Deep OCR Improvements
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.
Generic Shape Matching
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.
HALCON Deep Learning Framework
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.
