OCR Services

About OCR Services

OCR services are used for the following purposes:

• At data labeling time, when importing documents into Data Manager. The engines available for this step are UiPath Document OCR, Google Cloud Vision OCR, and Microsoft Read OCR.
• At run time when calling models from RPA workflows. The engines available for this step are all the engines integrated with the UiPath RPA platform including the above, plus Abbyy Finereader, Microsoft OCR (legacy), Microsoft Project Oxford OCR, and Tesseract.

In production, we recommend calling the OCR using the Digitize Document activity in your workflow and passing the Document Object Model as input to the activity calling the ML model. For this purpose, you need to use the Machine Learning Extractor activity (Official feed).

As a quick convenience for testing purposes, you can also configure the OCR directly in AI Center (Settings window), but this is not recommended for production deployments.

Requirements

This section details the hardware and software requirements for installing OCR Engines.

Hardware Requirements

• Machines Involved : VM in the Cloud/On-Prem Box/Laptop

• Operating Systems: Windows (Windows 10)/Linux (Ubuntu/RedHat)

• Computing Engines: CPU/GPU

• OCR: UiPath Document OCR CPU/UiPath Document OCR GPU

	CPU Cores	RAM (GB)	Video RAM (GB)	HDD (GB)
UiPath CPU	4	4		50
UiPath GPU	1	4	8	50

Software Requirements

Linux Operating System

If you install the product on a VM in the cloud, the following operating systems are supported:

Software	Versions
Ubuntu	20.04 LTS 18.04 LTS 16.04 LTS
RHEL	7.x

If you install the product on a machine in an on-premises data center, the following operating systems are supported:

Software	Versions
Ubuntu	20.04 LTS 18.04 LTS 16.04 LTS
RHEL	7.x

Windows Operating System

See the official Docker website for the list of Windows operating systems supported.

On Windows, your machine requires virtualization enabled. We strongly recommend this be done only on physical machines like laptops or desktop workstations. We do not support running on Docker on Windows in Virtual Machines (Cloud or Datacenter) using Nested Virtualization.

Browsers

Software	Versions
Google Chrome	50+

Network Configuration

• Data Manager needs access to OCR engine <IP>:<port_number>. OCR engine might be UiPath Document OCR on-premises, Google Cloud Vision OCR, Microsoft Read Azure, Microsoft Read on-premises.
• Robots need access to OCR <IP>:<port_number>. Same OCR options as above.

• OCR engines need access to the Licensing server hosted by UiPath in Azure, on port 443.

Minimal Trial or Proof-of-Concept Configuration

If you only want to serve pre-trained out-of-the-box models, you can run an OCR engine on your Windows 10 laptop. Make sure Docker Desktop has 8G of RAM available.

If you want to try training a custom model as a demo on a small volume of data (under 100 documents), you can run the OCR Engine on an environment with a limit of 4GB of RAM. For small cases like this, a GPU for the OCR engine may not be necessary.

Prerequisites

OCR Engines are containerized applications that run on top of docker. You cannot run these on the same machine as AI Center on-premises. To run them on a separate machine, the prerequisites installer commands below can be used to set up docker and optionally the NVidia drivers. These scripts should not be run on the machine where AI Center will be installed.

Important: Docker images can have many GB in size, so the folder Docker uses to hold its files on Linux must be on a partition sufficiently large to not run out of space. By default, it is always on the root partition.

To see how large your root partition is, type the following in the terminal, and look for the line with a / in the rightmost column:

df -h

If the size of that partition is smaller than the minimal storage requirements, then see the Configuring the Docker Data Folder section.

Installing Docker

Linux

Follow instructions in the official Docker documentation, or run this command:

curl -fsSL https://raw.githubusercontent.com/UiPath/Infrastructure/master/ML/du_prereq_installer.sh | sudo bash -s -- --env cpu

If this command fails, then you have an incompatible Linux operating system and you need to request your IT to install Docker on the machine following the instructions in the official Docker documentation.

Azure VMs

If you are installing on a VM in Azure, then use this command instead:

curl -fsSL https://raw.githubusercontent.com/UiPath/Infrastructure/master/ML/du_prereq_installer.sh | sudo bash -s -- --env cpu --cloud azure

Windows 10

Download and install Docker Desktop. On recently updated versions of Windows 10, you will need WSL2 installed. So when presented with a dialog saying "WSL 2 Installation is Incomplete" please click the Restart button.

When running Data Manager you need to create a working folder for each Docker container (perhaps named workdir for Data Manager) and include the path to it in the docker run command, after the -v flag. When doing this on Windows, Docker Desktop will pop up a notification like the one below. You need to click on Share it to proceed.

Configuring the Docker Data Folder (Linux Only)

Fill in the path to the folder where you want Docker to hold its files, then run this command and then reboot:

curl -fsSL https://raw.githubusercontent.com/UiPath/Infrastructure/master/ML/du_prereq_installer.sh | sudo bash -s -- --change-mount </path/to/folder>

Docker Cheat Sheet

Docker helps ship software in Docker “images. A running instance of an image is called a container. A container can be stopped, removed, started again, as many times as needed, as long as the image is available.

Once the image is removed, it is lost. The only way to recover it is to pull it again from the registry it came from if it is still available there.

A running container is analogous to a small Virtual Machine, in that it has an internal filesystemand network interfaces, which are separate from the host machine filesystem and network. Folders and ports can be mapped from the container to the host using –v and –p arguments, respectively.

In the table below you can find a list of common commands for the Docker command line.

Click here for the full list of base Docker commands.

Command	Description
docker login <registry name> -u <username> -p <password>	Log in to a registry.
docker pull <registry name>/<image name>:<image tag>	Download an image from a registry. The tag latest is commonly used to refer to the latest version of an image.
docker run –d -p 5000:80 <registry name>/<image name>:<image tag> OR docker run –d –p 5000:80 <image id>	Run an image in detached mode, while mapping port 80 from inside the container to port 5000 on the host machine, and <container folder> to <host folder>. Detached mode means the container does not block the terminal, so you can perform other operations on the same terminal.
docker images	List images present on your system.
docker ps –a	List all containers (both running and stopped). The container id is used to refer to that container when one needs to stop it or remove it, for instance.
docker stop <container id>	Stop the container This command does not remove the container, but is required in advance to removing it.
"docker rm <container id>"	Remove the container The container must be stopped beforehand.
docker logs <container id>	Display the logs of the container.
docker rmi <image id>	Remove one or more images from the system. This helps save storage space as images can take up a lot of space.
Docker container prune -f	Remove all stopped containers

Linux Terminal Cheat Sheet

Command	Description
sudo <any_command>	Run a command as administrator. Try this whenever you get a Permission Denied error.
ifconfig	Display information about the network interfaces in your system. Find the IP of your machine in the eth0 or docker0 sections.
pwd	Display the path to the current folder.
ls	List the content of a directory.
cd <folder_name>	Go to a different folder.
mkdir <folder_name>	Create a new folder.

(Optional) GPU Machine Install

Linux

Run this command:

curl -fsSL https://raw.githubusercontent.com/UiPath/Infrastructure/master/ML/du_prereq_installer.sh | sudo bash -s -- --env gpu

On some systems running the command twice or a system reboot might be required to install all requirements.

Azure Specific: To use the NV-series virtual machines you need to either install the NVIDIA driver before executing the above command, or you can use a Driver Extension from Azure to install the necessary NVIDIA driver according to that tier GPU model.

Azure VMs

If you are installing on a VM in Azure, then use this command instead:

curl -fsSL https://raw.githubusercontent.com/UiPath/Infrastructure/master/ML/du_prereq_installer.sh | sudo bash -s -- --env gpu --cloud azure

Installation

UiPath Document OCR

UiPath Document OCR is a proprietary OCR technology of UiPath, supporting characters used by the following Latin script languages: English, French, German, Italian, Portuguese, Romanian, and Spanish. Text in other languages will be recognized but without accents, for instance, “Ł” in Polish will be recognized as “L”. Pages processed using UiPath Document OCR are not counted towards the page quota purchased along with the Document Understanding Enterprise license so UiPath Document OCR is free to use.

UiPath Document OCR is available with the following deployment types:

• cloud public URLs - more details on the Public Endpoints page
• on-premises (including air-gapped) using UiPath.DocumentUnderstanding.OCR.LocalServer activity package (does not require Internet access)
• on-premises regular standalone docker container (requires Internet access)
• on-premises air-gapped standalone docker container (does not require Internet access)
• on-premises as ML Skill in AI Center regular deployment (requires Internet access)
• on-premises as ML Skill in AI Center air-gapped deployment (does not require Internet access)

1. To install UiPath Document OCR standalone docker container, run these commands:

   docker login aiflprodweacr.azurecr.io -u *** -p **docker pull aiflprodweacr.azurecr.io/uipath-ocr:latest

2. Run using CPUs

   docker run -d -p 5000:80 aiflprodweacr.azurecr.io/uipath-ocr:latest LicenseAgreement=accept

3. Run using GPU

   docker run -d -p 5000:80 --gpus all aiflprodweacr.azurecr.io/uipath-ocr:latest LicenseAgreement=accept

4. In AI Center, when creating a new ML Package, at the bottom of the screen there is the optional OCR configuration section where you can define the server side OCR Engine type, the OCR URL, and the OCR Key. The OCR Key is the API Key you obtain from the Licenses section of your Automation Cloud account. This is the OCR configuration which will be used by the MachineLearning Extractor activity if you check the "UseServerSideOCR" box. This box is unchecked by default, and in that case the extractor will use the OCR in the Digitize Document activity.

   Important: UiPath Document OCR container cannot run on the same machine as AI Center On-Premises.

Google Cloud OCR

The endpoint can be obtained from the Google Cloud Platform documentation. The ApiKey can be obtained from your Google Cloud Platform Console if you have a Google Cloud Vision service in your subscription.

Microsoft Read

Important: Applicable to both Azure and on-premises container endpoints.

In the case of Azure services, you need to provide both the Endpoint and the ApiKey.

In the case of on-premises container endpoints, API Key is not necessary.

Configuring OCR Service in Data Manager and AI Center Document Understanding ML Packages

The table below shows how to configure the six supported OCR engine types in both Data Manager and AI Center.

OCR Engine	OCR Method	OCR Key	OCR URL
UiPath	UiPath Document OCR	UiPath Automation Cloud Document Understanding API Key Enterprise Plan	http://<IP_addr>:<port_number>
Google	Google Cloud Vision OCR	GCP Console API Key	https://vision.googleapis.com/v1/images:annotate
Microsoft Read 2.0 On-Prem	Microsoft Read OCR	None	http://<IP_addr>:<port_number>/vision/v2.0/read/core/Analyze
Microsoft Read 2.0 Azure	Microsoft Read OCR	API Key for your resource from Azure Portal	<Azure_resource_Endpoint>/vision/v2.0/read/core/asyncBatchAnalyze
Microsoft Read 3.2 On-Prem	Microsoft Read OCR	None	http://<IP_addr>:<port_number>/vision/v3.2/read/analyze
Microsoft Read 3.2 Azure	Microsoft Read OCR	API Key for your resource from Azure Portal	<Azure_resource_Endpoint>/vision/v3.2/read/analyze