What is intelligent document processing?
A wrong premium figure that passes a spell-check still bills the policyholder the wrong amount.
Intelligent document processing (IDP)
IDP
Intelligent document processing (IDP) is a pipeline that reads scanned documents or PDFs and pulls out specific fields. It then checks those fields against rules or history before a system uses them. The output is a field you can act on, not a wall of recognized text.
Plain OCR converts an image of text into text. IDP goes further. It maps that text to named fields, like invoice total or policy number, then checks each one before anything downstream trusts it.
A modern IDP pipeline usually pairs a vision model or OCR engine for extraction with a separate validation step. That step runs format rules, range checks, or a comparison against another document. Anything it cannot confirm gets flagged.
The distinction matters because extraction and validation fail differently. A model can read a payroll figure correctly and still have transcribed it from the wrong column. Only a check against the expected range, or against a prior audit, catches that.
The accuracy gain lives in the checks, not the model
Most IDP pitches lead with the extraction model: better OCR, a bigger vision model, handwriting support. That is the visible part, and it is also the smaller lever.
We built PremiumAudit.io, an insurance audit platform, to extract figures from payroll records and policy documents and check them before a report goes out. Calculation errors fell 95% after launch. The extraction step reads the number; the validation step is what stopped a wrong one from reaching a policyholder.
A field that fails validation should route to a person, not get silently accepted or silently dropped. The exception queue is what makes an IDP pipeline safe to run unattended on most documents.
- 01Extraction reads the field; validation decides whether to trust it.
- 02Format and range checks catch what a fluent-looking read gets wrong.
- 03Anything that fails a check needs a queue, not a silent pass-through.
- IngestScan or PDF arrives.
- ExtractModel maps text to named fields.
- ValidateRules and range checks run.
- ExceptionFailed checks route to a person.
- RecordConfirmed field feeds the system.
A field only reaches the record after it passes validation, not right after it is read.
Common questions
01Is IDP the same thing as OCR?
No. OCR converts an image of text into text. IDP uses extraction, which is often OCR or a vision model, as one step, then adds field mapping and validation on top. OCR output alone has no concept of which field a number belongs to or whether it is plausible.
02Does IDP need a specific document type or layout?
Older template-matching tools needed a fixed layout per document type. Vision-model-based extraction handles layout variation better. A pipeline still performs best when it knows which fields to expect.
03What happens when IDP gets a field wrong?
A field that fails a format or range check should route to a person rather than pass through. A pipeline with no exception path is trusting extraction alone. That is the failure mode validation exists to prevent.
04How is IDP different from RPA?
RPA automates a sequence of clicks and steps across systems. IDP is the step that turns an unstructured document into structured fields, which an RPA workflow can then act on. The two are often used together: IDP reads the document, RPA moves the resulting data.
