Optical Character Recognition (OCR) is essential for automating data extraction from PDFs and image documents into business software. In Zapier, alternatives typically fall into two categories: data extraction automation tools that include OCR as a feature, and tools focused solely on OCR functionality. This article explores these options and provides a practical example of automating data extraction from documents.
Software Engineer
Optical character recognition (OCR) in document processing generally falls into two categories, a distinction that also applies to the alternatives available in Zapier:
These no-code tools provide everything needed to automate data extraction workflows. They offer customizable, re-trainable AI models for document data extraction and crucial infrastructure for data extraction automation. With intuitive interfaces for data validation, error handling, formatters for business logic, and pre-built integrations with automation tools, APIs, and webhooks, they streamline the entire extraction process.
They typically cater to businesses with mid-to-high document processing volumes that prefer a self-service approach and rapid deployment.
If you're curious about the current state document OCR business tools, read our Guide to Document Data Extraction using AI in 2025.
Large language models (LLMs) like ChatGPT excel at extracting data from unstructured text by understanding context and meaning with high accuracy. They are especially useful for processing complex documents like contracts and reports.
However, Zapier doesn’t offer a direct integration for using ChatGPT in data extraction, requiring a workaround. One option is leveraging Google Drive’s OCR—uploading a PDF to a designated folder, where it converts into a Google Docs file with extracted text. Zapier can then retrieve this text, send it to ChatGPT for key data extraction, and structure it for use in Google Sheets or other apps.
This method combines Google’s OCR with ChatGPT’s text parsing abilities but involves a long, complex Zap workflow. A simpler alternative is Airparser, a ChatGPT-based data extraction tool designed for automation.
PDF.co offers various PDF automation tools for Zapier, including OCR for extracting invoice data. Except for its AI-powered invoice OCR model, PDF.co relies on a traditional template-based approach, requiring a separate template for each document type. This makes it challenging to process diverse documents efficiently.
It is best suited for businesses handling smaller document volumes where manual verification is manageable. If your PDFs vary significantly, maintaining accurate extraction can be labor-intensive. When using PDF.co purely for OCR to extract all text, additional parsing rules are needed to isolate key data points.
Let’s see an actual Zapier automaton example of how to extract receipt data from emails to Google Sheets. We'll be using Cradl AI as our data extraction tool, but similar tools generally follow the same steps.
Before starting, make sure you’ve created a free Cradl AI account.
Once you're logged in, create your first AI model in just a few clicks. You can either clone a template for common documents like receipts and invoices, or build a custom model from scratch.
In this tutorial, I’ll clone the receipt model. If you're working with invoices, check out our Zapier guide to extracting invoice data from emails to Sheets.
To process your first document, simply upload it, and your AI model will automatically extract the data.
Cradl AI's models are highly accurate but will flag uncertain predictions for manual review instead of automatically validating the document. Any corrections you make contribute to retraining and improving the model over time. Once everything looks accurate, click Validate.
In this example, the receipt didn’t include a VAT amount, so the AI model left it blank, with 88% confidence that no VAT was present.
Connect your Cradl AI model to Zapier to automate data extraction and use the data in thousands of apps. If you’re new to Zapier, sign up and create a Zap.
Search for Cradl AI among the Zapier's integrations, and choose Cradl AI's Document Parsing Completed trigger. This will activate a Zap after completing the document validation step we performed in the step above.
We’ll use Google Sheets to store our extracted data, but Excel works fine too. Just make sure your spreadsheet is hosted in a cloud service that integrates with Zapier, like Google Drive or Microsoft OneDrive.
Create a new sheet and add headers, typically matching the fields you’re extracting. In Google Sheets, simply type the headers into the topmost cells.
Because we'll be adding one row to our spreadsheet for each document, we'll use Google Sheets's Create Spreadsheet Row action.
If you need to add multiple rows at once (e.g., for purchased items), check out our guide on extracting data from PDF tables and exporting it to Excel via Zapier.
When mapping Cradl AI's extracted fields to your spreadsheet headers, you’ll see many options beyond the ones in your headers.
99% of the time, you're looking for the fields prefixed with Validated Predictions and suffixed with Value, like Validated Predictions Purchase Date Value or Validated Predictions Total Amount Value. The other fields are either metadata or original values that haven’t been validated.
Upload your documents to Cradl AI and wait for the model to process them. If the AI is uncertain about any predictions, it will flag them for manual review. Make any adjustments, click Validate, and the entire data extraction workflow—from Cradl AI to Zapier to Google Sheets—will run automatically.
If you would like a version of this tutorial that does into more detail, this video has got you covered.
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Internal document processing on autopilot.
