How AI-Powered Label Scanning Reads Like a Human (But Faster)

Imagine you're standing in a grocery aisle, searching for a punnet of fresh raspberries...

Instantly, your brain picks out the expiry dates on the produce labels. Without even thinking about it. You ignore the packing date printed next to it, the barcode below, the weight, the price, and the nutritional information panel.

Now ask a barcode scanner to do the same thing. It’s impossible.

Traditional barcode scanning follows a simple contract: one barcode in, one data string out. Point, scan, done. That works beautifully when all the data you want is contained in one barcode on a simple label.

But walk through any real-world store and you'll find labels that laugh in the face of that simplicity. Electronics packaging with multiple barcodes (product code, batch number, serial). Food labelled with printed weights and expiry dates. Batch and lot numbers. And did I mention price labels?

Check out this montage and you’ll see what I mean. Labels aren’t even standardized. Layouts shift depending on retailer, product category, even the season.

For frontline workers scanning hundreds of items a day, this complexity adds up fast. They scan one barcode, manually type in the weight, visually check the expiry date, scan another barcode for the serial number. Repeat. All day.

That's the problem we built Smart Label Capture to solve. Powered by the Scandit AI Engine, it lets users scan an entire label with one press, and returns a complete, structured data set of exactly the fields their workflow needs.

Not everything on the label. Just what matters for the task at hand.

I've written before about using AI to scan multiple barcodes simultaneously. Smart Label Capture required something different. We taught machine learning models to interpret labels the way humans do, by understanding context, not just reading characters.

The hardest part isn't even reading the text. It's knowing which text to read.

That expiry date your brain effortlessly picks out and reads? Like many other things to do with vision, what’s easy for you is a hard engineering challenge.

Even using optical character recognition (OCR) to read an expiry date is difficult. OCR that works just fine on a typed document struggles when faced with the myriad of unstandardized formats, abbreviations, printing methods and fonts on everyday labels.

But that isn’t even the hardest part. Before you even start decoding text, you need to find the correct text to decode in a very busy camera frame.

That frame might also contain a packing date, a manufacturing date, a supplier name, a lot number that looks suspiciously like a date, and a QR code encoding a timestamp.

In the image below, you can see Smart Label Capture capturing barcode data, net weight, and unit price, while ignoring total price, packing date, sell by date, and supplier information.

Knowing what information not to return is as important as knowing what data to capture.

Then you need to map that result to a field the target application understands. Your backend doesn't want the string "2.17" floating in isolation. It needs weight_kg=2.17, combined with price=4.50 and the product barcode.

All at the same time, and in a structured format ready to feed into inventory systems or point-of-sale software.

In other words, scanning complex labels relies on meaning, not just data extraction.

Capture all label data in a single scan

Explore Smart Label Capture

How Smart Label Capture works

The Scandit AI Engine distinguishes between different label fields — and label types — by understanding visual cues. In human terms, we call these "keys".

When you look at a label, you're constantly using keys. You see a dollar sign, and your eyes jump to the number next to it. You see "EXP", and you know the date that follows matters for freshness.

We designed Smart Label Capture to apply the same reasoning, at machine speeds, using a combination of OCR, machine learning, and computer vision.

It searches for visual cues — text positioned near barcode candidates, formatting patterns, spatial relationships. Then it uses those cues to classify nearby values according to specific workflows, such as:

• Format checks: Does the captured value match an expected pattern? A valid weight format, a price with two decimal places, a date in DD.MM.YYYY or MM/DD/YYYY?
• Context checks: Does the value sit near the correct cue text? Is a number positioned beside "Net Weight" or "Gross Weight"? Does it align with the expected label structure for this product category?
• Workflow checks: Do multiple extracted elements agree with each other and with the requested workflow? For VIN scanning, that's a barcode with alphanumeric text positioned above it. For weighted grocery items, that's weight + price + product identifier, with expiry date optional depending on the category.

Smart Label Capture ships with several pre-built label types covering common labels, such as price (pictured below), expiry date, packing date, weight, serial number, IMEI, and VIN number.

It also supports fully custom labels. With a bit of work, it will handle any combination of fields, any position, any layout you throw at it.

What does AI-powered label scanning look like in practice?

Our goal was never to "return everything." We designed Smart Label Capture to return only the fields necessary for a given workflow.

Weighted items at a grocery checkout? You need weight and price, plus a product identifier. That's it.

Fresh goods in a warehouse receiving dock? Expiry date and lot number become critical. Weight might not matter at all.

Electronics inventory with serial tracking? You need UPC number, serial number, IMEI1, IMEI2. As you can see in the image below, these are all encoded as separate barcodes.

The Scandit AI Engine understands what's needed for each context, and compresses what used to be multi-stage workflows — scan, type, verify, scan again — into a single action.

That results in:

• Fewer scans, because Smart Label Capture captures multiple barcode and text elements at once, not sequentially.
• Faster per-item capture, because users don't need to decipher labels or manually enter information
• Higher accuracy and fewer downstream corrections by eliminating manual data entry.

One of our US customers used Smart Label Capture to prevent undercharges on weighted items. They protected $1.3M in annual revenue and gained 500 hours annually through 7× faster picking.

Not 7% faster. Seven times faster.

Why AI-powered label scanning belongs in your workflows

Do your scanning workflows still rely on capturing barcodes only — and pushing the rest of the cognitive load onto frontline workers? Then you're paying for it in lost time, downstream errors, and employee frustration.

Smart Label Capture works because it understands the semantics of label elements, not just their pixel patterns. It treats every label as the sum of its parts, not as separate, disconnected pieces of information.

That leads to faster capture times and higher downstream accuracy. And, perhaps most importantly, frontline workers who can focus on decisions that actually require human judgment, instead of fighting with their tools.

“With Scandit, we enabled associates to scan a large area of shelves and easily identify incorrect prices. We also built a capability to allow one-touch tag printing to update prices in real time.”

Lance Martel, CIO, Staples Canada

Learn more

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