OCR vs Data Extraction — What's the Difference?
OCR reads text from documents. Data extraction returns structured, field-level JSON. Understanding the difference determines which approach your automation actually needs.
OCR
OCR (Optical Character Recognition) converts image pixels into a text string. It is purely a reading mechanism — it does not understand what the text means or where specific fields are.
OCR output — raw text
Invoice FCT-000342 ACME Corporation Date: 2024-05-28 Consulting services 8h x 125.00 Design mockups 1 x 500.00 Total: 1500.00 USD
You still need custom rules to extract invoice_number, vendor, total, etc.
Data Extraction
Data extraction uses OCR internally, then applies AI to identify and map specific fields. The result is structured JSON — no post-processing required.
Extraction output — structured JSON
{ "invoice_number": "FCT-000342", "vendor_name": "ACME Corporation", "invoice_date": "2024-05-28", "total_amount": 1500.00, "currency": "USD" }
Ready to push to your database or ERP — zero post-processing.
Feature comparison
| Feature | OCR | Data Extraction (Parselyze) |
|---|---|---|
| Output format | Raw unstructured text | Structured JSON with named fields |
| Field mapping | None — text only | invoice_number, total_amount, line_items, etc. |
| Layout dependency | Very high — breaks on format changes | Low — AI adapts to any layout |
| Post-processing needed | Yes — regex, rules, custom parsers | No — ready-to-consume JSON |
| Usable without code | No | Yes — field definitions in plain language |
| Accuracy on scanned docs | Moderate (depends on quality) | High — AI corrects OCR errors |
| Handles tables (line items) | Poorly — rows merge or split | Yes — as structured arrays |
| Integration effort | High — significant parsing logic | Low — single API call, JSON response |
When to use each approach
Use basic OCR when…
You only need raw text — no field-level structure
Building a full-text search index from document content
Processing documents where structure does not matter
Cost is more important than accuracy or field mapping
Use data extraction when…
You need specific fields like invoice number, total, or line items
Data must flow into a database, ERP, or accounting system
You process many different document layouts
Accuracy and reliability are critical for your workflow
Frequently asked questions
What is OCR?
OCR (Optical Character Recognition) is the technology that reads text from images or scanned documents and converts it into a digital text string. The output is a flat, unstructured stream of characters — similar to copy-pasting text from a PDF.
What is data extraction?
Data extraction goes further than OCR. It identifies specific fields within a document — such as invoice number, vendor name, and total amount — and returns them as structured key-value pairs or JSON. Data extraction uses OCR internally, but adds AI-powered field identification and structuring on top.
Can OCR be used for invoice processing?
OCR alone is not sufficient for invoice processing. It will give you raw text, but you still need to parse that text to find the invoice number, totals, and line items — which requires custom rules that break when invoice layouts change. AI-powered data extraction handles this automatically.
Is Parselyze an OCR tool?
Parselyze uses OCR as a component internally, but it is a data extraction platform — not a raw OCR tool. You define the fields you want, and the API returns structured JSON with those fields populated from any document, regardless of layout.
When should I use OCR instead of data extraction?
Use basic OCR when you only need the full text content of a document without caring about field-level structure — for example, building a search index or running keyword analyses. For anything that requires specific fields or automation, data extraction is the right choice.
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