container is running

There are several ways to validate that the container is running. Locate the External IP address and exposed port of the container in question, and open your favorite web browser. Use the various request URLs below to validate the container is running. The example request URLs listed below are http://localhost:5000, but your specific container may vary. Keep in mind that you're to rely on your container's External IP address and exposed port.

Request URL Purpose
http://localhost:5000/ The container provides a home page.
http://localhost:5000/ready Requested with GET, this provides a verification that the container is ready to accept a query against the model. This request can be used for Kubernetes liveness and readiness probes.
http://localhost:5000/status Also requested with GET, this verifies if the api-key used to start the container is valid without causing an endpoint query. This request can be used for Kubernetes liveness and readiness probes.
http://localhost:5000/swagger The container provides a full set of documentation for the endpoints and a Try it out feature. With this feature, you can enter your settings into a web-based HTML form and make the query without having to write any code. After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that's required.

Container's home page

Query the container's prediction endpoint

The container provides REST-based query prediction endpoint APIs.

Use the host, http://localhost:5000, for container APIs. You can view the Swagger path at: http://localhost:5000/swagger/vision-v3.2-read/swagger.json.

Asynchronous read

You can use the POST /vision/v3.2/read/analyze and GET /vision/v3.2/read/operations/{operationId} operations in concert to asynchronously read an image, similar to how the Computer Vision service uses those corresponding REST operations. The asynchronous POST method will return an operationId that is used as the identifer to the HTTP GET request.

From the swagger UI, select the Analyze to expand it in the browser. Then select Try it out > Choose file. In this example, we'll use the following image:

tabs vs spaces

When the asynchronous POST has run successfully, it returns an HTTP 202 status code. As part of the response, there is an operation-location header that holds the result endpoint for the request.

 content-length: 0
 date: Fri, 04 Sep 2020 16:23:01 GMT
 operation-location: http://localhost:5000/vision/v3.2/read/operations/a527d445-8a74-4482-8cb3-c98a65ec7ef9
 server: Kestrel

The operation-location is the fully qualified URL and is accessed via an HTTP GET. Here is the JSON response from executing the operation-location URL from the preceding image:

  "status": "succeeded",
  "createdDateTime": "2021-02-04T06:32:08.2752706+00:00",
  "lastUpdatedDateTime": "2021-02-04T06:32:08.7706172+00:00",
  "analyzeResult": {
    "version": "3.2.0",
    "readResults": [
        "page": 1,
        "angle": 2.1243,
        "width": 502,
        "height": 252,
        "unit": "pixel",
        "lines": [
            "boundingBox": [
            "text": "Tabs vs",
            "appearance": {
              "style": {
                "name": "handwriting",
                "confidence": 0.96
            "words": [
                "boundingBox": [
                "text": "Tabs",
                "confidence": 0.933
                "boundingBox": [
                "text": "vs",
                "confidence": 0.977
            "boundingBox": [
            "text": "paces",
            "appearance": {
              "style": {
                "name": "handwriting",
                "confidence": 0.746
            "words": [
                "boundingBox": [
                "text": "paces",
                "confidence": 0.938


If you deploy multiple Read OCR containers behind a load balancer, for example, under Docker Compose or Kubernetes, you must have an external cache. Because the processing container and the GET request container might not be the same, an external cache stores the results and shares them across containers. For details about cache settings, see Configure Computer Vision Docker containers.

Synchronous read

You can use the following operation to synchronously read an image.

POST /vision/v3.2/read/syncAnalyze

When the image is read in its entirety, then and only then does the API return a JSON response. The only exception to this is if an error occurs. When an error occurs the following JSON is returned:

    "status": "Failed"

The JSON response object has the same object graph as the asynchronous version. If you're a JavaScript user and want type safety, consider using TypeScript to cast the JSON response.

For an example use-case, see the TypeScript sandbox here and select Run to visualize its ease-of-use.

Stop the container

To shut down the container, in the command-line environment where the container is running, select Ctrl+C.


If you run the container with an output mount and logging enabled, the container generates log files that are helpful to troubleshoot issues that happen while starting or running the container.


For more troubleshooting information and guidance, see Cognitive Services containers frequently asked questions (FAQ).

If you're having trouble running a Cognitive Services container, you can try using the Microsoft diagnostics container. Use this container to diagnose common errors in your deployment environment that might prevent Cognitive Services containers from functioning as expected.

To get the container, use the following Docker pull command:

docker pull

Then run the container, replace {ENDPOINT_URI} with your endpoint, and {API_KEY} with your key to your resource:

docker run --rm \
Eula=accept \
Billing={ENDPOINT_URI} \

The container will test for network connectivity to the billing endpoint.


The Cognitive Services containers send billing information to Azure, using the corresponding resource on your Azure account.

Queries to the container are billed at the pricing tier of the Azure resource that's used for the ApiKey.

Azure Cognitive Services containers aren't licensed to run without being connected to the metering / billing endpoint. You must enable the containers to communicate billing information with the billing endpoint at all times. Cognitive Services containers don't send customer data, such as the image or text that's being analyzed, to Microsoft.

Connect to Azure

The container needs the billing argument values to run. These values allow the container to connect to the billing endpoint. The container reports usage about every 10 to 15 minutes. If the container doesn't connect to Azure within the allowed time window, the container continues to run but doesn't serve queries until the billing endpoint is restored. The connection is attempted 10 times at the same time interval of 10 to 15 minutes. If it can't connect to the billing endpoint within the 10 tries, the container stops serving requests. See the Cognitive Services container FAQ for an example of the information sent to Microsoft for billing.

Billing arguments

The docker run command will start the container when all three of the following options are provided with valid values:

Option Description
ApiKey The API key of the Cognitive Services resource that's used to track billing information.
The value of this option must be set to an API key for the provisioned resource that's specified in Billing.
Billing The endpoint of the Cognitive Services resource that's used to track billing information.
The value of this option must be set to the endpoint URI of a provisioned Azure resource.
Eula Indicates that you accepted the license for the container.
The value of this option must be set to accept.

For more information about these options, see Configure containers.


In this article, you learned concepts and workflow for downloading, installing, and running Computer Vision containers. In summary:

  • Computer Vision provides a Linux container for Docker, encapsulating Read.
  • The read container image requires an application to run it.
  • Container images run in Docker.
  • You can use either the REST API or SDK to call operations in Read OCR containers by specifying the host URI of the container.
  • You must specify billing information when instantiating a container.


Cognitive Services containers are not licensed to run without being connected to Azure for metering. Customers need to enable the containers to communicate billing information with the metering service at all times. Cognitive Services containers do not send customer data (for example, the image or text that is being analyzed) to Microsoft.

Next steps