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Optical character recognition with AI improves label reading

23 November 2023

Mathijs Baron and Feifei Huo argue that Optical character recognition (OCR) powered by artificial intelligence (AI) provides a modern, automated solution to bring label read rates closer to 100%.

A lot of data is contained on a label – addresses, barcodes, expiration dates, dates of production, stock keeping unit (SKU) numbers, batch codes and more. Depending on a warehouse operation, all of this information is potentially critical for the sorting process.

Labels are usually read when items pass through camera tunnels as part of a checking procedure, providing actionable information such as where the item needs to go as well as other crucial data for the logistics process. However, expecting a perfect read every time is unrealistic. Labels can be obscured or damaged, which results in a ‘no-read’. While these may constitute a minority, in a high-volume warehouse operation, they can stack up quickly.

Until recently, a label no-read usually resulted in a slow, costly manual intervention to move the item back onto the right path. OCR can provide the solution. By enhancing imagery from cameras and harnessing customer information to reconstruct and identify key data strings on damaged or obscured labels, the technology ca improve read rates. Furthermore, it can achieve this fast enough for a fully automated process.

Deep learning improves read rates
The Text Vision solution from Prime Vision employs deep learning AI. Whereas many OCR solutions will bring read rates up to 95% or so, Text Vision can push this up to 98 or 99% in real world applications.

It operates by taking images of labels, enhancing them in pre-processing, and then finding the appropriate text block or data string that needs to be read. Text is then extracted and assessed by AI, which uses logic to find the best result. Following this reasoning, a successful read is achieved, enabling a relevant action to be taken without interrupting the automation process.

To make this possible, the AI must be trained through deep learning to identify the correct region of interest containing the relevant field or data string on the label. The system is taught using real world examples of customer labels, so it focuses on the right area. AI can also cross reference extracted information with databases to help with reconstruction. Ultimately, OCR can be optimised to each operation, ensuring that it is applicable to any automatic sorting system.

Identifying barcodes can be quickly achieved with existing scanning hardware, but to reduce OCR computation times, we recommend a dedicated GPU processor. The faster processing speeds mean that even if an item suffers a no-read, it can be solved in such a short timeframe that no manual intervention is required. Instead, the system can respond automatically, seamlessly moving the item onto the next stage of the process in a quick and cost-effective manner.

Mathijs Baron is, International Sales & Business Development, and Feifei Huo is R&D Engineer at Prime Vision.

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