At its core, optical character recognition (OCR) technology allows machines to read and interpret printed text, a critical task in various industries. In the manufacturing industry, OCR plays a vital role in automating text verification processes, ensuring regulatory compliance and maintaining product traceability?.
For manufacturers, particularly in food and beverage, maintaining accuracy in labeling is paramount. Product text verification, such as date and lot codes, is essential for tracking inventory, ensuring FDA compliance and protecting consumer safety. A mislabeled product could lead to costly recalls or put consumer health at risk?. In this high-stakes environment, OCR technology serves as a critical control point that prevents errors before they escalate into major issues?.
AI-driven OCR
Traditional OCR systems, while effective, struggle with several limitations. Common challenges include poor lighting, inconsistent text formats and the curvature of or reflective surfaces on product packaging. These issues often result in inaccurate text recognition, leading to operational delays and costly mistakes?.
One common issue with traditional OCR is the presentation of text to the camera in a production environment. Proper lighting, product control on conveyors and the correct camera angles are all critical factors that can affect OCR accuracy?. In many cases, space constraints and product materials, such as plastic wraps or glossy surfaces, make text difficult to read. Additionally, inconsistencies in printer quality can lead to unreadable codes.
Fortunately, AI-powered OCR tools can overcome these challenges. By using advanced algorithms, these systems can account for variations in lighting, angle and text quality. They are also capable of reading text on curved or reflective surfaces—conditions that would normally cause traditional OCR systems to fail. This flexibility makes AI OCR particularly valuable in food and beverage manufacturing, where products often have irregular surfaces or are exposed to non-standard lighting conditions?.
For example, AI OCR tools excel in reading laser or inkjet codes, which are traditionally difficult to interpret. These AI systems can handle reflective surfaces, curved containers and even distorted text without the need for extensive manual intervention. The result is a significant reduction in development time, fewer false rejects and a more user-friendly system that operators can easily learn to use?.
Practical AI OCR applications
The real-world application of AI-driven OCR in manufacturing is not just theoretical—it’s already making a tangible difference. Gray Solutions has implemented several AI-powered OCR tools that demonstrate how these systems can improve manufacturing quality control.
One notable example is the use of Cognex’s ViDi EL deep learning OCR tool for verifying date and lot codes on pet food cans. This tool allows manufacturers to quickly set up a deep learning model, enabling fast and accurate text verification. Even in challenging environments with poor lighting or reflective surfaces, the system can deliver reliable results, reducing the risk of mislabeling.
Another advanced tool used by Gray Solutions is the Matrox Design Assistant, a flowchart-based program that uses pre-trained deep learning models to verify product text. This tool is particularly useful for reading non-standard fonts on packaging, such as those found on Ben & Jerry’s ice cream lids. By minimizing the need for manual adjustments, this system streamlines the production process while ensuring text accuracy?.
These tools represent just a fraction of the AI OCR solutions available today. With their ability to adapt to a range of production environments and their user-friendly design, AI-driven OCR systems are proving to be indispensable for manufacturers seeking to improve quality control?.
The future of AI OCR in manufacturing
As AI-driven OCR technology continues to evolve, its applications in manufacturing are expected to expand even further. The global market for machine vision technologies, which includes OCR, is projected to grow from $6.9 billion in 2023 to $7.8 billion by 2029, reflecting a growing demand for automated quality control solutions?. Within this market, OCR plays a significant role in automating inspections, reducing costs and improving product traceability.
For manufacturers in the food and beverage industry, investing in AI OCR is not just a matter of improving operational efficiency—it’s about safeguarding consumer trust. By ensuring that products are correctly labeled and traceable throughout the supply chain, OCR technology helps manufacturers maintain compliance with stringent industry regulations?.
For manufacturers looking to revolutionize their production processes, the time to invest in AI-driven OCR technology is now. With its ability to overcome traditional OCR challenges and its proven success in real-world applications, AI OCR is set to redefine the future of manufacturing quality control.
Dean Elkins is senior director of robotics and vision, and Michael Haymes is vision engineer at Gray Solutions, a certified member of the Control System Integrators Association CSIA). For more information about Gray Solutions, visit its profile on the Industrial Automation Exchange.
