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Intelligent Document Processing (IDP)

What is Intelligent Document Processing (IDP)?

Intelligent document processing (IDP) automatically reads documents such as invoices, contracts or forms, identifies their type and turns the relevant details into structured data. It combines text recognition for scans, natural language processing and validation rules. Organisations save manual data entry and speed up processes such as handling incoming invoices or post. Uncertain fields should be checked by people.

How KI.ordo helps

KI.ordo's document extraction template pulls fields such as date, sender, amounts and deadlines from document text; text-based PDFs and Word files can be imported as knowledge, but scanned documents without a text layer cannot.

Learn more →: Intelligent Document Processing (IDP)

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Topics

  • Documents
  • Data extraction
  • Automation

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Related terms

Optical character recognition (OCR)

Optical character recognition (OCR) converts text contained in images, such as scans, photos or faxes, into machine-readable text. Only then can documents be searched, copied or processed automatically. Modern OCR uses neural networks and can also recognise tables, layouts and, in some cases, handwriting. Typical uses include invoice processing, archive digitisation and reading forms; quality depends heavily on image quality.

  • documents
  • text recognition
  • +1

Named entity recognition (NER)

Named entity recognition (NER) is a natural language processing technique that finds names of people, companies and places, as well as dates or amounts, in text and assigns each to a category. It turns free text into structured data. Example: the sender’s company, contact person, delivery date and invoice amount are extracted automatically from an email and transferred to a CRM.

  • NLP
  • Information extraction
  • +1
Template

Unstructured Data

Unstructured data follow no fixed table schema: emails, contracts, manuals, web pages, presentations, images or audio recordings. They make up a large share of organisational knowledge but were long difficult to analyse automatically. Language models can read and search such content and turn it into structured information, for example extracting deadlines and amounts from a contract into a table.

  • Data
  • Documents
  • +1

Extractive AI

Extractive AI refers to methods that pull existing information out of texts or documents rather than writing new content. The output comes verbatim or in a fixed structure from the source – such as invoice numbers from PDFs, key sentences for a summary or the relevant passage in answer to a question. Because nothing is invented, the risk of false statements is lower than with purely generative approaches; the two are often combined.

  • Information extraction
  • Documents
  • +1
Template