Ravenwood, a linerless label technology company, has announced the launch of two software applications for its VXR vision pack inspection system.
The introduction of the new ‘Anti-Reflection’ and ‘Deep Learning’ technologies follows the recent integration of a seal contamination detection system.
Go deeper with GlobalData
Discover B2B Marketing That Performs
Combine business intelligence and editorial excellence to reach engaged professionals across 36 leading media platforms.
These enhancements aim to improve the accuracy and efficiency of preprint and linerless label analysis on packaging, particularly beneficial for the meat packing industry’s shift from modified atmosphere packaging towards flow wrap packaging.
The company’s continuous development of its X-ray inspection and vision technologies is reportedly driven by the need to comply with industry demands, regulatory requirements, and technological advancements.
Its new Anti-Reflection technology is designed to reduce glare and reflections that often hinder the imaging process of flow wrap packs.
Ravenwood’s solution involves a combination of advanced image processing software and a novel illumination method, which can be retrofitted into existing systems or included as an optional feature in new orders.
US Tariffs are shifting - will you react or anticipate?
Don’t let policy changes catch you off guard. Stay proactive with real-time data and expert analysis.
By GlobalDataMeanwhile, the Deep Learning technology, now a standard feature in new VXR systems, enhances the system’s optical character recognition (OCR) capabilities.
It allows for the accurate decoding of various font styles, including those produced by inkjet dot matrix printers, which are notoriously difficult for traditional OCR software to read.
This technology, powered by HALCON software, is user-friendly and reduces the need for extensive operator programming.
It has already been successfully integrated into several customer sites, demonstrating its effectiveness in reducing unnecessary pack rejections and food waste.
Ravenwood noted that both technologies can be retrofitted into existing Vision systems.
