Parse any PDF into structured data with one API call. You define the schema, the API reads the document and returns typed JSON - vendor, totals, line items, whatever you declare. Scanned PDFs included via OCR. No regex, no template tuning.
The schema is your contract. The API guarantees the response matches it - every type, every nested object, every array. Coercions are explicit and reported.
PDF-to-text gives you a string and a regex problem. PDF-to-JSON gives you data.total as a number you can insert directly into a database row.
"1,234.50" comes back as the number 1234.5. "April 26, 2026" comes back as the ISO string "2026-04-26". Coercions are explicit and reported in the response.
Every response includes a validation block: passed / partial / failed, plus warnings for missing required fields. You know the data quality before you persist it.
Three steps from a raw PDF to typed JSON. Works with any PDF - digital, scanned, single-page, or multi-page.
Send any PDF to the API endpoint - invoices, contracts, statements, reports, or scanned documents. Multi-page PDFs are processed automatically.
The API reads all pages, applies OCR to scanned content, understands the layout, and maps the values to the fields in your schema.
Receive clean, typed JSON matching your schema. Ready to store in a database, feed into a pipeline, or pass to another API.
Built for developers who need reliable PDF to JSON conversion without the complexity of traditional OCR and PDF parsing libraries.
Automatically detects and applies OCR to scanned PDFs and image-based pages. Works with both digital and paper-origin PDFs without any configuration.
One endpoint, one API call. Send a PDF, get JSON back. No SDKs required - works with curl, Python, Node.js, or any HTTP client.
Processes all pages of a PDF document. Extracts data that spans across pages - line items, tables, and sections that continue on subsequent pages.
Define exactly which fields to extract with JSON schemas. Support for strings, numbers, dates, arrays, and nested objects. One schema works across different PDF layouts.
PDF files are processed and deleted automatically. No document data is stored after extraction. EU-hosted infrastructure with encrypted connections.
Most single-page PDFs are processed in seconds. Synchronous and asynchronous modes available depending on your document size and page count.
The same schema-driven API works across every document type. Define a schema once, extract from thousands of files.
Extract structured JSON from any document with custom schemas.
Vendor, line items, totals, tax, and dates from invoices.
Store, items, totals, and payment method from receipts.
PO number, vendor, buyer, and SKU-level line items.
Transactions, running balances, and dates for reconciliation.
Box-level data from W-2, 1099, and other tax forms.
Parties, dates, governing law, and key clauses.
Carrier, parties, ports, containers, and cargo.
Most PDFs are visual documents - text positioned on a page, no machine-readable structure. Converting PDF to JSON means reading the document, identifying which text belongs to which field (vendor name, total, line item description), and returning a typed JSON object you can insert into a database or pass to another API.
You define a schema - a list of fields with names and types. The API uses that schema to guide extraction. Same PDF + different schema = different JSON. You can also auto-generate a schema from a sample document in the dashboard, then reuse it via the API.
Yes. Schemas support nested objects (a billing_address with street/city/zip sub-fields) and arrays of objects (line_items as an array where each item is an object with description/quantity/price). The API returns the same nested shape you declared.
PDF to text returns one big string in reading order - you still have to write parsing logic to find specific values. PDF to JSON returns the values directly, typed and named. The schema is the contract; you skip the parser.
Yes. The API runs OCR automatically on scanned PDFs (image-based PDFs). The schema-driven JSON output is the same whether the source is a digitally-generated PDF or a scan.
It does turn PDFs into JSON, but it is parsing, not a flat conversion. You define a schema and get back exactly those fields as typed values - numbers as numbers, dates as ISO strings - not a raw text dump. Scanned and digital PDFs are both handled, with OCR applied automatically.
Sign up free and run your first conversion in under two minutes.
curl -X POST \
https://api-parse.conversiontools.io/v1/extract \
-H "Authorization: Bearer YOUR_API_KEY" \
-F "file=@statement.pdf" \
-F 'schema={
"account_holder": "string",
"account_number": "string",
"statement_period": {
"start": "date",
"end": "date"
},
"transactions": [{
"date": "date",
"description": "string",
"amount": "number",
"balance": "number"
}],
"ending_balance": "number"
}'{
"status": "completed",
"pages": 4,
"data": {
"account_holder": "Jane Doe",
"account_number": "****6739",
"statement_period": {
"start": "2026-03-01",
"end": "2026-03-31"
},
"transactions": [
{
"date": "2026-03-04",
"description": "Whole Foods",
"amount": -84.52,
"balance": 4215.48
}
],
"ending_balance": 3127.04
}
}