In short
IDP combines several technologies. Optical character recognition (OCR) turns a scan or photo into text. Classification decides whether the document is an invoice, a delivery note or a signed form. Extraction finds the specific fields you care about. Validation checks those fields against rules and your existing records. Modern IDP increasingly uses language and vision models for the classification and extraction steps, which copes much better with varied layouts than older template-based tools did.
How a document moves through it
| Capture | Documents arrive by email, upload, scanner or phone photo and land in one queue. |
|---|---|
| Read | OCR converts the image to text, keeping track of where each word sits on the page. |
| Classify | The system decides what type of document it is, so the right fields are looked for. |
| Extract | Named details are pulled out: names, dates, amounts, line items, reference numbers. |
| Validate | Totals are checked, suppliers matched to your records, purchase order numbers looked up. |
| Review | Anything below a confidence threshold, or failing a check, goes to a person. |
| Export | Approved data is written to the accounting package, CRM or case system. |
How it differs from plain OCR
OCR on its own gives you text. You still have to find the invoice total in it. Older extraction tools relied on templates: for each supplier, someone marked where the total, date and number appeared on the page. When a supplier changed their invoice layout, the template broke.
IDP built on modern AI models reads a document more like a person does. It can find the total on an invoice it has never seen before because it understands what a total looks like and where it usually sits. That makes it practical for businesses with many suppliers, forms or document types.
Where it helps
The common starting point is supplier invoices: reading them, matching them to purchase orders and loading them into the accounting package. Other good fits include delivery notes and timesheets, application and claim forms, signed contracts that need key dates logged, identity and compliance documents, and inbound post that needs sorting.
The best candidates are documents that arrive in volume, carry a small set of details you need every time, and currently get typed into another system by hand. Before choosing a tool, count how many of each document type arrive in a typical month and how long each takes to process by hand. Those two numbers show where to start and whether the project is worth doing.
Why a person stays in the loop
Extraction is never perfect. Poor scans, handwriting, unusual layouts and ambiguous wording all cause mistakes, and a language model can occasionally produce a plausible value that is not on the page. A good IDP set-up gives each extracted field a confidence level, checks values against rules such as line items adding up to the total, and sends doubtful cases to a person.
Track how often reviewers correct each field. That tells you where the system is weak and whether accuracy is improving over time.
Data protection
Documents often contain personal data, and sometimes sensitive data such as health or financial information. Check where the processing happens, whether the provider keeps or trains on your documents, how long copies are stored, and who can see the review queue. For higher-risk documents, a data protection impact assessment is good practice.
Keep the original documents linked to the extracted data, so anyone checking a figure later can see where it came from.
When this matters
Relevant if staff spend hours each week retyping details from documents into other systems. New Era AI scopes document processing under its AI automation and integrations services, starting with the document type that costs you the most time.
Questions
Can it read handwriting?
Often, for clear handwriting, with lower accuracy than printed text. Handwritten documents usually need a higher share of human review.
Do we need thousands of examples to train it?
Usually not with modern AI-based tools, which can extract common fields with little or no training. A sample of your real documents is still essential for testing before go-live.
Will it work with our accounting software?
If the accounting package has an API or a supported import, yes. Check that first, because it decides how the extracted data gets in.
Is IDP the same as OCR?
OCR is one part of IDP. It turns images into text. IDP adds classification, extraction, validation and review so the result is usable data.