What's the difference between OCR and data extraction?

OCR (optical character recognition) is one specific technology used within the broader process of document data extraction, not a synonym for the whole thing. Understanding the distinction matters because "extraction" involves several additional steps beyond just reading characters off an image.

OCR's job is narrowly defined: convert an image of printed or handwritten text into machine-encoded text, character by character. Point OCR at a photo of a document and it returns a string of recognized characters, essentially transcribing what's visually present into text a computer can process further.

Data extraction takes that raw OCR output, along with barcode and chip data where relevant, and turns it into something genuinely useful for a business process. This involves parsing the raw text into distinct, labeled fields, recognizing that a particular string of characters represents a date of birth rather than an expiry date, for instance, based on its position and format within a known document template.

It involves validating that data, checking check digits, confirming formats match what's expected, and cross-referencing multiple sources against each other to catch inconsistencies. And it involves structuring the final output into a clean format, like a JSON object with clearly labeled fields, ready to be consumed by another system.

Put another way: OCR answers the question "what characters are in this image?" Data extraction answers the more useful question "what does this document actually say about this person, and can I trust that information?"

A system that only does OCR without the parsing, validation, and structuring steps would leave a business with a wall of raw, unlabeled text rather than a clean set of verified data fields, not nearly as useful for automating a real workflow like KYC onboarding or age verification.

This distinction is also why document scanning vendors, including ScanDoc, describe their products in terms of data extraction rather than just OCR. OCR is a necessary ingredient, but the value delivered to a business comes from everything built around it: template matching, validation logic, cross-referencing between data sources, and structured output.