Upload a PDF or image invoice and InvoicesOCR returns clean, structured JSON with the vendor, invoice number, dates, tax, totals, and a line-items array. Use the web app for one-off conversions or call the API to turn invoice files into JSON inside your own product, with no per-vendor template to build.
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A PDF invoice is a visual layout, not data. To use it in an application you need predictable JSON with the same keys every time, regardless of which vendor sent the file or how their template is laid out. Getting there with raw PDF text extraction or regex breaks the moment a layout changes.
Positions, labels, and table styles vary by supplier, so coordinate-based or template parsing that works for one vendor fails on the next and needs constant maintenance.
Pulling the text layer out of a PDF returns a jumble of words with no idea which value is the total or which rows are line items, so you still have to rebuild the record by hand.
Hand-written regex and string matching break on a new date format, a wrapped description, or a multi-page invoice, and each edge case adds more rules to babysit.
An image-only PDF or a phone photo carries no characters to parse, so without OCR there is nothing for your code to read into JSON.
InvoicesOCR pairs OCR with AI that understands invoice layouts. It reads the file, identifies the vendor, totals, and line-item table, and returns a consistent JSON object with the same keys for every invoice. No template to draw, no coordinates to map, and the same structured result whether the input is a clean digital PDF or a crooked scan.
The same field names appear on every invoice, so your code maps the response once and does not break when a vendor changes their layout.
Each line becomes an object with description, quantity, unit price, and amount inside a line_items array, ready to iterate over.
Convert ad hoc in the browser, or POST a file to the API and receive JSON back so you can automate it inside your own product.
Digital PDFs, scanned paper, and image-only files from any supplier, with no field mapping to define first.
Subtotal, tax, and total are checked so a misread digit is flagged before the JSON reaches your database.
Uploads are encrypted in transit and at rest, and files are deleted after processing.
From a PDF or image invoice to structured JSON in about a minute.
Drag a PDF or image into the web app, or POST the file to the API endpoint. Digital, scanned, and multi-page files all work.
Tip: A clear scan reads most accurately.
InvoicesOCR pulls the vendor, invoice number, dates, tax, totals, and line items into a consistent object and validates the math.
Download the JSON from the app or read it from the API response, then store it, post it to your ledger, or pass it on.
Tip: Need a spreadsheet instead? Export Excel or CSV from the same data.
US developers, SaaS teams, and operations groups that need invoice data as structured JSON inside an application or pipeline.
Pipe invoice JSON straight into your product or workflow with one API call.
Load structured invoice records into a database, data warehouse, or BI tool.
Feed clean JSON into Zapier, Make, or a custom RPA flow without manual steps.
Embed invoice capture in your own AP or accounting product with a stable schema.
Last updated June 2026
The point of converting an invoice to JSON is a predictable shape your code can rely on. InvoicesOCR returns header-level fields for the invoice as a whole and a line_items array with one object per row, so you read totals from the top level and loop over the lines. The keys stay the same whether the invoice came from one supplier or a hundred, which is what makes the response safe to map once and reuse. Below is the kind of structure you get back.
| JSON field | What it holds | Example value |
|---|---|---|
| vendor_name | Supplier or biller name | "Acme Supply Co." |
| invoice_number | Invoice or bill number | "INV-10427" |
| invoice_date | Issue date, normalized | "2026-05-14" |
| due_date | Payment due date | "2026-06-13" |
| currency | Currency code | "USD" |
| subtotal | Amount before tax | 1240.00 |
| tax | Tax amount | 99.20 |
| total | Amount due | 1339.20 |
| line_items[] | Array of rows, each with description, quantity, unit_price, amount | {"description":"Widget A","quantity":10,"unit_price":24.00,"amount":240.00} |
There is one design choice to make: do you want one JSON object per invoice with the lines nested inside, or one record per line item flattened out. Per-invoice keeps header totals and the line array together and is the natural shape for storing the document or posting a bill. Per-line-item repeats the invoice number and vendor on every row and suits loading into a table or a spend analysis. The extraction is the same either way, so pick the shape that matches where the data is going.
Older approaches read invoices by position or pattern: a template that expects the total in a fixed spot, or regex that matches a known string. Both break the first time a vendor reformats the page or sends a layout you have not seen. Because InvoicesOCR reads the invoice the way a person does, by understanding what each value means, it returns the same JSON for a new supplier without any new rules. For the technology behind that, see AI invoice data extraction, and to run it at scale see the invoice OCR API.
All three come from the same extraction, so choose by what happens next. Use JSON when the data flows into code, a database, or another system through an API. Use the invoice PDF to CSV converter when an ERP or accounting tool imports a CSV, and the invoice PDF to Excel converter when a person needs to review the numbers in a spreadsheet first. The invoice line-item extraction page explains how the per-line detail that fills the line_items array is captured, and bulk invoice upload covers converting a whole batch at once.
Upload the PDF to InvoicesOCR or POST it to the API. It reads the vendor, dates, tax, totals, and line items and returns a structured JSON object with the same keys every time. You can download the JSON from the web app or read it directly from the API response inside your own code.
Yes. The invoice OCR API accepts a PDF or image file and returns structured JSON with header fields and a line_items array. It uses a consistent schema across vendors, so you integrate it once and process invoices automatically without building a parser for each supplier layout.
You get top-level fields for the invoice as a whole, such as vendor_name, invoice_number, invoice_date, due_date, subtotal, tax, and total, plus a line_items array where each object holds description, quantity, unit_price, and amount. Reading totals from the top and looping the array covers the full invoice.
Yes. A scanned or photographed invoice is an image with no selectable text, so InvoicesOCR runs OCR on it first, then maps the recognized values to fields. The result is the same structured JSON you get from a digital PDF, so scanned and digital invoices return an identical shape.
Use one object per invoice, with the lines nested in a line_items array, when you are storing the document or posting a bill. Use one record per line item, with the invoice number repeated on each row, when you are loading the data into a table or analyzing spend. The extraction supports both shapes.
Regex and template parsing depend on a fixed layout and break when a vendor reformats the page or sends a new design. AI reads the invoice by understanding what each value means, so it returns the same JSON for a supplier it has never seen, with no new rules to write or maintain.
Yes. Drop a batch into the web app or send files to the API in a loop, and each returns its own JSON object. Batch conversion is the fastest way to turn a backlog of invoices into structured records you can load into a database or accounting system in one pass.
Automate invoice JSON inside your product.
The technology that reads any layout.
Same data as an import-ready CSV.
Same data as a formatted Excel workbook.
How the line_items array is built.
Convert hundreds of invoices in one batch.
