The hard part of national ID OCR isn't the OCR

You wire up OCR for your KYC flow, point it at a national ID card, and get back a clean { name, idNumber, dateOfBirth } . Ship it. Then you onboard your second country — and it falls apart. Fields you mapped don't exist. The name comes back as garbled Latin. The date of birth says the year 2567. Here's the thing nobody tells you when you start: the hard part of national ID OCR isn't the OCR. It's that every country's ID is a different document. A model that reads text off a card is table stakes. Turning 30 countries' cards into data your system can actually use is where the work is. Let me show you the three axes of variation that will bite you, then how to architect so they don't. Axis 1: the fields are different There is no universal "national ID" schema, because the cards themselves don't agree on what to print. A Thai ID card prints the holder's religion . A German ID card prints height and eye color . A Chinese ID card prints ethnicity and the issuing authority. None of these are edge cases — they're core fields on those documents. So the instinct to define one IdCard type with a fixed set of columns is wrong from day one. Either you drop information that some countries consider essential, or you end up with a sparse table full of null s and country-specific special-casing. And it's not just which fields exist — it's what they're called and how they're split. The same "name" concept might come back as a single full-name string on one card and as separate given/family fields on another, sometimes in two scripts at once. Your data model has to treat "the field set depends on the country" as a first-class fact, not an afterthought. Axis 2: the script is different If your users are global, a lot of their names are not in the Latin alphabet — Chinese, Thai, Arabic, and more. The naive move is to transliterate everything to Latin "so it's consistent." Don't. Transliteration is lossy and ambiguous: multiple native spellings collapse to the same Latin form, diacritics