# Intelligent Document Processing

> A pipeline that turns varied documents into validated, structured data by combining recognition, language understanding, and workflow rules.

- HTML version: https://robbiepalmer.me/ideas/intelligent-document-processing
- Source: https://aws.amazon.com/what-is/intelligent-document-processing/

Intelligent Document Processing turns documents into data that another system can use. A typical
pipeline classifies a document, reads its text and layout, identifies the fields or relationships
that matter, validates the result, and sends uncertain cases for review.

[OCR](/ideas/ocr) is often one stage, but reading characters is not enough. The system must also
interpret labels, tables, spatial structure, and domain-specific language. [NLP](/ideas/nlp), vision
models, schemas, and business rules can each contribute to that interpretation.

The hard part is defining a useful and testable output. A bill of lading, receipt, and handwritten
recipe need different entities, confidence thresholds, and consequences for error. Domain modelling
sets those expectations. [Human in the loop](/ideas/human-in-the-loop) review handles cases where
the evidence or model confidence does not justify automatic action.

## Questions it prompts

* Which downstream decision will use the extracted data?
* What must the system preserve besides text, such as layout, tables, handwriting, or provenance?
* Which errors can be corrected later, and which require review before the workflow continues?
* Does evaluation measure complete fields and documents, or only character accuracy?

## Related ideas

- [Domain-Driven Design](https://robbiepalmer.me/ideas/domain-driven-design.md): Model software around the language, rules, and boundaries of the problem domain.
- [Human in the Loop](https://robbiepalmer.me/ideas/human-in-the-loop.md): An interaction model in which a person contributes to a decision before the system can continue.
- [Named-Entity Recognition](https://robbiepalmer.me/ideas/ner.md): NER finds spans of text that name entities and assigns them types such as people, organisations, products, or identifiers.
- [NLP](https://robbiepalmer.me/ideas/nlp.md): Natural language processing uses computational methods to analyse, transform, and generate human language.
- [OCR](https://robbiepalmer.me/ideas/ocr.md): Optical character recognition converts text in images and scanned documents into machine-readable characters.
- [Ontology Engineering](https://robbiepalmer.me/ideas/ontology-engineering.md): The practice of defining the concepts, relationships, constraints, and shared vocabulary needed to represent a domain.

## Where it appears

- Project: [Commercial Knowledge Graph](https://robbiepalmer.me/projects/commercial-knowledge-graph.md)
- Project: [Intelligent Document Processing](https://robbiepalmer.me/projects/intelligent-document-processing.md)
- Project: [Recipe Site](https://robbiepalmer.me/projects/recipe-site.md)
- ADR: [ADR 030: Cooklang](https://robbiepalmer.me/projects/recipe-site/adrs/030-cooklang.md)
- ADR: [ADR 031: OpenRouter](https://robbiepalmer.me/projects/recipe-site/adrs/031-openrouter.md)

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