Machine vision automation is not just about scale, though – it’s about accuracy and improving the work humans do as well. The tedious repetitiveness of these tasks contributes to significant error rates and leads to low satisfaction and high levels of turnover, particularly when dealing with handwritten documents which are processable with IDP.
The sophistication of how computer vision is applied in autonomation is not limited to document processing. Video-based facial recognition in security processes, checkout-less supermarkets, and remote equipment identification via drones for inventory management are examples of how computer vision is being leveraged in automation.
the process end-to-end and then provide the input to automating a lot of the work needed to program the digital workers .are a concern cited by organizations when it comes to relying on artificial solutions to undertake certain processes. This is why it’s important to have the right processes in place for each application to ensure the best outcome.
This is partly because it reduces the time and cost it takes to process second opinions but also because, in a growing number of areas, machine vision/AI-based processing of radiology images is more accurate than humans.