Automated inspection of pharmaceutical labels is a critical part of an automated production process in the pharmaceutical and medical device industries. The inspection process ensures that the correct labels are applied to the right products and that the labels contain relevant and validated information. These are generally a combination of Optical Character Recognition (OCR), Optical Character Verification (OCV), Print Quality Inspection and measurement of label positioning. Some manufacturers also require a cosmetic inspection of the label for debris, inclusions, smudges and marks. The use of vision inspection systems can significantly improve the efficiency and accuracy of the automation process, while also reducing the potential for human error.
Automated vision inspection systems can also help to ensure compliance with regulatory requirements for labelling, and provide manufacturers with a cost-effective and efficient way to improve the quality of their products. With increasing pressure to improve production efficiency and reduce costs, more and more pharmaceutical and medical device manufacturers are turning to automated vision inspection systems to improve their production processes and ensure quality products for their customers.
Over the last few years, more vision inspection systems for pharmaceutical label checks have been adapting to the use of deep learning and artificial intelligence neural networks, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs).
Using AI and deep learning for optical character recognition (OCR) can significantly improve the performance of automated inspection systems for pharmaceutical labels. Traditional OCR systems rely on pre-defined templates and rules to recognize and interpret text on labels, which can be limited in their ability to recognize text accurately in different fonts, sizes, and layouts.
AI-based OCR systems, such as those using deep learning, can be trained on a large dataset of labelled images, allowing them to learn and recognize different fonts, sizes, and layouts of text. This makes them more robust and accurate in recognizing text in real-world scenarios, where labels may have variations in their appearance. The deep learning machine vision systems can also be trained to recognize text that is partially obscured or distorted, which is a common problem in real-world scenarios. This allows the system to make educated guesses about the text, which can improve its accuracy and reliability.
On the flip side, while the level of recognition may improve for hard-to-recognise situations for traditional machine vision inspection, the system also must be validated to set criteria for FDA/GAMP validation. How can this be achieved for a neural deep learning system?
It is true that gathering data for training an AI-based vision inspection system can be a hassle, but it is a crucial step to ensure the system’s performance. One way to overcome this is by using synthetic, validated data, which can be generated using computer-generated images. This can reduce the need for natural images and allows a broader range of variations to be included in the dataset. These synthetic images could be tested and validated on a traditional machine vision set-up, before then transferred to the training set for the AI-based vision inspection. Another way is to use transfer learning, in which a pre-trained validated model is fine-tuned on a smaller dataset of images specific to the task. This can significantly reduce the amount of data and resources needed to train a new model.
In conclusion, validated industries such as medical devices and pharmaceuticals continue to adapt to new, robust methods for traceability and quality print checking. Deep learning is evolving to meet the unique validation requirements of these industries.