--- title: "What is intelligent data extraction?" canonical_url: "https://www.nutrient.io/blog/intelligent-data-extraction/" md_url: "https://www.nutrient.io/blog/intelligent-data-extraction.md" last_updated: "2026-09-16T14:26:57.458Z" description: "Intelligent data extraction returns a typed value with a match type, a confidence score, and the region of the page it came from — not just a string of text." --- Intelligent data extraction reads a document and returns named, typed fields — not a page of text. Each field carries its value, plus a label describing how it was found, a confidence score, and the page region it came from. That last part is what separates it from the two older approaches. Optical character recognition (OCR) returns characters and leaves the meaning to the caller. Template extraction returns whatever sits in a predefined region or rule, and it grows brittle as documents drift from that layout. But intelligent extraction returns a field a program can act on, along with the evidence for it. ## What each approach gives back The difference shows up in the response, not the description. | What you get back | OCR | Template extraction | Intelligent extraction | | ------------------------ | ------------------------------------------------------ | -------------------------------- | --------------------------------------------- | | Output | A string of characters | The text inside a defined region | Named fields matching a schema you supply | | Types | None — everything is text | None — everything is text | Strings, numbers, integers, Booleans, arrays | | Knows what a value _is_ | No | Only by position | Yes — the field is named and typed | | Handles a changed layout | Not applicable — it never associated text with a field | No — layout assumptions break | Yes — fields are found by meaning | | Says how it found it | No | No | Yes — an exact, partial, or approximate match | | Says how sure it is | Per character, at best | No | Per field, as a score | | Points at the source | Often, per character or word — but not tied to a field | The region you defined | The region the value was actually read from | The bottom three rows change what a program can do. Without provenance, a downstream system has little to validate a value against. A value carrying a match type, a score, and a location can be routed on those signals — strong ones straight through, weak or ambiguous ones to a human. ## A single field, in full Take one field on an invoice: the invoice number. A schema declares it as a string. In this API, the response nests both parts under `output`: `output.data` holds the values, and `output.metadata` mirrors that same structure field for field, so a value and its citation line up by key. ```json { "output": { "data": { "invoice_number": "INV-2024-0042" }, "metadata": { "invoice_number": { "match": "id_match", "confidence": 0.93, "bbox": { "x": 878, "y": 268, "width": 82, "height": 25 }, "pageIndex": 0, "pageNumber": 1 } } } } ``` Four facts come back with the value. `match` reports how the API grounded it to the source. Here, it’s `id_match` — the invoice number matched a single source block exactly. Other fields might come back `id_match_multiblock` (matched across several blocks), `id_match_partial` (only some of the cited blocks resolved), `fuzzy_match` (close to the source text but not identical), or `not_found`. `confidence` is a relative score from zero to one, not a calibrated probability — a higher number means more confidence, not a percentage chance of correctness. `bbox` gives the rectangle on the page, and `pageNumber` says which page. Together, they point a reviewer at the exact spot instead of the whole document. Match type deserves as much attention as the score next to it. A confidence of 0.93 on an `id_match` and 0.93 on a `fuzzy_match` aren’t the same claim. In this response model, `confidence` rates how sure the extractor is about the text it read; `match` rates how sure it is that the text is the right field. The two can diverge. A `fuzzy_match` at high confidence means the extractor is sure it read the text correctly, but only approximately sure it’s the invoice number rather than some other value nearby. For an identifier, that’s an expensive combination to miss, because the value looks trustworthy on `confidence` alone. Read match type and confidence together, and open the bbox on anything less than an `id_match`. ## Where it’s used - **Finance and accounts payable** — Invoices, purchase orders, and remittance advice, where the layout differs by supplier but the fields don’t. - **[Insurance](https://www.nutrient.io/api/data-extraction-api/insurance/) and lending** — Claims, certificates, applications, and loss reports, where a value has to be traceable back to the submitted document. - **[Healthcare](https://www.nutrient.io/api/data-extraction-api/medical/) and regulated intake** — Claim forms and patient records, where a field nobody can verify is worse than no field at all. In each case, the field and its source have to stay together, because somebody eventually has to answer where a number came from. **Call to Action** Try Nutrient Data Extraction API [Learn More](https://www.nutrient.io/api/data-extraction-api/) ## FAQ #### What is intelligent data extraction? Intelligent data extraction reads a document and returns named, typed fields rather than raw text. Each field includes the extracted value and a match type describing how it was located. It also carries a confidence score and the coordinates of the source region on the page. #### How is intelligent data extraction different from OCR? OCR converts an image of text into characters. It doesn’t know which characters form an invoice total or a due date, and it returns no structure. Intelligent data extraction returns the fields themselves, typed and named, along with the citation for each one. Many intelligent extraction systems use OCR internally as a first step. #### Is intelligent data extraction the same as intelligent document processing? No. Intelligent document processing describes the whole pipeline — classifying a document, extracting its data, validating the result, and routing it onward. Intelligent data extraction is the extraction stage within that pipeline. See the [guide to intelligent document processing](https://www.nutrient.io/blog/what-is-intelligent-document-processing.md) for how the surrounding stages fit together. #### Why does a confidence score need a source region? A score on its own cannot be checked. A score paired with a source region can be checked, not just trusted. A reviewer opens the page, looks at the highlighted rectangle, and confirms or corrects the value in seconds — no need to reread the document. ## Related reading - [Intelligent document processing](https://www.nutrient.io/blog/what-is-intelligent-document-processing.md) — The surrounding pipeline: classification, validation, and routing - [Document AI vs. OCR](https://www.nutrient.io/blog/document-ai-vs-ocr.md) — Choosing between engines, and the hybrid architecture most teams end up with - [Beyond OCR: document structure understanding](https://www.nutrient.io/blog/when-ocr-isnt-enough-document-structure-understanding/) — Tables, equations, handwriting, and reading order in practice - [Nutrient Data Extraction API](https://www.nutrient.io/api/data-extraction-api/) — Schema-defined extraction with per-field citations - [Accuracy benchmarks](https://www.nutrient.io/api/data-extraction-api/benchmarks/) — 200 PDFs with hand-annotated ground truth, rescored on every release --- ## Related pages - [The business case for accessibility: Five ways it drives enterprise value](/blog/5-ways-accessibility-drives-enterprise-value.md) - [Accessibility Untangled Why It Matters Guide](/blog/accessibility-untangled-why-it-matters-guide.md) - [Advanced Techniques For React Native Ui Components](/blog/advanced-techniques-for-react-native-ui-components.md) - [`vector_store` holds your indexed documents (see the multimodal RAG post](/blog/agentic-rag.md) - [How to build an AI agent for contract redlining against a compliance playbook](/blog/ai-contract-redlining-compliance-playbook.md) - [Ai Document Automation Extraction To Action](/blog/ai-document-automation-extraction-to-action.md) - [Ai Document Workflows Ocr Compliance Heavy Teams](/blog/ai-document-workflows-ocr-compliance-heavy-teams.md) - [Ai Legal Assistant Document Authoring](/blog/ai-legal-assistant-document-authoring.md) - [Amazon Textract Alternatives](/blog/amazon-textract-alternatives.md) - [Start (clears any prior buffer), navigate the document, then stop into a file.](/blog/android-faster-pdf-rendering.md) - [Android Pdf Out Of Memory Handling](/blog/android-pdf-out-of-memory-handling.md) - [Angular File Viewer Pdf Image Office Files](/blog/angular-file-viewer-pdf-image-office-files.md) - [Approval Workflow Software](/blog/approval-workflow-software.md) - [Approvals Matrix](/blog/approvals-matrix.md) - [Auto Tagging And Document Accessibility In Dotnet Sdk](/blog/auto-tagging-and-document-accessibility-in-dotnet-sdk.md) - [Simple PII redaction.](/blog/automated-pii-removal.md) - [Azure Document Intelligence Alternatives](/blog/azure-document-intelligence-alternatives.md) - [Best Ai Document Workflow Platforms](/blog/best-ai-document-workflow-platforms.md) - [Best Document Ai Platforms](/blog/best-document-ai-platforms.md) - [Best Document Classification Platforms](/blog/best-document-classification-platforms.md) - [Best document parser for RAG: LlamaParse vs. Unstructured vs. Reducto vs. Nutrient](/blog/best-document-parser-llamaparse-unstructured-reducto.md) - [Best Document Parsing Apis](/blog/best-document-parsing-apis.md) - [Best Document Viewers](/blog/best-document-viewers.md) - [Best Llm Document Understanding Platforms](/blog/best-llm-document-understanding-platforms.md) - [Best Multilingual Ocr Software](/blog/best-multilingual-ocr-software.md) - [Best Pdf Parsers For Rag](/blog/best-pdf-parsers-for-rag.md) - [Best Salesforce Document Generation Apps](/blog/best-salesforce-document-generation-apps.md) - [Best Secure Document Collaboration Platforms](/blog/best-secure-document-collaboration-platforms.md) - [Bpm Guide](/blog/bpm-guide.md) - [Bpm Tools](/blog/bpm-tools.md) - [Build Vs Buy Document Extraction](/blog/build-vs-buy-document-extraction.md) - [Business Automation](/blog/business-automation.md) - [Capex Vs Opex](/blog/capex-vs-opex.md) - [The CEO’s AI playbook: Why decision architecture beats model selection](/blog/ceo-ai-playbook-decision-architecture.md) - [1. 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