Optimized for AI

Source: Birger Morken / Posten Norge AS

Digital stamps are a simple alternative to physical stamps when you are sending letters or parcels. The digital stamp is a digital code that you buy online – write the code on your shipment and send it.

IHPostal developed digital stamps for Posten Norge - digital codes that are optimized - for AI and for humans. They are easy for humans to read and write by being short and concise – and they are optimal for AI recognition due to their design. The codes identify the physical object – for tracking and traceability, and the code is readable even when the code is destroyed, either by the writer (e.g., bad handwriting), or by being smeared across the surface (print on a problematic surface).

The Digital Stamps developed by IHPostal for Posten Norge contributed to Posten Norge earning a place in the Top 5 Innovative Companies in Norway in 2018:

Det er gledelig at vi blir lagt merke til og at eksterne aktører ser at vi setter innovasjon og utvikling høyt på agendaen. Vi er fremtidsrettet og skal være relevante også i fremtiden, tiltrekke oss kompetent arbeidskraft og lage løsninger som gjør hverdagen enklere for kundene våre.

Konsernsjef Tone Wille, Posten Norge

Posten Norge A/S is a Nordic postal and logistics group that delivers integrated solutions in the postal services and employs more than 10,000 employees in Norway, with a turnover of NOK 23,996 mio.

Identification and Tracking

The digital coding schemes used for Digital Stamps can also be used for identifying and tracking physical objects, e.g. mail items, letters and parcels.

The codes can be written as digits and characters, but can also be printed as bar codes or QR codes, or embedded invisibly into a product design in the form of digital watermarks.

10 + 13 = 10 – 13 = 7

The codes are based on sophisticated computing science and mathematics, i.e. algebraic fields, where seemingly counter-intuitive logic like 10 + 13 = 10 – 13 = 7 protect the codes against counterfeiting and ensure reliable detection and recognition.