If you want invoice data without wiring up a document-AI platform, InvoicesOCR reads any PDF or scanned invoice with AI and returns the vendor, dates, totals, and every line item as a clean Excel or CSV file in seconds. There is no API to integrate, no JSON to parse, and no platform to adopt. Upload an invoice below and see the line-item data right away.
Upload your receipts and invoices
Drop files here or click to upload
Up to 50 files
Free to try, no account needed to start
Uploading...
Affinda is a broad document-AI platform. It extracts many document types, from invoices and resumes to passports and bills of lading, classifies and splits document packs, and returns structured JSON through a REST API that you connect to your own systems or to its catalog of downstream integrations. That breadth is useful if you are building extraction into a product. For a finance team that just wants invoice data in a spreadsheet, the platform-and-API model is more than the job needs, and the usage-based, billed-in-arrears pricing can be hard to predict.
Affinda is designed to be integrated. To get value you generally connect the REST API or wire up an integration, which means developer time before invoices turn into something finance can use.
The API returns structured JSON. Someone still has to map and load that into Excel or your accounting system. A bookkeeper who just wants a clean file has an extra step in the way.
A platform that handles dozens of document types carries configuration and surface area that an invoice-only team rarely uses. You pay for breadth when you only need invoices.
Affinda meters usage and bills monthly in arrears, with the per-document rate dropping at higher volume. That works for engineering teams but is harder to budget for a small AP team processing an unpredictable monthly pile.
InvoicesOCR does one thing and skips the integration. Upload a PDF, scan, or photo in the browser and the AI returns the vendor, invoice number, dates, tax, totals, and full line items as a clean Excel or CSV file. There is no API to call, no JSON to map, and no platform to set up. The focus stays on getting invoice data, line items included, into a spreadsheet you can import.
It runs in the browser. There is no REST API to connect and no developer time needed before your first invoice is processed.
You get a clean spreadsheet ready for QuickBooks, Xero, or NetSuite, not a JSON payload someone has to map and load first.
AI extraction handles PDFs, scans, and phone photos from any supplier in any format, with no template or per-vendor setup to configure.
Each line on the invoice becomes its own row, so you get description, quantity, unit price, and amount, not just the header totals.
An accountant or AP clerk can use it directly. There is no API key to manage and no integration to maintain.
Files are encrypted and deleted automatically after processing, so invoice data does not linger.
From a supplier invoice to a clean, itemized spreadsheet in about a minute, with no API to call and nothing to configure first.
Drag in PDF, JPG, PNG, or scanned invoices, one at a time or as a batch.
Tip: Multi-page PDFs are supported.
InvoicesOCR reads the vendor, invoice number, dates, tax, totals, and every line item and shows them for a quick check, with no JSON to inspect.
Export a clean spreadsheet and import it into your accounting system, or arrange the columns for a specific ERP template.
Tip: The same file works for QuickBooks, Xero, and NetSuite imports.
US accountants, bookkeepers, and finance teams who want invoice data in a spreadsheet without integrating a document-AI API or adopting a broad extraction platform.
Turn client invoices into clean, itemized Excel without writing code or mapping JSON.
Process the monthly vendor invoice pile into a spreadsheet without standing up an integration.
Get reliable invoice extraction without metered usage to forecast or a platform to administer.
Use the browser tool for ad hoc work and the API when an invoice batch needs to run automatically.
Last updated June 2026
Affinda is a document-AI platform with a wide model library. It reads many document types, classifies and splits document packs, and returns structured JSON through a REST API that you connect to your own systems or to its catalog of downstream integrations. That is a strong fit if you are building automated extraction into a product or a high-volume pipeline. InvoicesOCR is built for the narrower, more common job: a finance user uploads an invoice in the browser and gets clean, itemized data as Excel or CSV, with no API to call and no JSON to map.
The practical difference is the integration step. With Affinda the value shows up after you wire the API into your stack and handle the JSON it returns. With InvoicesOCR there is nothing to integrate: you open the page, upload an invoice, and download a spreadsheet. If your goal is invoice data in a file your accounting system can read, rather than a programmable platform, the no-code tool gets you there the same day.
The real test for invoice extraction is the line-item table. InvoicesOCR captures description, quantity, unit price, and amount for each line and writes one row per line, so you get itemized detail rather than just the invoice total. Because the AI reads invoice structure directly, a new vendor with an unfamiliar layout works the same as a familiar one, with no template or document type to configure first. That itemized output is what makes the spreadsheet useful for coding, approvals, and reconciliation in your own books.
Affinda returns JSON, which is exactly what a developer wants and exactly what a bookkeeper does not. Someone has to take that JSON and turn it into rows your accounting system accepts. InvoicesOCR removes that step by handing you Excel or CSV directly, formatted with one row per line item. If you later do want automation, InvoicesOCR also offers an invoice OCR API, so you can start manually today and call the API when a batch needs to run on a schedule, without committing to a platform up front.
This comparison should be honest. If you need to extract many document types beyond invoices, want a REST API and JSON to build a custom pipeline, need document classification and pack-splitting, or are integrating extraction into your own product across hundreds of downstream systems, Affinda is built for that and does it well. InvoicesOCR is focused on invoices and on finance users who want a spreadsheet without writing code. Run a few of your own invoices through both and compare the line-item output you would actually import before you decide.
Affinda meters usage and bills monthly in arrears, with the per-document rate falling as volume rises. That model suits an engineering team that can forecast its API calls. A small AP team processing an unpredictable monthly pile often prefers a simpler, self-serve tool it can pick up when invoices arrive, with no usage meter running in the background and no integration to keep healthy. For the specific job of turning invoices into spreadsheets, the lighter tool removes both the build and the budgeting guesswork.
Most teams comparing platforms have a specific document pile in mind. If yours is component and raw-material bills, see how we handle invoice extraction for manufacturing; if you run a finance function and want spend visibility from every bill, the invoice extraction for CFOs page walks through it; and if the core goal is simply to eliminate manual invoice data entry, that page shows the before-and-after of keying invoices by hand.
"Affinda was great for our engineers, but our AP team just needed invoices in Excel. A browser tool that reads any layout and exports every line covered the job with no integration to build."
The best alternative depends on what you are building. If you want invoice data in a spreadsheet without integrating an API, a focused tool like InvoicesOCR is the simplest fit: it reads any invoice layout in the browser and exports Excel or CSV with nothing to wire up. If you need a document-AI platform that returns JSON across many document types, Affinda may suit you better.
Affinda is a document-AI platform that uses machine learning to classify documents and extract data across many types, including invoices. Documents enter by email, upload, or API, the platform identifies the document type and splits packs, and it returns structured JSON through a REST API. You then connect that output to your own systems or to its catalog of downstream integrations.
Yes. InvoicesOCR is built specifically for invoices. Instead of a multi-document platform with an API, it reads PDFs, scans, and photos of invoices in the browser and returns the vendor, invoice number, dates, tax, totals, and a row for every line item as Excel or CSV. There is no API to call and no JSON to parse, so an accountant or AP clerk can use it directly.
Affinda competes with document-AI platforms, OCR APIs, and no-code invoice extractors. For the specific task of getting invoice data into a spreadsheet, InvoicesOCR is a direct alternative: it reads any invoice layout and returns vendor, dates, totals, and full line items as Excel or CSV, with no API to integrate and no JSON to map into your accounting system.
Affinda uses usage-based pricing, billed monthly in arrears, with the per-document rate falling as volume rises, and it offers a free trial. Exact rates depend on your volume and plan, so check Affinda directly for current pricing. InvoicesOCR is built for teams that want straightforward invoice-to-spreadsheet conversion without a usage meter to forecast.
It can, but it does not have to. The browser tool gives you Excel or CSV directly, which is what most finance users want, so there is no JSON to map. If you do need structured output for automation, InvoicesOCR also offers an invoice OCR API that returns data programmatically, so you get the spreadsheet path and the API path from one tool.
Yes, and it is a core feature. InvoicesOCR captures description, quantity, unit price, and amount for each line and writes one row per line item, alongside the vendor, invoice number, dates, tax, and totals. That itemized output is what makes the exported spreadsheet useful for coding, approvals, and reconciliation, rather than giving you only the header totals.
Yes. There is nothing to migrate and no integration to rebuild. Upload your invoices, review the extracted vendor, dates, totals, and line items, and download Excel or CSV. Because the tool reads any layout with AI, your first batch works the same way every batch after it does, and finance staff can start without a developer connecting an API.
No. There are no templates or document types to configure. The AI reads invoice structure directly, so a new supplier with an unfamiliar layout works the same as a familiar one. You do not set up or maintain a layout when a vendor changes its format, which removes the setup work that platform-style tools usually require.
