HALCON Progress

Why choose HALCON Progress?

  • Receive new HALCON features as soon as they are ready for the market
  • New version ~every 6 months
  • Subscription based (automatic yearly renewal, access to all features released within subscription period)
  • Support during subscription period
  • Maintenance through regular new releases
  • Deep Learning is included

Latest Version: HALCON 21.05

HALCON 21.05 will be released on May 21, 2021. As a HALCON Progress user, you can experience a variety of improvements and new features that will make solving your machine vision task even easier. A first look at this release’s contents below:

  • Deep OCR improvements
  • Generic Shape Matching
  • HDevelop usability improvements
  • HALCON Deep Learning Framework
  • Subpixel bar code reader improvements
  • Improvements of basic operators in 2D and 3D for fast and robust preprocessing
Improved robustness and extended character support for Deep OCR


With Deep OCR, HALCON includes a holistic deep-learning-based approach to OCR. Compared to other algorithms, this technology can localize characters much more robustly, regardless of their orientation, font type, and polarity. With HALCON 21.05, the performance and usability of Deep OCR have been improved. Big images are now handled more robustly and the result now contains a list of character candidates with corresponding confidence values, which can be used to further improve the recognition results. Customers also benefit from an overall improved stability as well as from the fact that they can address a wider range of possible applications, thanks to additional character support.

Industry-proven shape-based matching for robust localization of objects


HALCON 21.05 introduces Generic Shape Matching, which makes MVTec’s industry-proven shape matching technologies even more user-friendly and future proof. By significantly reducing the number of required operators, users can now implement their solution much faster and a lot easier. Moreover, thanks to the unification of HALCON’s different shape matching methods into a single set of operators, users can now integrate new shape-matching-related features more smoothly.

hdevelop screenshot usability improvements
Screenshot of HDevelop


HDevelop’s new window docking has been improved. Users have now more options to control the position where floating windows are opened. Until now, the top left corner of the main screen has been used as the origin. Now it’s also possible to select the upper left corner of the screen where HDevelop is located, or the upper left corner of HDevelop itself.

In addition, HDevelop now also includes a new docking feature called “Auto-hide”. This allows users to quickly shrink widgets they currently don’t need into the sidebar and easily bring them back when needed.

Finally, graphics windows can now be grouped and organized in a much more convenient manner, in the new Canvas window.


HALCON 21.05 introduces a first version of the HALCON Deep Learning Framework. This framework allows experienced users to create their own models within HALCON. With this feature, experts can now realize even the most demanding and highly complex applications in HALCON without having to rely on pretrained networks or third-party frameworks.


HALCON’s subpixel bar code reader is capable of reading codes with very thin bars. In HALCON 21.05, the subpixel bar code reader has been improved regarding low-resolved codes. The decoding rate for those can now increase up to 50%.


In HALCON 21.05, 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. This mode usually leads to a much faster processing time.

Further improvements in HALCON 21.05 include general speedups of basic image operators.

halcon 21.05 point cloud sampling example
Blue: Sampled point cloud; Grey: Original point cloud
halcon 21.05 point cloud sampling example
Orange: Smoothed point cloud; Grey: Original point cloud

Last version: HALCON Progress 20.11

Improved Surface-based 3D-Matching

In HALCON 20.11, the core technology, edge-supported surface-based 3D-matching, is significantly faster for 3D scenes containing many objects and edges.

In addition to this speedup, the usability has been improved by removing the need to set a viewpoint.

DotCode and Data Matrix Rectangular Extension

In HALCON 20.11, the data code reader 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, like those used in the tobacco industry.

Furthermore, the ECC 200 code reader now supports the Data Matrix Rectangular Extension (DMRE).

Deep OCR

Deep 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. The ability to automatically group characters allows the identification of whole words. This strongly increases the recognition performance since, e.g., misinterpretation of characters with similar appearances can be avoided.

Improved Shape-based Matching

In HALCON 20.11, the core technology, shape-based matching, has been improved.

More parameters are now estimated automatically. This increases usability as well as the matching rate and robustness in low contrast and high noise situations.

HDevelop Facelift

For enhanced usability, HALCON’s integrated development environment HDevelop has been given a facelift.

In HALCON 20.11, more options for individual configurations have been implemented, featuring e.g., a dark mode and a new modern window docking concept. Moreover, themes are now available to improve visual ergonomics and to suit individual preferences.

Deep Learning Edge Extraction

Deep learning edge extraction is a new and unique method to robustly extract edges (e.g., object boundaries) that comes with two major use cases.

Especially for scenarios where a variety of edges is visible in an image, it can be trained with only few images to reliably extract the desired edges. Hence, the programming effort to extract specific kinds of edges is highly reduced with this version of MVTec HALCON. Besides, the pretrained network is innately able to robustly detect edges in low contrast and high noise situations. This makes it possible to extract edges that usual edge detection filters cannot detect.


HALCON 20.11 introduces a new HALCON/Python interface. This enables developers who work with Python to easily access HALCON’s powerful operator set.

To learn more about the HALCON Editions, click here.

Contact: sales@lucidimaging.in for more info and pricing.