How to extract text from PDF in Make with Plumsail Documents

Extracting data from PDF files is a common task when building scenarios in Make. Whether you are working with invoices, forms, contracts, or reports, the goal is often the same — extract text from PDF and turn it into structured data you can use.

But simply getting plain text from a PDF is not enough. You often need to extract specific values such as dates, names, reference numbers, or totals and use that structured data in Google Sheets or other systems. This is where the ability to extract data from PDF and automate PDF processing becomes essential.

In the Make Community, I came across multiple questions like these:

Sorry for the trouble, but there's something I can't figure out. I'm trying to convert PDF files into text. My idea is simple: take invoices in PDF format and load them into Google Sheets or Excel tables. That's it.

I want to extract the raw text from a PDF file, I can use the Google Drive module “download file”. It gives me raw data, how can I convert it to text?

These real questions were the reason I wrote this article. It shows how to extract text from PDF using Plumsail Documents inside a Make scenario, apply regular expressions to extract data from PDF, and use that data in Google Sheets.

If you are looking for a clear and practical way to extract info from PDF and automate PDF processing in Make, this guide will walk you through the entire process.

Further in the article:

1. Extract data from PDF – Scenario overview
2. Extract text from PDF in Make: Step-by-step guide
   • Gmail module to watch for incoming emails with PDF attachments
   • Gmail module to list attachments
   • Plumsail Documents module to extract text from PDF
   • Plumsail Documents module to extract specific values using regular expressions
   • Google Sheets module to write extracted data
3. Automate more with extracted PDF text
4. Start extracting data from PDF in Make with Plumsail Documents

 

Extract data from PDF – Scenario overview

In this scenario, Make monitors a Gmail inbox for incoming messages. When a new email arrives with a PDF attachment, the file is sent to Plumsail Documents, which extracts the full text content from the PDF. I then use a regular expression to extract specific data points such as the invoice number, issue date, sender name, and total amount directly from the text.

The extracted data is added to Google Sheets in a structured format. This makes it easy to store, process, or connect the values with other systems.

This is the original invoice document from which the data will be extracted:

 

With this setup, I can fully automate PDF processing. There is no need to open files or copy values manually. Everything runs in the background inside Make.

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Let's start.

Extract text from PDF in Make: Step-by-step guide

The scenario starts with Gmail → Watch emails, which monitors my inbox for new messages. Then I use List email attachments to find the files attached to those emails. For each PDF attachment, I run Plumsail Documents → Extract text from PDF to get the full plain text content.

Once I have the text, I apply Plumsail Documents → RegExp Match to extract specific fields: invoice number, date, client name, and total. Finally, I use Google Sheets → Add a row to write this data into a table — one row per invoice.

 

Now let's go through each step in more detail.

Gmail module to watch for incoming emails with PDF attachments

I started with the Gmail → Watch emails module. It monitors my inbox and triggers the scenario when a new message arrives.

Here is how I configured it:

• Add the Watch emails module from Gmail
• Connect my Gmail account
• Select the Inbox label
• Set Content format to Full content to include all details
• Choose the start date for when to begin checking messages
• Set the trigger to Immediately to process emails as they arrive

 

The setup for this Gmail module is complete. Next, I'll configure the module that works with email attachments.

Gmail module to list attachments

After setting up email monitoring, I added the Gmail → List attachments module. It takes the message ID from the previous step and returns all attached files.

Here is how I configured it:

• Added the List attachments module from Gmail
• Mapped the Message ID field to the output of the Watch emails module
• Left all other fields with default values

 

This module gives me direct access to the files in each email. In the next step, I'll pass the PDF attachments to the text extraction module.

Plumsail Documents module to extract text from PDF

Now that I have access to the attachments, I added the Plumsail Documents → Extract text from PDF module. It extracts info from PDF and returns its content as plain text or HTML, depending on the output format you choose. If you're using Plumsail Documents for the first time, you'll automatically get a 30-day free trial with full access to all features.

Here is how I configured it:

• Added the Extract text from PDF module from Plumsail Documents
• Connected my Plumsail Documents account
• Mapped the File field to the output of the List email attachments module from Gmail
• Set Result type to Text
• Left all other fields (Start Page, End Page, Password) empty since I want to extract the full content of each PDF

 

This module will return raw text that I can then process further with regular expressions.

Plumsail Documents module to extract specific values using regular expressions

This module helps extract structured data from the plain text returned by the Extract text from PDF step.

I selected my existing Plumsail Documents connection.

In the Regular Expression Pattern field, I used this expression:

Number:\s*#\s*(?<invoice>[A-Z0-9\-]+)\s* Date:\s*(?<date>\d{4}-\d{2}-\d{2})\s* (?<client>[^\r\n]+)[\s\S]*?Total:\s*\$\s*(?<total>\d+)

It captures the invoice number, date, client name, and total amount using named groups. Specifically:

• (?<invoice>[A-Z0-9\-]+) — captures the invoice number like NVP-100012.
  • (?<invoice>...) — named group called invoice
  • [A-Z0-9\-]+ — matches one or more uppercase letters (A-Z), digits (0-9), or hyphens (-)

 

• (?<date>\d{4}-\d{2}-\d{2}) — captures the invoice date in YYYY-MM-DD format, like 2025-07-04.
  • (?<date>...) — named group called date
  • \d{4} — four digits (year)
  • - — hyphen
  • \d{2} — two digits (month), then two digits (day)

 

• (?<client>[^\r\n]+) — captures the client name, for example Tech Solutions Ltd.
  • (?<client>...) — named group called client
  • [^\r\n]+ — matches one or more characters that are not a line break (\r or \n)

 

• (?<total>\d+) — captures the total amount, such as 1500.
  • (?<total>...) — named group called total
  • \d+ — matches one or more digits

 

This makes it easier to map the extracted values directly to columns in Google Sheets or any other destination in your workflow.

If you're not sure how to write a regular expression or want to test your pattern, these resources can help:

• Plumsail RegExp Match documentation
• Regex101 - an interactive tool for testing and debugging regular expressions

In the String to search for matches field, I mapped the Extracted Text output from the previous module.

This module gives me only the key data I need, so I can push it directly to a Google Sheet without cleaning or parsing manually.

That completes the configuration of the Plumsail Documents → RegExp Match module.

Note Regular expressions depend on the exact structure of the text. If the PDF format is different — for example, line breaks are placed differently, or labels like TOTAL or BILL TO are written in another way, the pattern might fail and return no results.

To solve this, you can:

• Use multiple RegExp Match modules with different patterns for each known format
• Add conditional logic (Routers or Filters in Make) to detect which pattern to apply
• Use a simpler pattern that matches the entire line, then split the content with functions like split()

Google Sheets module to write extracted data

The final module in the scenario is Google Sheets → Add a Row. I used it to record invoice data into a spreadsheet automatically.

Here's how I configured it:

• Connected my Google account
• Set Search method to Search by path
• Selected My Drive
• Entered the path to the spreadsheet: /Invoice Register
• Selected Sheet1
• Set Table contains headers to Yes

Then I mapped the extracted values from the RegEx module into the table columns:

• Invoice No. (A) → invoice
• Date of Issue (B) → date
• Billed from (C) → client
• Total Amount (D) → total
• Status (E) → set to "Unpaid" by default

 

This completes the setup of the Google Sheets step. When the full scenario runs, each matching invoice is recorded in a new row automatically.

 

Automate more with extracted PDF text

This approach can be used in many other scenarios, not just for collecting invoice data.

• Send extracted data to an ERP or CRM. After parsing invoice fields, you can send them to systems like HubSpot, Zoho, or Bitrix24 to log payments or update customer records.
• Summarize documents using AI. Use tools like OpenAI or Google Vertex AI to generate a short summary or categorize the document based on its content.
• Create searchable archives. Store the extracted text along with the original PDF in a database, and make it searchable by keywords or metadata.
• Track keywords. Scan incoming documents for specific words like “overdue” or “urgent” and trigger notifications if needed.
• Index legal documents or applications. Extract fields from contracts, agreements, or forms and save them into structured tables for further use.

Once the text is extracted, it becomes much easier to work with documents across your entire workflow.

Start extracting data from PDF in Make with Plumsail Documents

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