Automated quality inspection cells help achieve zero-defect production and increase throughput through 24/7 operations. However, unexpected stops in complex, AI-powered cells can pose challenges due to unpredictable scenarios and edge cases.

OCR is a challenging field in image processing. The industry aims to improve accuracy and reliability, but what are the key factors in choosing an OCR system?

In a world where AI vision technology is setting new quality control standards across industries, machines can now detect even the smallest defects in car parts and ensure that every packaged product meets health standards.

AI, despite its hype, often causes delays in manufacturers' automation strategies due to confusion and fear. Fundamentally, AI complements machine vision, which uses handcrafted algorithms needing new formulas and trial-and-error development for each product type or feature.

Today companies record process trends digitally. However, analysis is still conducted in much the same way, with operations staff manually identifying trends. Enter artificial intelligence (AI) and machine learning.

User-friendly software tools that enable even users without experience to evaluate their applications with AI vision and implement them intuitively.