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Use n8n’s Extract From File node with the Extract From PDF operation. Give it a PDF as binary data (the default binary property is data), then add a separate cleanup, parsing, or AI step if you need fields such as an invoice number, date, vendor, or total. The PDF operation extracts content; it does not create your business-specific schema automatically.
The current n8n node for PDF extraction
In current n8n versions, the built-in route is Extract From File → Extract From PDF. Older tutorials may tell you to use a Read PDF node. The Extract From File node replaced Read PDF from n8n version 1.21.0 onward, so the older name can be misleading when you search the node picker.
The node expects a file in n8n’s binary data structure. A URL, filename, or base64-looking string in an ordinary JSON field is not automatically a PDF input. First create or receive binary data, then point Extract From File at the property that contains it.
What the workflow does—and does not do
| Stage | Result |
|---|---|
| Acquire the file | A PDF arrives as binary data from an HTTP Request, Webhook, local-file source, or storage integration. |
| Extract From File | The PDF’s contents are converted into JSON that later nodes can use. |
| Clean or transform | A Code node, Set/Edit Fields node, or equivalent step removes unwanted whitespace, joins lines, or selects relevant text. |
| Map business fields | A parser, regular expression, ruleset, or AI step produces a schema such as invoice number, date, vendor, and total. |
| Validate and deliver | Checks confirm required fields and send the normalized record to your database, spreadsheet, accounting system, or API. |
Extraction and field mapping are separate responsibilities. A successful extraction can still produce text that needs cleanup, and an AI parser can still return incomplete or incorrect values. Validate the result before writing it to a system of record.
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Build a PDF-to-text workflow step by step
1. Choose a binary file source
Common starting points are an HTTP Request node downloading a PDF, a Webhook receiving an upload, a local-file source in a self-hosted installation, or a storage integration. Configure the source so its output includes a binary property, not only JSON metadata.
2. Connect Extract From File
- Add an Extract From File node after the file-producing node.
- Set the operation to Extract From PDF.
- In the input binary field setting, enter the name of the upstream binary property. Leave it as
dataonly when the incoming property is actually nameddata. - Execute the node and inspect the output panel. Confirm that extracted content is present before adding downstream logic.
If the upstream node uses a different property name—such as pdf or another custom name—copy that exact name into Extract From File. Matching the property name is more important than the filename shown in the item.
3. Handle uploads arriving through a Webhook
For a Webhook upload, enable the Webhook node’s Raw body option as required by n8n’s documented pattern. Then execute the Webhook with a real PDF and inspect the item for a binary property. If no binary property appears, Extract From File has nothing to read, regardless of the request’s content type or filename.
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4. Inspect the extracted JSON
Open the execution data and identify where n8n placed the extracted text. PDF layouts differ: line breaks, headers, columns, and page boundaries may not appear in the order a person sees them. Do not assume that a visual table will arrive as a perfectly ordered table in JSON.
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Turn extracted text into useful data
Plain text for search, indexing, or summaries
If your goal is a search index, archive, notification, or summary, keep the extracted content as text and pass it to the next service. A Code node can normalize repeated whitespace, remove page artifacts, or prepend metadata such as the source filename and document ID.
Deterministic fields with rules
For stable templates, use explicit rules. For example, locate an invoice label, capture the value on the same line, normalize the date format, and remove currency symbols before numeric conversion. Add checks for missing labels and ambiguous matches instead of silently writing empty values.
AI-assisted structured extraction
For variable invoices or forms, send the extracted text to an AI step and request a strict JSON shape. State the required fields, permitted data types, and what to return when a value is absent. Parse the response, reject invalid JSON, and validate required fields before continuing. The extraction node supplies source text; it does not guarantee that an AI model will identify every field correctly.
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A downstream record might contain invoiceNumber, invoiceDate, vendorName, subtotal, tax, total, and currency. Keep the raw extracted text and source identifier alongside normalized fields so an operator can review a questionable result.
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Scanned PDFs and OCR
A digitally generated PDF usually contains a text layer. A scanned PDF may contain only page images, so ordinary extraction can return little or no usable text. In the public n8n invoice workflow example, the Extract From File options include enabling OCR for scanned PDFs. Treat that as a configuration pointer for the version you run: option names and behavior can vary by n8n release and deployment.
- Test one known scanned document before processing a large batch.
- Check whether OCR output preserves decimal points, minus signs, and dates.
- Expect tables, handwriting, stamps, and low-resolution scans to need additional review.
- Route documents with empty or suspicious text to a manual or alternate-processing path.
Choosing a route for your PDF workflow
| Question | Why it matters | Practical choice |
|---|---|---|
| Where does the file originate? | The source must emit binary data that n8n can pass to the extraction node. | Use HTTP Request, Webhook, local file, or storage integration, then verify the binary property. |
| Is there a text layer? | Image-only pages need OCR. | Enable OCR when available and test the deployed n8n version. |
| Do you need text or fields? | Raw extraction does not define a business schema. | Add cleanup for text; add rules or AI plus validation for fields. |
| Where is binary data stored? | Self-hosted storage choices affect scaling and security. | Review your binary-data configuration and access controls before handling sensitive files. |
Troubleshooting
“No binary data” or an empty result
Cause: The previous node emitted JSON only, or the binary property name does not match. Fix: Open the source execution, expand the binary section, and copy the actual property name into Extract From File. Re-run the source and extraction nodes together.
The Webhook receives a file, but extraction cannot read it
Cause: The webhook body was not configured for the expected raw binary pattern. Fix: Enable Raw body, send a fresh test upload, and confirm that a binary property appears in the webhook output.
The node exists in a tutorial but not in the editor
Cause: The tutorial uses the retired Read PDF name. Fix: Search for Extract From File and select Extract From PDF.
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Text is incomplete or badly ordered
Cause: PDFs store positioned text rather than a guaranteed reading order, and multi-column layouts can be extracted unexpectedly. Fix: inspect several pages, add cleanup logic, and preserve the original file for review. If the document is scanned, test OCR.
Fields are missing after AI parsing
Cause: The source text may be incomplete, the prompt may allow omissions, or the model may return an invalid shape. Fix: require a fixed JSON schema, parse and validate it, flag missing required fields, and retain the raw text for a retry or human check.
Self-hosted jobs become slow or storage grows
Cause: Binary files consume storage and processing resources, especially in batches. Fix: review n8n’s binary-data storage configuration, retention policy, concurrency, and security controls. Scale only after measuring the workload in your own deployment.
Operational practices for reliable extraction
- Record the source URL or document ID, filename, page count when available, and workflow execution ID.
- Separate download, extraction, parsing, validation, and delivery into visible stages.
- Use an error branch for download failures, empty extraction, OCR failures, invalid JSON, and missing required fields.
- Keep the original binary document where your retention and privacy policy permit it.
- Limit access to binary storage because PDFs can contain personal, financial, or confidential information.
- Process representative text PDFs and scanned PDFs before enabling unattended batch runs.
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