When a vendor says “97% accuracy,” the number only means something once you know what they’re measuring. The strongest intelligent document processing systems, Eondocs included, report up to 97% field-level accuracy across document types – and field-level is the measure that matters. This post explains what that number really means, how it’s reached on messy real-world documents, and how to read accuracy claims with a critical eye.
Accuracy can be measured three ways, and they’re not interchangeable. Character-level accuracy asks how many individual characters were read correctly – a flattering number, since most characters in any document are easy. Document-level accuracy asks whether a whole document came out perfect, which is a harsh bar nobody likes to quote. Field-level accuracy sits in between and is the most useful: it asks how many of the specific fields you care about – invoice number, total, date, vendor – were pulled out correctly. A system can have very high character accuracy and still get fields wrong if it labels the right number as the wrong field, so field-level accuracy is the figure that actually predicts how much correcting you’ll be doing.
Reaching the high-90s at field level takes several steps working together. Pre-processing cleans up the input – straightening, de noising, and normalising the document so reading starts from the best possible image. The recognition engine then reads the text, and the classification and extraction steps work out what kind of document it is and where each field lives. Validation checks each value against expected formats and rules – a date that isn’t a real date, or a total that doesn’t add up against the line items, gets flagged rather than quietly passed through. That combination of clean input, layout-independent extraction, and rule based checking is what pushes field accuracy up to and past the 97% mark.
No system is perfect on every field of every document, which is why confidence scoring matters just as much as raw accuracy. Each field gets a confidence score. High-confidence fields flow straight through to your ERP; only the low-confidence ones get sent to a person for a quick look. So the rare mistakes are caught exactly where the system was unsure, rather than scattered invisibly through your data. In practice, that’s what makes high-volume automation safe – you’re not stuck choosing between checking everything and checking nothing.
Clean, born-digital PDFs are easy. The documents that matter are the hard ones — scanned at an angle, faxed, photographed, handwritten, or low-resolution. A meaningful accuracy figure is one measured across that messy reality, not a tidy demo set. So when you’re evaluating a system, test it on a real sample of your own worst documents, because that’s where the difference between products shows up.
Ask three questions of any accuracy claim. First, what level is it measured at – field, character, or document? Insist on field-level. Second, across what documents – clean samples or real-world variety? Third, does the system flag its own uncertainty with confidence scoring, so mistakes get caught instead of hidden? A vendor confident in its accuracy will be happy to prove it on your documents.
For the full picture of how extraction works end to end, see the pillar guide to intelligent document processing software.
