Invoice OCR is the process of using optical character recognition and AI to automatically extract structured data—vendor names, invoice numbers, line items, totals, due dates—from invoice documents. Instead of manually keying data from PDFs, scans, or emailed invoices into a spreadsheet or ERP, invoice OCR reads the document and pulls out the fields you need in seconds.
Lido is an AI-powered invoice OCR platform built for finance and operations teams that process invoices at scale. It reads any invoice format—PDF, scan, image, or email—without templates or training. Soldier Field’s accounts payable team processes over 1,000 invoices per month with Lido, saving 20 hours of manual data entry every week, and they were up and running in 15 minutes.
Invoice OCR sits between document receipt and data entry, replacing the manual step where someone reads an invoice and types values into a system. In a modern accounts payable workflow, the entire process from ingestion to export can be fully automated.
Document ingestion. Invoices arrive from multiple channels—email attachments, scanned paper documents, uploaded PDFs, or even photos taken on a phone. The OCR system accepts all of these formats without requiring you to standardize inputs first.
AI reads the document. Rather than matching text to a rigid template, AI-powered invoice OCR analyzes the full document. It identifies the layout, finds key fields like vendor name, invoice number, date, and payment terms, and locates line-item tables regardless of where they appear on the page. This works whether the invoice is a clean digital PDF or a slightly skewed scan with stamps and handwriting.
Data extraction and structuring. The system pulls every relevant data point—header-level fields and individual line items—and organizes them into structured rows and columns. You get a clean dataset, not raw text. Fields like quantities, unit prices, tax amounts, and totals are parsed as numbers, not strings, so they’re immediately usable for calculations.
Output to your system. Extracted data flows into a spreadsheet, ERP, or accounting platform. Some teams export to Excel for review before posting. Others push directly into NetSuite, QuickBooks, or SAP. The point is that the data arrives structured and ready—no re-keying required.
Manual invoice processing is slow, error-prone, and impossible to scale. If your AP team is still reading invoices and typing data into spreadsheets or ERP screens, every one of these problems gets worse as volume grows.
Not all invoice OCR works the same way. The difference between template-based and AI-powered extraction is the single most important factor in whether an OCR solution actually saves you time or just shifts the work somewhere else.
The right invoice OCR tool should reduce work from day one, not create a new implementation project. Here are the criteria that matter most when evaluating options.
Lido takes a fundamentally different approach to invoice OCR. There are no templates to build, no training period to wait through, and no IT setup required. You upload invoices, and AI extracts the data.
Standard OCR converts images of text into machine-readable characters—it turns a scan into raw text but doesn’t understand what the text means. Invoice OCR goes further by identifying specific data fields like vendor name, invoice number, line items, and totals, then structuring them into usable data. Lido’s invoice OCR uses AI to understand document context, so it extracts structured fields from any invoice layout without needing templates or manual configuration.
Yes. AI-powered invoice OCR can process scanned paper documents, photos of invoices, and documents with handwritten annotations. The accuracy depends on image quality—a clear scan will yield better results than a blurry phone photo. Lido handles scanned and photographed invoices alongside native PDFs, so you don’t need to separate documents by format before processing them.
With template-based OCR tools, yes—you need to create and maintain a separate template for every vendor invoice layout, which becomes unmanageable as your vendor list grows. With AI-powered tools like Lido, no templates are required. Lido reads any invoice format on the first upload, regardless of layout, language, or structure. This is the key difference that makes AI-powered extraction practical for teams with dozens or hundreds of vendors.
Modern AI-powered invoice OCR extracts header fields and line items with accuracy that matches or exceeds template-based systems. Accuracy varies by document quality—clean digital PDFs extract at near-perfect rates, while degraded scans may require review. Lido provides computed columns that let you build automatic validation checks, such as flagging invoices where extracted line totals don’t sum to the stated total, so exceptions are caught immediately rather than downstream.
With Lido, setup takes minutes, not weeks. There’s no template configuration, no training data to provide, and no IT implementation required. Soldier Field’s AP team was processing live invoices within 15 minutes of signing up. You can test Lido on your own invoices with a free trial of 50 pages—no credit card required—to verify accuracy before committing.
Invoice OCR typically extracts header-level fields—vendor name, invoice number, invoice date, due date, PO number, payment terms, subtotal, tax, and total—as well as line-item detail including descriptions, quantities, unit prices, and line totals. Lido extracts all of these fields automatically and structures them into a spreadsheet format where each line item becomes its own row, making the data immediately usable for matching, validation, and export.
Most invoice OCR tools offer some form of export or integration. The most common options are Excel and CSV export, direct ERP connectors, and API access for custom workflows. Lido supports export to Excel and CSV and can push structured data into accounting platforms and ERPs. For teams with specific integration requirements, the structured output from Lido can feed into any system that accepts tabular data.
%20(1).svg)
