Data Extraction & OCR Workflows
Turn PDFs, scans, emails, and forms into clean, usable data automatically. Capture fields, validate results, and send them to Sheets, CRM, or Slack—without copy-paste.
📊 About Data Extraction & OCR
How does Data Extraction & OCR automation work?
A workflow pulls in files or messages (PDFs, scans, images, emails) from sources like Gmail, Google Drive, or a webhook. OCR converts the document into text, then extraction logic maps it to fields you care about, like invoice number, total, dates, or contact details. Many workflows add validation, so bad data is flagged instead of stored. Finally, results are sent to a destination such as Google Sheets, Airtable, or your CRM, with optional alerts for approvals.
Do I need technical skills to automate Data Extraction & OCR?
Not usually. Most Flowpast workflows are plug-and-play: you connect your accounts, choose the fields you want, and test with a sample document. If your documents vary a lot, you might spend a little time tweaking prompts or rules, but you don’t need to code. And frankly, even “advanced” setups are often just adding a few steps like validation and notifications.
How much time can automation save for Data Extraction & OCR?
If you’re copying details from invoices, receipts, forms, or PDFs into a spreadsheet, automation typically cuts this in half or better. One document might only take a few minutes, but the real win is volume and consistency. Processing 50 files can eat an entire morning, especially when you have to double-check totals and dates. With automation, you review exceptions instead of typing everything, so your team stays focused on decisions and follow-ups.
What do I need to get started with Data Extraction & OCR workflows?
You’ll need n8n, a document source (like Gmail, Google Drive, or a form/webhook), and a destination (Google Sheets, Airtable, or a database). For OCR and smarter extraction, connect an AI provider such as OpenAI. Use a few real samples before going live, because document layouts vary. It also helps to define “must-have” fields and simple validation rules, like matching totals or checking that emails look valid.
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