Deep OCR Training
|HALCON`s Deep OCR enables users to efficiently
solve text reading applications in a multitude of use cases. In HALCON
22.05 the training functionality has been extended to offer application
specific training with the user's own application dataset. This extends
the ability of users to solve complex applications like reading text
with low contrast (e.g., on tires).
Another advantage of HALCON's Deep OCR is with the ability to train
very rarely used special characters or printing styles. Training for
Deep OCR significantly improves the performance and usability and
makes applications more robust.
Global Context Anomaly Detection
|HALCON 22.05 allows for detection of logical
anomalies in images. This builds on the deep learning anomaly detection
feature that HALCON software already provides. Previously, it was
possible to detect local, structural anomalies. However, the new Global
Context Anomaly Detection is a technology that can understand
the logical content of the entire image. As with HALCON's existing
anomaly detection, the Global Context Anomaly Detection
tool only requires good images for training, eliminating the need
of data labeling. This technology makes it possible to detect entirely
new variants of anomalies like missing, deformed, or incorrectly arranged
Print Quality Inspection improvements
Print Quality Inspection (PQI) refers to the evaluation and
grading of certain aspects of printed barcodes and data codes according
to international standards.
HALCON supports 1D and 2D codes and in HALCON 22.05, the PQI
of data codes has been further improved. It is now up to 150% faster.
In addition, the module grid determination for print quality inspection
of ECC 200 has been improved.
Other new features
New operator that performs adaptive histogram
equalization to improve contrast locally in an image.
HDevelops Matching Assistant now
generates the code based on Generic Shape Matching.
A number of new tools have been made faster
Small improvements to HDevelop
|A typical issue associated with current
AI Accelerator hardware is the proprietary API's that currently have
no set standard. To combat this MVTec introduced the AI² Interface.
AI² is future-proof and allows the impentation of plug-ins for
specific hardware. Currently TensorRT and OpenVINO tookits
are available, but expect more to come.
|OpenVINO is a toolkit for high-performance
deep learning on Intel hardware. The toolkit extends workloads across
Intel® hardware (including accelerators) and maximizes performance.
OpenVINO provides plugins for Intel hardware such as:
Intel VPUs (e.g., Intel® Movidius Neural Compute
Using the OpenVINO toolkit plugin can shorten system runtime
and lower memory consumption.
|Code128 Reader improvements.
|HALCON 21.11 allows codes with a larger
amount of blur to be read.
|HALCON 21.11 delivers a new technology
called instance segmentation. This combines semantic segmentation
and object detection. Instance segmentation allows objects to be assigned
to different classes with pixel accuracy. This technology is useful
in applications where objects are very close to each other, touch
|Improved Dictionary Handling
|Dictionaries make it easy and convenient to manage
complex data in HALCON. For example, different data types such as
images, Regions Of Interest and parameter settings can be bundled
in a single dictionary. Dictionaries can now be initialized with a
single operator call and the syntax for adding and retrieving elements
has been simplified. In addition, the auto-completion now also suggests
the keys contained in the dictionary, which further speeds up and
simplifies working with dictionaries.
|Future-proof interface for shape-matching
|HALCON 21.11 provides Generic Shape Matching that
offers user-friendly access to MVTecs industry-proven shape
matching technologies. In this version users can now implement their
solution much faster as the number of required operators is significantly
reduced. Further, existing functionalities are enhanced to increase
usability. e.g. the clutter feature has been integrated, handle inspection
has been optimised, and additional parameters have been integrated
and included in the automatic parameter estimation.
| HDevelop now offers a dark mode.
Canvas now offers a zooming tool as well as the ability to
fit all graphic windows into view.
The bar code reader now allows users to set a maximum bar code
|HALCON 21.05 Progress
Optical Character Recognition improvements
OCR is a holistic deep-learning-based approach for OCR. Compared to
existing algorithms, Deep OCR can localize characters much more robustly,
regardless of their orientation, font type and polarity. The performance
and usability of Deep OCR have been substantially improved in version
21.05. The handling of big images has been upgraded and the subsequent
results now contain a list of character candidates with corresponding
confidence values. This can be used to improve the recognition results.
21.05 introduces Generic Shape Matching which makes MVTec's shape
matching technologies more user-friendly and future-proof. In this
version users can now implement their solution much faster as the
number of required operators is significantly reduced.
new window docking has been improved in HALCON 21.05. Users are now
enabled with more options to control the position when floating windows
are opened. Previously the top corner of the main screen has been
used as the origin. Now its also possible to select the upper
left corner of the screen where HDevelop is located, or the upper
left corner of HDevelop itself. Additionally a new feature called
"Auto-hide" has been introduced, this feature allows users
to quickly shrink widgets into the side bar when they don't need them
and bring them back when necessary.
Deep Learning framework
21.05 includes a first version of HALCON Deep Learning framework.
This allows experienced users to create their own models within HALCON.
barcode reader improvements
HALCON 21.05 the subpixel barcode reader has been improved for low-resolved
codes. The decoding rate for those can now increase up to 50%.
of basic operators in 2D and 3D for fast and robust preprocessing
this version the 3D point cloud sampling now supports a new mode called
"furthest point" which typically results in a more uniform
sampling of a 3D object. The 3D point cloud smoothing has been extended
by a new mode that uses information from the XYZ-mappings. 3D point
cloud smoothing can be used as a preprocessing step to smooth point
clouds and remove noise
|HALCON 20.11 Steady and Progress
20.11 offers an edge-supported surface-based 3D-matching which is
significantly faster for 3D scenes containing multiple objects and
edges. Additionally, the usability has been improved by removing the
need to set a viewpoint.
DotCode and Data Matrix Rectangular Extension
data code reader in HALCON 20.11 has been extended by the new code
type, DotCode. This type of 2D code is based on a matrix of dots.
It can be printed very quickly and is especially suitable for high
speed manufacturing lines.
the ECC 200 code reader now supports the Data Matrix Rectangular Extension
OCR is a holistic deep-learning-based approach for OCR. This new technology
brings machine vision one step closer to human reading. Compared to
existing algorithms, Deep OCR can localize characters much more robustly,
regardless of their orientation, font type and polarity.
|Shape based matching enhanced
HALCON 20.11, the shape-based matching tool has been improved. This
increases usability as well as the matching rate. It also increases
robustness in low contrast and high noise situations.
|Changes to HDevelop
20.11 has implemented more viewing options for individual viewing
changes feature a dark mode with white text on a black background.
A new modern window docking concept allows windows to be repositioned.
Moreover themes are now improved to improve visual ergonomics and
to suit individual preferences.
Learning edge extraction
Learning edge extraction is a new and unique method to robustly extract
edges. There are two major use cases for this new tool. The first
case is where images have a wide variety of edge types visible. The
tool can be trained with only a few images to reliably extract all
desired edges. Hence the programming effort to extract specific types
of edges is highly reduced. The second major use case, is where edges
are low contrast and in high noise situations. The tool is innately
able to robustly detect these edges. It makes it possible to extract
edges that traditional edge detection filters cannot detect.
|New HALCON/Python Interface
20.11 introduces a new HALCON/Python interface. This enables developers
who work with Python to easily access HALCON's powerful operator set.
|HALCON Progress 19.11
|Additonal Deep Learning
technologies for a broader range of applications
|Providing increased accuracy and an extended choice of compatible
|HALCON 19.11 offers a new anomaly detection system
which facilitates the automated surface inspection for, e.g. detection
and segmentation of defects.
|HALCON's newly implemented Grad CAM supports you
in analysing which parts of an image have a strong influence for the
inference into a certain class. Furthermore, the Grad-CAM heatmap
is very fast compared to the previously offered method.
|Supportive of ONNX Format
|Within the 19.11 edition CNNs can be
exported into the ONNX (Open Neural Network Exchange) format. From
19.11 onwards HALCON is able to read data on ONNX format allowing
to use previously created 3rd party networks within HALCON.
|HALCON 19.11 is significantly accelerated
for multi-core systems.
|Generic box finder
|A new feature in HALCON 19.11 is the
generic box finder for pick and place applications. This feature allows
users to find boxes of different sizes based on 3D space eliminating
the need to train a model for each required box size.
HALCON 19.11 brochure
|HALCON Steady 18.11
More new powerful
|Two editions available- HALCON
Steady (as successor of HALCON 13) offered as a perpetual
license and HALCON Progress - available as a 12 month
subscription with a fast six-month release cycle.
Users are able, seamlessly, to train their own classifier using pretrained
CNNs included in HALCON which have been highly optimised for industrial
|ECC 200 Code Reader Improvements
Improved recognition rate can be increased by 5% (data based on MVTec's
internal ECC 200 benchmark consisting of more than 3,700 images from
various applications). The ECC 200 reader is now able to read codes
with disturbed quiet zone and codes against complex backgrounds can
be found and read faster and more robustly.
|Improved Automatic Text Reader
HALCON features an improved version of the automatic text reader,
which now detects and separates touching characters more robustly.
|Improved Bar Code Reader
HALCON now features optimized edge detection, which improves the ability
to read bar codes reliably with very small line widths as well as
those with strongly blurred codes. the quality of the bar codes is
also verified in accordance with the most recent version of the ISO/IEC
|New Data Structure "Dictionaries
HALCON 18.11 introduces a new data structure "dictionary",
an associative array that opens up new ways of working with complex
data. For example, this allows bundling various complex data types
(eg., an image, corresponding ROIs and parameters) into a single dictionary,
making it easier to structure programs
|Easier handling of Variable
Inspect in HDevelop
Double-clicking a handle variable now returns all parameters associated
with the handle and their current settings.
HDevelop provides a new library export that makes the use of HALCON
procedures from C++ and .NET as easy and intuitive as calling any
other C++/.NET function. This new library export also generates CMake
projects which can easily be configured to output project files for
many popular IDEs
|Automatic Handle Clearing
HALCON now makes it much more comfortable to work with handles by
clearing these automatically once they are no longer required. This
significantly reduces the risk of creating memory leaks and makes
writing "safe code" much simpler.
|Surface Fusion For Multiple
3D Point Clouds
HALCON now offers a method that fuses multiple 3D point clouds into
one watertight surface. This new method combines data from various
3D sensors, even from different types like a stereo camera, a time
of flight camera, and fringe projection. This technology is especially
useful for reverse engineering.
HALCON now offers optimized functions for surface-based 3D matching.
These can be used to determine the position of objects in 3D space
more reliably, making development of 3D applications easier.
|Support for Hypercentric Lenses
A new camera model within HALCON now allows the corrections of distortions
in images that were recorded with hypercentric (also known as pericentric)
HALCON 18.11 includes new operators, which enable the user to inspect
specular and partially specular surfaces to detect defects by applying
the principle of deflectometry.
for all machine vision applications
|This powerful machine vision software suits
all types of applications. HALCON 13 not only masters complex 3D machine
vision tasks but also offers supplier's experience, a comprehensive
set of technologies and the flexibility of your choice of hardware.
|3D matching and 3D reconstruction
HALCON views information simultaneously from multiple cameras leading
to a more robust reconstruction than was possible using previous stereo
HALCON's HDevEngine applications can now be debugged directly,
making error tracking, and even remote error-tracking, much easier.
|Robust Identification Technologies
New deep-learning technology achieves higher reading rates than with
previous classification methods. HALCON 13's automatic text reader
is faster and supports reading of dot print characters and barcodes
even if large parts of the code are defective or not visible. Dealing
with blur and distortion is easier.
|Easy texture inspection
Traditionally one the the more challenging machine vision tasks, dealing
with an objects different characteristics has proved problematic.
HALCON 13 offers an easy-to-use texture inspection, that automatically
identifies defects by comparison with an image of flawless materials
HALCON 13 also offers significant speedups for all related technologies,
i.e., shape-based 3D matching, local and perspective deformable matching,
and component-based matching.