OCR to JSON: Convert Scanned Documents and Images to Structured Data
Upload any scanned PDF or image and receive structured JSON with fields, tables, and entities automatically extracted. No regex, no custom parsing logic.
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Built for
- Invoice ingestion pipelines
- Expense automation tools
- Backend systems needing structured data
Why OCR alone doesn't give you structured data
OCR systems return text, not structured data.
To make it usable, developers build fragile parsing logic:
- Invoice formats change across vendors
- Receipts vary per country / store
- Tables break traditional parsing logic
These pipelines work at first, but quickly become fragile as document formats change.
Every new document format increases complexity, edge cases, and maintenance.
The real limitation of traditional approaches
Document parsing is not just about extracting text, it is about understanding structure.
Tables, line items, entities, and relationships cannot be reliably reconstructed with regex or static rules alone.
This is why most pipelines break when documents evolve or vary slightly.
How to convert unstructured documents to JSON
Parselyze adds a structured extraction layer on top of OCR. Instead of raw text, you receive a clean JSON object with named fields and typed values.
Fields, tables, and entities are mapped directly into your predefined JSON schema, from any scanned PDF, invoice image, or photo.
One API call. Structured JSON out. No regex. No layout-specific logic. No maintenance.
Replace post-processing
Stop turning raw OCR blocks into brittle regex pipelines just to get fields your app can use.
Capture fields and tables
Extract scalar fields and repeating rows like line items in the same JSON response.
Ship faster
Define your schema once, then reuse it across OCR-heavy workflows, uploads, and background jobs.
OCR to JSON example: invoice extraction
Upload a scanned PDF or image. Parselyze handles the OCR internally and returns structured JSON automatically.
{ "document_type": "invoice", "invoice_number": "FCT-000342", "invoice_date": "2024-05-28", "vendor_name": "ACME Corporation", "currency": "USD", "total_amount": 1500.00, "line_items": [ { "description": "Consulting services", "qty": 8, "unit_price": 125.00, "total": 1000.00 }, { "description": "Design mockups", "qty": 1, "unit_price": 500.00, "total": 500.00 } ] }
Need this in your app?
Define your template once, then extract structured JSON automatically via SDK, REST API, or webhook workflows.
How the OCR to JSON conversion works
A simple three-step flow: upload your document, Parselyze runs OCR and extracts structured data, receive clean JSON. No parser maintenance required.
Upload a document (PDF or image)
Send a PDF or image through the dashboard, REST API, or Node.js SDK.
Define your JSON schema
Map invoice numbers, totals, dates, entities, line items, or any custom field names to your own JSON schema.
Receive structured JSON via API or webhook
Use the parsed result directly in your database, ERP, product workflow, support tooling, or automation pipeline.
OCR to JSON for any input format
Convert scanned PDFs, mobile photos, screenshots, and image documents to structured JSON, all via the same API.
Scanned PDFs
Process scanned PDF files, including multi-page archives, supplier invoices, and uploaded paperwork.
Images and photos
Convert JPG, PNG, WEBP, TIFF, and smartphone photos into structured JSON with named fields.
Mixed document batches
Handle receipts, contracts, IDs, and custom forms in the same ingestion pipeline using templates.
What you can build with the OCR to JSON API
Use structured JSON output to automate real downstream operations instead of stopping at raw text extraction.
Invoice processing automation
Convert scanned invoices into structured JSON to automatically import totals, dates, and line items into accounting systems.
Receipt data extraction
Extract merchant names, amounts, and dates from receipts to automate expense tracking and reimbursements.
Contract data ingestion
Parse contracts and agreements to extract key information like parties, dates, and clauses for internal systems.
Document ingestion pipelines
Convert large volumes of PDFs and scanned documents into structured JSON to feed data warehouses or automation workflows.
Common document types
Define the schema once, then reuse it across these categories.
Integrate OCR to JSON extraction into any app
Use the SDK or call the REST API directly. Submit scanned files and receive structured JSON ready for sync or async workflows.
npm install parselyzeReady to integrate?
SDK examples, REST API reference, webhook handling, and template-driven extraction make it easier to launch a reliable structured data extraction workflow.
REST API and Node.js SDK
Ship quickly with API docs, SDK examples, cURL samples, and production-friendly authentication.
Built for automation
Send files and receive JSON in the same request, or use webhooks for async workflows.
Stable schemas over raw text
Replace brittle regex and post-processing with predictable fields that match your template.
Frequently asked questions
Everything you need to know about Document parsing conversion.
What is document data extraction?
Document data extraction is the process of turning unstructured files like PDFs, invoices, receipts, or forms into structured, machine-readable JSON with defined fields such as dates, totals, entities, and line items.
How is document parsing different from standard OCR?
Standard OCR returns raw text without structure. Document parsing adds a structured extraction layer that maps content into predefined fields and tables, producing clean JSON ready to use in applications, databases, or workflows.
What document types does Parselyze support?
Parselyze supports invoices, receipts, contracts, medical forms, ID documents, and any custom document type. You define the schema once, and the same extraction logic works across different layouts and formats.
What file formats are accepted?
Parselyze accepts PDF files (native and scanned), PNG, JPG, JPEG, WEBP, TIFF, and BMP images. It also supports multi-page documents and smartphone photos with varying quality.
How do I get started with the API?
Sign up for a free account, create an extraction template in the dashboard, then send documents via the REST API or Node.js SDK. You can start receiving structured JSON in minutes, with 50 pages included per month.
Can Parselyze extract structured data without custom regex?
Yes. Parselyze uses AI-based document parsing to extract structured fields and tables without requiring regex, manual parsing rules, or layout-specific code.
Start turning documents into structured JSON today
50 pages/month free · No credit card required