How to Extract Data from PDF Invoices (and Stop Doing It Manually)

The Invoice Data Entry Problem

If you process invoices for clients, you already know the drill: open the PDF, scan for the vendor name, find the invoice number, locate the date, tally up line items, check the total. Repeat. All day.

For a bookkeeper managing even 10 clients, this can mean hundreds of invoices a month. Each one takes 2–5 minutes of focused attention. That's not just time — it's the kind of repetitive, error-prone work that causes mistakes, mental fatigue, and scope creep into hours that should be billable elsewhere.

The good news is that invoice data extraction has gotten significantly better in the past few years. The bad news is that not all approaches are equal, and choosing the wrong one can cost more time than it saves.

This guide covers the three main approaches, what fields actually matter to extract, how to evaluate any tool's accuracy, and how to get extracted data into your accounting software.

The Three Approaches to Invoice Data Extraction

1. Manual Entry

The baseline. You read the invoice, you type the data. No software required beyond your accounting platform.

When it works: Low invoice volume (fewer than 50/month per client), complex or unusual invoice formats, invoices that require judgment calls about coding.

Real cost: At 3 minutes per invoice and a $35/hr staff rate, 100 invoices costs $175 in labor — every month. At 500 invoices, that's $875/month, plus the fatigue factor and error rate.

Error rate: Human data entry error rates run roughly 1–3% for experienced staff. On financial data, that means one bad number per 33–100 invoices. Small errors compound across tax season.

2. Template-Based OCR

Tools like Rossum (older workflow), ABBYY FlexiCapture, and some built-in accounting platform features use fixed templates: you tell the software "invoice number is always in this region of the page."

When it works: High-volume, single-vendor batches where the invoice format never changes. Think: one supplier sending the same layout month after month.

When it breaks: Any variation in vendor format — new template, different address, slightly different line item layout — and the extraction fails or silently miscaptures data. You then spend time debugging templates instead of processing invoices.

The hidden cost: Template setup takes hours per vendor. If you manage 30 clients each with 10+ vendors, you're looking at significant upfront configuration. And every time a vendor changes their invoice format (software upgrades, rebranding, new billing system), you rebuild.

3. AI-Based Extraction

Modern large language models can read a PDF invoice and extract structured data without templates. They understand context: they know that "INV-2024-0091" is likely an invoice number even if it's in an unusual position, and that a line item labeled "Professional Services — March" is a description, not a quantity.

When it works: Mixed vendor pools, scanned or photographed invoices, any situation where template configuration would be impractical. This is the approach most new invoice extraction tools use, including SkipEntry.

Limitations: AI extraction is not perfect. Accuracy depends on the model, the prompt, and the PDF quality. Scanned invoices with poor contrast or unusual fonts have lower accuracy. AI tools can also return wrong values confidently. Good tools add validation layers (math checks, format checks) to catch these.

Cost structure: AI extraction typically costs per page or per invoice, making it predictable to budget. The economics make sense once you're past roughly 30–50 invoices per month.

What Fields Actually Matter

Not all invoice fields are equally important for your accounting workflow. Here's what most bookkeepers need:

Core fields (always extract):

• Vendor name — needs to match your chart of accounts or vendor list in QBO/Xero
• Invoice number — critical for deduplication and audit trail
• Invoice date — determines the accounting period
• Due date — for AP aging
• Total amount due — the headline number
• Subtotal — pre-tax amount
• Tax amount — for GST/HST/sales tax recording

Line item fields (extract when possible):

• Description
• Quantity
• Unit price
• Line total
• GL account or expense category (this usually requires human judgment)

Secondary fields (useful but optional):

• Purchase order number
• Payment terms (Net 30, etc.)
• Currency
• Remit-to address (for multi-location vendors)

When evaluating an extraction tool, test it specifically on these fields for your actual invoice pool. Vendors you process frequently are the ones where accuracy matters most.

How to Evaluate Extraction Accuracy

Marketing claims about accuracy are meaningless without context. Here's how to actually test a tool before committing:

Step 1: Build a test set. Pull 20–50 invoices from your actual client files. Include: simple digital invoices, complex multi-line invoices, scanned or photographed invoices, and any unusual formats you know cause problems.

Step 2: Run extraction and spot-check. Don't review every field on every invoice — that's as slow as manual entry. Instead, check: (a) vendor name matches, (b) total amount is correct, (c) invoice date is correct. These are the three fields that cause the most downstream problems if wrong.

Step 3: Check math validation. A good tool will flag when line items don't sum to the subtotal, or subtotal + tax doesn't match the total. This catches the "close but wrong" extractions that are hardest to spot manually. SkipEntry, for example, runs math validation on every extraction and flags discrepancies before you export.

Step 4: Test your edge cases. What happens with a two-page invoice? A scanned invoice rotated 90 degrees? An invoice with a credit memo on the back? These are where tools diverge most.

Step 5: Measure correction time, not just accuracy. An extraction that's 90% correct but easy to fix in 30 seconds is better than one that's 95% correct but requires navigating a confusing UI to correct.

A realistic target for AI extraction on clean digital PDFs: 95%+ field-level accuracy. On scanned invoices: 85–92% depending on scan quality.

Getting Extracted Data Into QuickBooks or Xero

Extracted data is only valuable if it flows into your accounting system cleanly.

QuickBooks Online: QBO supports vendor bill import via CSV. The required columns are: VendorName, TxnDate, RefNumber, APAccount, Currency, LineAccount, LineAmount, LineDescription. Date format must be MM/DD/YYYY. The biggest gotcha: vendor names must match exactly what's in your QBO vendor list, or the import fails.

Xero: Xero's bill import is similar but uses slightly different column headers and accepts YYYY-MM-DD date formats. Xero is more forgiving on vendor name matching — it will create a new contact if the name doesn't match, which is sometimes helpful and sometimes creates duplicates.

What good tools do: The best extraction tools generate import-ready files for QBO or Xero directly, handling the column mapping and date formatting automatically. SkipEntry produces export files formatted specifically for each platform, reducing the import step to a file upload.

The vendor matching problem: This is the step that still requires human judgment most often. Your QBO vendor list has "Acme Corp" — the invoice says "Acme Corporation, Inc." These are the same company, but automated tools may not know that. Good tools let you set up vendor aliases or suggest matches.

When AI Extraction Makes Sense — and When It Doesn't

AI extraction is a strong fit when:

• You process more than 50 invoices per month
• You have multiple vendors with varying formats
• Your team spends meaningful time on data entry (not just review)
• You're already paying for accounting software that doesn't include good import tools

It's less compelling when:

• You have 5–10 invoices per month total — manual entry is fine at that volume
• All your invoices come from one or two vendors with consistent formats — a simpler template tool works
• Your invoices require significant judgment (complex job costing, unusual expense splits) — the bottleneck isn't data entry, it's coding

The extraction step is one part of the AP workflow. It doesn't replace the review, coding, and approval steps — it just removes the typing. If your process is slow because of approval delays or unclear expense policies, extraction won't help with those.

Bottom Line

Manual invoice data entry is a solved problem at scale — the tools exist to eliminate most of it. The question is which approach fits your volume and vendor mix.

For most bookkeeping practices with 50+ invoices per month per client: AI extraction, with a review step and math validation, is the right approach. It's faster than template-based tools to set up, handles mixed vendor pools, and the accuracy is good enough that review time is minimal.

Start with a real test on your actual invoices before committing. Any tool worth using will let you test with your own files before paying.