OCR-D setup guide

OCR-D’s software is a modular collection of many projects (called modules) with many tools per module (called processors) that you can combine freely to achieve the workflow best suited for OCRing your content.

System requirements

Minimum system requirements

- 8 GB RAM (more recommended) - The more RAM is available, the more concurrent processes can be run - Exceedingly large images (newspapers, folio-size books...) require a lot of RAM
- min. 20 GB free disk space for local installation (more recommended) - How much disk space is needed depends mainly on the individual purposes of the installation. In addition to the installation itself you will need space for various [pretrained models](https://ocr-d.de/en/models), training and evaluation data for training, and data to process.
- Operating system: Ubuntu 18.04 - Ubuntu 18.04 is our target platform because it was the most up-to-date Ubuntu LTS release when we started developing and [will be supported for the foreseeable future](https://ubuntu.com/about/release-cycle) - Other Linux distributions or Ubuntu versions can also be used, though some instructions have to be adapted (e.g. package management, locations of some files) - With Windows Subsystem for Linux (WSL), a feature of Windows 10, it is [also possible to set up an Ubuntu 18.04 installation within Microsoft Windows](https://github.com/OCR-D/ocrd-website/wiki/OCR-D-on-Windows) - OCR-D can be deployed on an [Apple MacOSX machine using Homebrew](https://github.com/OCR-D/ocrd-website/wiki/OCR-D-on-macOS)
- Python 3.5, 3.6 or 3.7 - OCR-D's target Python version is currently Python 3.5 which we will continue to support until at least Q1 2021 - Python 3.6 and 3.7 are also tested and supported - We currently **cannot fully support Python 3.8**, because there currently (July 2020) are no pre-built Python packages for Tensorflow and other GPU related software). We expect to unconditionally support Python 3.8 in Q1 2021 the latest.

For installation on Windows 10 (WSL) and macOS see the setup guides in the OCR-D-Wiki

Alternatively, you can use Docker. This way, you will only have to meet the minimum requirements for free disk space. But you can use any operating system you want and do not have to worry about the Python version.


There are two ways to install OCR-D modules:

  1. Using the ocrd_all prebuilt Docker images ocrd/all to install a module collection (recommended)
  2. Using the ocrd_all git repository to install selected modules natively

We recommend using the prebuilt Docker images since this does not require any changes to the host system besides installing Docker.

For developers it might be useful to install the modules individually, either via Docker or natively. Beware that for all other users and purposes we do not recommend installing modules individually, because it can be difficult to catch all dependencies, keep the software up-to-date and ensure that they are at usable and interoperable versions.


The ocrd_all project is an effort by the OCR-D community, now maintained by the OCR-D coordination team. It streamlines the native installation of OCR-D modules with a versatile Makefile approach. Besides allowing native installation of the full OCR-D stack (or any subset), it is also the base for the ocrd/all Docker images available from DockerHub that contain the full stack (or certain subsets) of OCR-D modules ready for deployment.

Technically, ocrd_all is a Git repository that keeps all the necessary software as Git submodules at specific revisions. This way, the software tools are known to be at a stable version and guaranteed to be interoperable with one another.

ocrd_all via Docker

mini medi maxi

There are three versions of the ocrd/all image: minimum, medium and maximum. They differ in which modules are included and hence the size of the image. Only use the minimum or medium images if you are certain that you do not need the full OCR-D stack for your workflows, otherwise we encourage you to use the large but complete maximum image.

Check this table to see which modules are included in which version:

Module minimum medium maximum
cor-asv-ann -
dinglehopper -
format-converters -
ocrd_calamari -
ocrd_keraslm -
ocrd_olena -
ocrd_segment -
tesseract -
ocrd_anybaseocr - -
ocrd_kraken - -
ocrd_ocropy - -
ocrd_pc_segmentation - -
ocrd_typegroups_classifier - -
sbb_textline_detector - -
cor-asv-fst - -

Fetch Docker image

To fetch the maximum version of the ocrd/all Docker image:

docker pull ocrd/all:maximum

Replace maximum accordingly if you want the minimum or medium variant.

(Also, if you want to keep the modules’ git repos inside the Docker images – so you can keep making fast updates, without waiting for a new pre-built image but also without building an image yourself –, then add the suffix -git to the variant, e.g. maximum-git. This will behave like the native installation, only inside the container. Yes, you can also commit changes made in containers back to your local Docker image.)

Testing the Docker installation

For example, let’s fetch a document from the OCR-D GT Repo:

wget 'https://ocr-d-repo.scc.kit.edu/api/v1/dataresources/736a2f9a-92c6-4fe3-a457-edfa3eab1fe3/data/wundt_grundriss_1896.ocrd.zip'
unzip wundt_grundriss_1896.ocrd.zip
cd data

Let’s segment the images in file group OCR-D-IMG from the zip file into regions (creating a first PAGE-XML file group OCR-D-SEG-BLOCK-DOCKER)

You can spin up a docker container, mounting the current working directory like this:

docker run --user $(id -u) --workdir /data --volume $PWD:/data -- ocrd/all:maximum ocrd-tesserocr-segment-region -I OCR-D-IMG -O OCR-D-SEG-BLOCK-DOCKER

For instructions on how to process your own data, please see the user guide. Make sure to also read the notes on translating native command line calls to docker calls above. Make sure the image name matches the executable.

Updating Docker image

To update the docker images to their latest version, just run the docker pull command again:

docker pull ocrd/all:<version>

This can even be set up as a cron-job to ensure the image is always up-to-date.

ocrd_all natively

The ocrd_all project contains a sophisticated Makefile to install or compile prerequisites as necessary, set up a virtualenv, install the core software, install OCR-D modules and more. Detailed documentation can be found in its README.


There are some system requirements for ocrd_all.

You need to have make installed to make use of ocrd_all:

sudo apt install make

Clone the repository (still without submodules) and change into the ocrd_all directory:

git clone https://github.com/OCR-D/ocrd_all
cd ocrd_all

You should now be in a directory called ocrd_all.

It is easiest to install all the possible system requirements by calling make deps-ubuntu as root:

sudo make deps-ubuntu

This will install all system requirements.

Now you are ready for the final step which will actually install the OCR-D-Software.

You can either install

  1. all the software at once with the all target (equivalent to the maximum Docker version),
  2. modules individually by using an executable from that module as the target, or
  3. a subset of modules by listing the project names in the OCRD_MODULES variable (equivalent to a custom selection of the medium Docker version):
make all                       # Installs all the software (recommended)

make ocrd-tesserocr-binarize   # Install ocrd_tesserocr which contains ocrd-tesserocr-binarize
make ocrd-cis-ocropy-binarize  # Install ocrd_cis  which contains ocrd-cis-ocropy-binarize

make all OCRD_MODULES="core ocrd_tesserocr ocrd_cis" # Will install only ocrd_tesserocr and ocrd_cis

(Custom choices for OCRD_MODULES and other control variables (cf. make help) can also be made permanent by writing them into local.mk.)

Note: Never run make all as root unless you know exactly what you are doing!

Installation is incremental, i.e. failed/interrupted attempts can be continued, and modules can be installed one at a time as needed.

Running make will also take care of cloning and updating all required submodules.

Especially running make all will take a while (between 30 and 60 minutes or more on slower machines). In the end, it should say that the last processor was installed successfully.

Having installed ocrd_all successfully, ocrd --version should give you the current version of OCR-D/core. Activate the virtual Python environment, which was created in the directory venv, before running any OCR-D command.

source venv/bin/activate
ocrd --version
ocrd, version 2.13.2 # your version should be 2.13.2 or later

Testing the native installation

For example, let’s fetch a document from the OCR-D GT Repo:

wget 'https://ocr-d-repo.scc.kit.edu/api/v1/dataresources/736a2f9a-92c6-4fe3-a457-edfa3eab1fe3/data/wundt_grundriss_1896.ocrd.zip'
unzip wundt_grundriss_1896.ocrd.zip
cd data

If you haven’t done it already, activate your venv:

# Activate the venv
source /path/to/ocrd_all/venv/bin/activate

Let’s segment the images in file group OCR-D-IMG from the zip file into regions (creating a first PAGE-XML file group OCR-D-SEG-BLOCK):

ocrd-tesserocr-segment-region -I OCR-D-IMG -O OCR-D-SEG-BLOCK

For instructions on how to process your own data, please see the user guide.

Updating the software

As ocrd_all is in active development, it is wise to regularly update the repository and its submodules:

git pull 

This will refresh the local clone of ocrd_all with the changes in the official ocrd_all GitHub repository.

Now you can install the changes with

make all 

This will run the installation process for all submodules which have been changed. In the end, it should say that the last processor was installed successfully. --version for the processors which have been changed should give you its current version.

Individual installation (experts only)

For developing purposes it might be useful to install modules individually, either with Docker or natively. With all variants of individual module installation, it will be up to you to keep the repositories up-to-date and installed. We therefore discourage individual installation of modules and recommend using ocrd_all as outlined above..

All OCR-D modules follow the same interface and common design patterns. So once you understand how to install and use one project, you know how to install and use all of them.

Individual Docker container

This is the best option if you want full control over which modules you actually intend to use while still profiting from the simple installation of Docker containers.

You need to have Docker

Many OCR-D modules are also published as Docker containers to DockerHub. To find the Docker image for a module, replace the ocrd_ prefix with ocrd/:

docker pull ocrd/tesserocr  # Installs ocrd_tesserocr
docker pull ocrd/olena  # Installs ocrd_olena

Now you can test your installation.

Native installation

Installing each module into your system natively requires you to know and install all its dependencies first. That can be system packages (or even system package repositories) or Python packages.

To learn about system dependencies, consult the module’s README files. In contrast, Python dependencies should be resolved automatically by using the Python package manager pip.


ocrd_tesserocr requires tesseract-ocr >= 4.1.0. But the Tesseract packages bundled with Ubuntu < 19.10 are too old. If you are on Ubuntu 18.04 LTS, please enable Alexander Pozdnyakov PPA, which has up-to-date builds of tesseract and its dependencies:

sudo add-apt-repository ppa:alex-p/tesseract-ocr
sudo apt-get update

Next subsections:


First install Python 3 and venv:

sudo apt install python3 python3-venv
# If you haven't created the venv yet:
python3 -m venv ~/venv
# Activate the venv
source ~/venv/bin/activate

Once you have activated the virtualenv, you should see (venv) prepended to your shell prompt.

From PyPI

This is the best option if you want to use the stable, released version of individual modules.

However, many modules require a number of non-Python (system) packages. For the exact list of packages you need to look at the README of the module in question. (If you are not on Ubuntu >= 18.04, then your requirements may deviate from that.)

For example to install ocrd_tesserocr from PyPI:

sudo apt-get install git python3 python3-pip python3-venv libtesseract-dev libleptonica-dev tesseract-ocr-eng tesseract-ocr wget
pip3 install ocrd_tesserocr

Many ocrd modules conventionally contain a Makefile with a deps-ubuntu target that can handle calls to apt-get for you:

sudo make deps-ubuntu

Now you can test your installation.

From git

This is the best option if you want to change the source code or install the latest, unpublished changes.

git clone https://github.com/OCR-D/ocrd_tesserocr
cd ocrd_tesserocr
sudo make deps-ubuntu # or manually with apt-get
make deps             # or pip3 install -r requirements
make install          # or pip3 install .

If you intend to develop a module, it is best to install the module editable:

pip install -e .

This way, you won’t have to reinstall after making changes.

Now you can test your installation.