With Score Visualization for Shape Matching in HALCON 25.11, users gain increased transparency when setting up shape matching applications. Instead of only returning an overall score, the feature provides a breakdown of how different model parts contribute to the final result. By configuring color-coded bins, users can immediately see which areas match well and which perform poorly, for example due to shadows or unwanted textures. This visual feedback makes it much easier to refine models, remove problematic parts, and optimize applications – a major usability advantage especially for non-expert users.
The feature can also support advanced scenarios in robotics, helping determine which object in a stack is least covered and should be picked first.
With new Deep OCR recognition models in HALCON 25.11, text reading becomes faster and more resource-efficient without compromising accuracy. The models deliver up to 50× faster inference on embedded devices. All models are pretrained by MVTec on industrial image data, and include the proven alignment preprocessing, which improves recognition when text varies in position or orientation. Thanks to their optimized architecture, they enable real-time OCR applications on low-power devices while maintaining high accuracy. This makes the models ideal for demanding inline applications such as serial number inspection, label verification, or lot tracking OCR tasks, across industries from logistics and packaging to pharmaceuticals, consumer goods, and medical technology.
With HALCON 25.11, MVTec adds support for the MobileNetV4 series, an efficient new generation of deep learning models optimized for resource-constrained systems and edge devices. These models support both classification and object detection tasks and deliver high accuracy while maintaining low computational requirements. Users benefit from fast inference times, lower system costs, and straightforward integration into existing HALCON projects. All models are pretrained by MVTec, ensuring strong performance for various downstream tasks such as quality inspection, product classification, presence detection, and surface defect analysis. Typical industries include automation, electronics, packaging, food, and medical technology.
With HALCON 25.11, code reading and print quality inspection (PQI) become even more robust and versatile.
QR code detection has been improved for challenging cases such as curved or deformed surfaces. A more powerful candidate search significantly raises the detection rate, while runtime has been reduced for standard scenarios – enabling reliable reading in industries like logistics, packaging, food production, and bottle labeling.
The bar code reader has also been enhanced for Code 128 and GS1-128, making it more tolerant to irregular bar widths caused by printing variations or local distortions. This increases decoding reliability across diverse industrial applications.
In addition, HALCON now supports the latest print quality inspection standards ISO/IEC 15415:2024 and ISO/IEC 29158:2025. This ensures code quality can be verified according to the most up-to-date requirements in sectors such as pharmaceuticals, food, and logistics.
Together, these enhancements provide compliance, long-term process stability, and higher robustness across a wide range of industrial code reading applications.
With HALCON 25.11, MVTec provides Software Bills of Materials (SBOMs), giving users transparent insight into the software components included in the product. SBOMs are becoming a key requirement under new regulations such as the EU Cyber Resilience Act and are increasingly demanded in process- and safety-critical industries.
By providing SBOMs directly with HALCON, MVTec simplifies compliance and reduces workload for customers. Delivered as machine-readable SPDX JSON files, SBOMs make it easier to perform vulnerability and license analyses, fulfill regulatory obligations, and react quickly to newly discovered risks. The result is less integration effort, lower long-term costs, and greater confidence in meeting both regulatory and customer requirements.
