FAQ
General
Where can I start my journey into the OCR-D ecosphere?
Who is the target audience of OCR-D?
OCR-D’s primary target audience are libraries and archives, digitizing historical prints at scale.
Where can I get support on OCR-D?
- Open an issue at the OCR-D/core repository
- Chat with OCR-D project members and other OCR-D users in OCR-D’s chat room.
- Send an email to …
What is the difference between OCR-D and ABBYY?
ABBYY is a software developer producing the ABBYY Recognition Server which offers layout detection and text recognition with a pay-per-page pricing model. OCR-D is a project that integrates a wide variety of solutions for the full gamut of possible OCR workflow steps. ABBYY is simple to use but offers few options for customization whereas OCR-D workflows can be fine-tuned for best recognition of specific corpora. OCR-D has a strong focus on historical prints, trainable layout detection and text recognition and open interfaces to accommodate future developments, whereas ABBYY performs more strongly for modern print. Finally, OCR-D is a community effort with a strong focus on transparency and Free Software.
What is the difference between OCR-D and Tesseract?
Tesseract is the leading Free
Software OCR solution and tightly integrated into OCR-D in both a technical and
organizational sense. Technically, Tesseract has been wrapped as
ocrd_tesserocr, an OCR-D-compliant
processor that is more powerful than the command line tool bundled with
Tesseract. Organizationally, Tesseract maintainers and contributors have been
part of the OCR-D project from the beginning and the originally OCR-D-developed
Tesseract training tool
tesstrain has been adopted by
the wider Tesseract community.
What is the difference between OCR-D and TRANSKRIBUS?
TRANSKRIBUS is a software platform and server infrastructure to make it easier for Digital Humanities practitioners to collaborate on Handwriting Text Recognition. Apart from the different use cases
- historical prints for OCR-D, historical manuscripts for TRANSKRIBUS - there are differences in philosophy: All components of OCR-D are freely available as Apache-licensed Free Software whereas some core components of TRANSKRIBUS, particularly the recognition engine, are proprietary. TRANSKRIBUS is a server-client architecture with an Eclipse-based graphical user interface at its core whereas OCR-D’s focus is on mass digitization and command line interaction.
What is the difference between OCR-D and ocr4all?
Is OCR-D production-ready?
Which formats are supported by OCR-D?
OCR-D is primarily based around METS as a container format and PAGE-XML for
layout detection and text recognition results. Other OCR formats such as ALTO,
hOCR or ABBYY FineReader XML are supported through conversion with
ocrd_fileformat.
The preferred image format within OCR-D is TIFF but PNG and JPEG are also supported. JPEG2000 is not currently supported but can be added in the future if there is demand for it.
Why does OCR-D need METS files? How can I process images without METS?
The processes within OCR-D are designed around METS for the simple reason that it is such an ubiquitous and well-defined format used in libraries and archives around the world. By relying on a container format instead of just images, processors can make use of more information and can store detailed results in a well-defined fashion.
If the data to be processed isn’t already described by a METS file, the ocrd command line
tool offers simple ways to create new METS files or augment existing ones.
How much does it cost to deploy OCR-D?
OCR-D is Free Software, licensed under the terms of the Apache 2.0 license and will be free to use and adapt in perpetuity.
How are the full texts produced by OCR-D presented to the (library) user? Are they integrated into the library catalog and can therefore be used for full text search in the catalog?
What are the system requirements for OCR-D-software?
The OCR-D/core framework is fairly light
compared with other interoperability platforms. System requirements therefore
depend on the actual processors to be used and the scale of the operation. It
is possible to use OCR-D on commodity hardware such as desktop PCs and laptops
but can also be deployed to massive servers or even single-board computers.
However, OCR workflows can be very memory-intensive, in particular when working with large neural network models that have to be loaded into memory. We recommend at least 16 GB of RAM to support even the most demanding workflow steps.
Another bottleneck for OCR workflows is input/output. We recommend storing data on SSD instead of HDD.
CLI
How can I find out the version of OCR-D software?
To find the version of the OCR-D/core framework installed, run the ocrd CLI
with the --version flag:
$ ocrd --version
ocrd, version 2.2.2
All OCR-D processors also support the --version flag, e.g.:
ocrd-tesserocr-recognize --version
Version 0.7.0, ocrd/core 2.2.2
How do I get help on ocrd CLI commands?
Every command and subcommand of the ocrd CLI tool supports the --help
option to print a description, arguments and options:
ocrd --help
ocrd workspace --help
ocrd workspace add --help
How do I get help on OCR-D processors?
All OCR-D-compliant processors support the -h/--help flag as well:
$ ocrd-tesserocr-recognize --help
How can I specify parameters on the command line?
Parameters to an OCR-D-compliant processor must be specified in the JSON syntax. The JSON data
can be passed to a processor with the -p CLI option, which can be either the filename of a file containing the JSON data or the JSON data itself:
ocrd-tesseract-recognize -I IN -O OUT -p '{"model": "Fraktur"}'
# same effect:
echo '{"model": "Fraktur"}' > /tmp/params.json
ocrd-tesseract-recognize -I IN -O OUT -p /tmp/params.json
How do I specify multiple input/output file groups?
You can specify multiple file group names for both input and output by joining
the names with a comma (,).
ocrd-tesserocr-recognize -I DEFAULT,REGIONS -O OCR-TESSSERACT
This would instruct ocrd-tesserocr-recognize to take images from the
DEFAULT group and region-segmented layout information from the REGIONS
group.
How to configure logging?
How to stop tensorflow logging spam
Another thing that needs to be added to tame TF is
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'– before the tensorflow module gets imported.
To achieve the same, run this before executing a TF-based processor in the
shell (or even add it to your $HOME/.bashrc to set this permanently):
export TF_CPP_MIN_LOG_LEVEL=3