Document Entity Extractor
Extract names, dates, locations, and organizations from documents. Free.
Key Capabilities
Named-entity recognition over your document
Beyond reading text, the extractor identifies the meaningful entities inside it - people, organisations, dates, locations, amounts, and reference numbers - and labels each one so you get structured data instead of an undifferentiated wall of text.
Works from images, PDFs, or pasted text
Feed it a scanned contract, an invoice photo, or pasted text. It runs OCR first when needed, then applies entity extraction to whatever it recovers, so the same workflow handles paper and digital sources.
Structured, copy-ready output
Extracted entities are grouped by type so you can drop names into a CRM, dates into a calendar, or amounts into a spreadsheet without re-reading the whole document.
How to Use
Add your document
Upload an image or PDF, or paste text directly. The tool will OCR images and scanned files automatically.
Run entity extraction
The extractor reads the content and tags each entity by type - people, organisations, dates, locations, and amounts.
Export the structured entities
Review the grouped results and copy the entities you need into your CRM, spreadsheet, or notes.
Common Use Cases
- Contract and agreement reviewLegal and operations teams pull the parties, effective dates, renewal terms, and monetary amounts out of a contract to populate a summary or tracking sheet.
- Invoice and receipt data captureFinance teams extract vendor names, invoice numbers, dates, and totals from documents to speed up bookkeeping and reconciliation.
- Research and knowledge mappingResearchers identify every person, organisation, and place mentioned across a document set to build a reference index or relationship map.
Frequently Asked Questions
What types of entities can it extract?
The tool identifies and extracts person names, organization names, locations/addresses, dates and times, monetary amounts, email addresses, phone numbers, and other structured data elements from unstructured document text.
Who uses this tool professionally?
Journalists extract names, dates, and organizations from leaked document dumps. Legal researchers extract entity mentions from case law documents for relationship mapping. Intelligence analysts extract key entities from open-source text intelligence for threat assessment.
How accurate is entity extraction?
Clearly written proper nouns (names, organization names) achieve high extraction rates. The tool leverages pattern recognition for dates, emails, and phone numbers. Ambiguous entities (e.g., names that are also common words) may need manual verification.
Is my document kept private?
Yes. All processing happens locally in your browser. Documents containing sensitive named entities are never uploaded to any server.
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