HALCON 20.11 launch: Improved surface-based 3D-matching and Deep OCR

12 Nov 2020

HALCON 20.11 is due to be released on November 20, 2020 and it’s packed with many new features such as deep learning edge detection, Deep OCR and faster 3D matching – all of which will help to further enhance the performance of your machine vision application.

This release will be available in both, the Steady and Progress editions, which means Steady edition users will now have access to all the features released in the past three Progress versions.

Newest HALCON Features



The latest version of HALCON includes the introduction of DotCode reading, a 2D code that is used in many high-speed applications where a code needs to be printed and read/verified during production runs.


Localized grouped characters with Deep OCR

HALCON 20.11 also includes another new feature called Deep OCR. MVTec developed this holistic deep-learning-based approach for optical character recognition (OCR) to localise and recognise numbers and letters much more robustly – regardless of their orientation, font type, and polarity.

The ability to group characters automatically also allows the identification of whole words. This improves the recognition performance significantly and avoids the misinterpretation of characters with similar appearances.


Edge detection is greatly improved in HALCON 20.11 by harnessing the power of deep learning. The new and unique method for robustly extracting edges is perfect in scenarios where there may be a large number of edges. The deep-learning-based edge extraction function can be trained with only a few images to reliably extract only the desired edges – greatly reducing the programming effort for processes of this type.

Even with low contrast and high noise situations, the pretrained network is able to robustly detect edges, right out of the box. This makes it possible to also extract edges that cannot be identified with conventional edge detection filters.

In addition, “Pruning for Deep Learning” now enables users to subsequently optimize a fully trained deep learning network. They can now control the priority of the parameters speed, storage, and accuracy and thus modify the network precisely according to application-specific requirements.


Scene with many objects or edges

The latest release demonstrates significant improvements in the 3D environment as well. Edge-supported, surface-based 3D matching is now significantly faster for 3D scenes with many objects and edges.

Usability has also been improved by removing the need to set a viewpoint. All of this is further enhanced with parameters that are estimated automatically, improving user-friendliness and performance in challenging situations that have low contrast and/or high noise.


HALCON 20.11 makes things much easier not only for users but also for developers. A new language interface enables programmers who work with Python to seamlessly access HALCON's powerful operator set.

In addition, the integrated development environment HDevelop has been given a facelift. It now offers more options for individual configuration, such as a modern window docking concept. Moreover, themes are now available to improve visual ergonomics and adapt HDevelop to personal preferences.

Screenshot of HDevelop in dark mode