Size AI's Label Scanner reads any garment label and pulls 15+ structured data points, including brand, multi-format sizing, fabric composition, care symbols, and 5 stretch levels. It holds up on faded and vintage tags, and it runs on-device, so it works with no signal.
One inside-collar tag can print three sizing systems at once: W30 L32, an Asian "Size L" and an EU 42. Typed into a listing by hand, that is three chances to get the size wrong, and every value is already on the label.
That gap is the whole reason the Label Scanner exists. Point the camera at any garment label, hold steady for a second, and Size AI extracts more than 15 structured data points before you can put the phone down. Brand. Multi-format sizing. Fabric percentages. Stretch level. Care symbols. Manufacturing details. The label always told the buyer that information. Now it tells the listing too.
What follows is what the Label Scanner pulls, how to scan a label cleanly, and a handful of edge cases worth knowing about.
What the scanner actually does
Point at any label and the on-device OCR engine reads the text. A second model classifies what each piece of text means: this string is a brand, this is a size in EU format, this is a fabric percentage, this row of pictograms is a wash code. The structured output saves to the garment record automatically.
A single scan typically lifts 15+ data points off a single tag. The OCR holds up across faded labels, vintage tags, and the kind of warped print that shows up after a few cycles in the dryer. It runs on-device, which is why scanning works in a thrift-store basement with no signal.
What gets extracted
Scanning a clean label produces:
| Field | What it reads |
|---|---|
| Brand and model | Full names plus model where available, like "Levi's 501" or "Patagonia Better Sweater" |
| Multi-format sizing | W30L32, Size L, EU 42, UK 10, all read off the same tag where present |
| Fabric composition | Each fiber and its exact percentage, like Cotton 84%, Polyester 15%, Spandex 1% |
| 5-level stretch analysis | Minimal, Light, Medium, High, or Maximum, derived from fabric content and construction |
| Product and style codes | Style numbers, item numbers, and the SKU-style identifiers brands print alongside the logo |
| Manufacturing details | Country of origin, production season, factory codes, RN numbers |
| Care instructions and symbols | Wash temperature, drying method, ironing temperature, plus the pictograms most buyers don't bother to decode |
Care instructions matter more than they sound. They're the second-most-asked question in marketplace messages after sizing. Including them in the listing description preempts the message thread.
How to scan a label
The flow lives in the Results screen after a measurement capture.
- 1
Tap "Scan Label"
The camera view opens with a focus frame in the center.
- 2
Fill 50–75% of the frame with the label
Hold the phone parallel to the tag, not at an angle. Daylight is best. Avoid glare on glossy tags.
- 3
Hold steady for one to two seconds
The scan completes automatically when the OCR is confident. No shutter button.
- 4
Review the extracted fields
The app shows every detected data point with a confidence score. Edit anything that looks off, then save.
- 5
Scan additional labels for the same garment
Some pieces have a brand label inside the collar, a fabric content label on the side seam, and a care label in the wash bag. Scan each one. The app merges the data into a single garment record.
Scanning one clean label takes one to two seconds. Multiple tags per garment add up to maybe ten seconds total. End to end, the saving versus typing data into a listing template runs about thirty to sixty seconds per garment.
Tips that actually move the needle
- Flat label first. The OCR handles light bending, but a tag folded at the seam often misreads sizing. Pull the label flat against the table or against the inside of the collar before the scan.
- Multiple tags merge cleanly. Scan the brand label, then the fabric tag, then the care tag. The app reconciles them into one garment record without overwriting fields that already have higher-confidence data.
- No internet required. The OCR pipeline runs on-device. Scanning works in a thrift store, a warehouse, a flea market, anywhere.
- Manual fallback for missing labels. Cut tags and completely missing labels can't be scanned. The Garment Detail screen has a manual entry fallback for every field the scanner would have populated.
Note: Any language on the tag
The OCR reads labels in any language and returns the extracted fields in English, so a Japanese care label and a French fabric tag land in the same structured record.
