Ever stared at a folder with thousands of PDF invoices, resumes, or reports and thought, "There has to be a better way"? Spoiler: there is.

At WeSpark Automations, we recently built a PDF data extraction pipeline that processed over 30,000 documents—scanned images, digital PDFs, multi-page forms—and pushed clean, structured data straight into Google Sheets. Zero manual copy-paste. Zero coffee-fueled all-nighters.​

Here's how we did it using n8n, OCR, and a bit of AI magic.

The Problem: Manual PDF Hell
Most businesses deal with PDFs daily:

Invoices with vendor names, amounts, dates buried in tables​

Resumes with inconsistent formatting across Word, PDF, and scanned images​

Financial reports locked in multi-page, image-heavy files

Extracting data manually is slow, error-prone, and soul-crushing. Our client was spending 40+ hours per week just copying data from PDFs into spreadsheets.​

The Solution: n8n + AI Extraction Workflow
We built an automated workflow using n8n (open-source automation tool) that:

Monitors email/Google Drive for incoming PDFs​

Detects PDF type: Text-based vs. scanned (OCR needed)​

Extracts structured data using AI models (names, dates, amounts, tables)​

Validates & cleans the data (catches low-confidence extractions)​

Pushes to Google Sheets or any database/CRM​

Tech Stack Breakdown

- n8n (workflow orchestration)
- PaddleOCR / Tesseract (for scanned PDFs)
- OpenAI GPT / Claude (for intelligent field extraction)
- Google Sheets API (data destination)
- Webhooks (for real-time triggers)

Step-by-Step: Building the Workflow
Step 1: Trigger Node
Set up an Email Trigger or Google Drive Watch node in n8n to detect new PDFs.​

// Example: Watch a specific Gmail label
Node: Gmail Trigger
Label: "Invoices/Process"
Attachments Only: Yes
File Types: .pdf
Step 2: PDF Type Detection
Use a Code Node to check if PDF is text-based or scanned:

# Pseudo-code
if pdf.has_selectable_text():
route = "text_extraction"
else:
route = "ocr_extraction"
Step 3: OCR for Scanned PDFs
For image-based PDFs, integrate PaddleOCR or Tesseract:​

// n8n HTTP Request to OCR API
POST /ocr/process
Body: { "pdf_binary": $binary.data }
Step 4: AI-Powered Field Extraction
Use OpenAI/Claude to extract specific fields (invoice number, date, total, line items):​

`// GPT-4 Prompt
"Extract the following from this invoice text:

• Invoice Number
• Invoice Date (format: YYYY-MM-DD)
• Vendor Name
• Total Amount
• Line Items (JSON array)

Return JSON only."`
Step 5: Data Validation
Add a Function Node to validate extracted data:​

// Flag low-confidence extractions
if (confidence < 0.85) {
flagForReview = true;
}
Step 6: Push to Google Sheets
Use the Google Sheets Node to append rows:​

Node: Google Sheets
Action: Append Row
![ ](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/y77yb50d2f2tl14rdbsg.png)
Sheet: "Invoice Data"
Columns: [Invoice_Number, Date, Vendor, Amount]
Real-World Results
After deploying this workflow:

30,000+ PDFs processed in 2 weeks​

Accuracy: 94% (with human review for flagged items)

Time saved: 38 hours/week → redirected to analysis instead of data entry

Cost: $0.03 per PDF (mostly API costs)

Challenges We Solved

1. Mixed PDF Types
   Some documents were half-text, half-scanned. Solution: Split into sections and route appropriately.​

2. Tables Spanning Multiple Pages
   Used ImageTableDetector models to reconstruct tables across pages.​

3. Inconsistent Formats
   Fed variations into AI with few-shot examples to improve accuracy.​

Try It Yourself
Want to build this? Here's the starter workflow:

Clone our n8n template (DM for access)

Set up OCR API (PaddleOCR or Google Vision)

Configure your AI extraction prompts

Connect to Google Sheets/database

Test with 10-20 sample PDFs

Deploy and monitor

Tools & Resources
n8n Community Edition (self-hosted, free)​

PaddleOCR (open-source OCR)​

OpenAI API (GPT-4 for extraction)

Google Sheets API (free tier: 500 requests/100 seconds)​

What's Next?
We're expanding this to:

Multi-language PDFs (Arabic, Chinese invoices)

Handwritten form recognition

Real-time dashboard for extraction monitoring

If you're drowning in PDFs and want to automate extraction, this workflow is your lifeboat.​

Questions? Drop them in the comments. I'll share the n8n workflow JSON if there's interest.

About the Author:
Abdul Mohiz is an AI Automation Specialist at WeSpark Automations, building intelligent workflows with n8n, Make.com, and AI. Check out more automation guides at wespark.tech.