How to Extract Specific Data from PDF Documents Automatically

PDF text extraction vs structured PDF data extraction

A PDF can contain hundreds or thousands of pieces of information, but most business processes only require a small part of them.

Imagine a supplier registration document containing company details, contacts, payment terms, banking information and legal text. Your workflow might only need:

company_name
registration_number
contact_email
payment_terms

A traditional PDF text extractor may return the document like this:

SUPPLIER REGISTRATION FORM

Northstar Services
Registration Number: REG-58421

Primary Contact
Sarah Martin
finance@example.com

Payment Terms
Invoices shall be paid within 30 days...

The information is technically available, but someone — or another system — still has to determine which value corresponds to the supplier name, registration number, email or payment terms.

With structured PDF data extraction, the target fields are defined first:

{
  "company_name": "Northstar Services",
  "registration_number": "REG-58421",
  "contact_email": "finance@example.com",
  "payment_terms": "30 days"
}

This approach is also described as document field extraction, key information extraction or key-value extraction.

4 common ways to extract data from PDFs

1. Copy and paste the information manually

For a single document, manual extraction may be enough. You open the PDF, find the required value and copy it into your spreadsheet, CRM or another system.

The problem appears when the same process has to be performed across dozens or hundreds of files. Every PDF still needs to be opened, searched, checked and copied manually.

2. Convert the entire PDF

Another option is to convert the PDF into text, CSV or a spreadsheet. This works when most of the document content is relevant.

If you convert the entire PDF and still need to search the converted file for the information you wanted, you have converted the document — not automated the extraction.

For a 15-page document where only five values matter, converting everything can simply move the same search problem into another format.

3. Build or use a PDF parser

A PDF parser gives developers more technical control. A custom workflow may read PDF text, detect tables, identify patterns, use page coordinates and map values to predefined fields.

This can make sense for highly specific technical requirements, but it also creates maintenance work when layouts or formatting change.

4. Use field-based automated data extraction

Field-based extraction starts with the result you want. Instead of extracting everything first, you define fields such as:

company_name → Text
registration_number → Text
contract_start_date → Date
annual_value → Number
approved → Boolean

The extraction process then identifies the values corresponding to that structure.

How to extract specific data from a PDF with Heyyel Data Extractor

Heyyel Data Extractor lets you define the information you want to retrieve before processing the document. The process can be reduced to five main steps.

Step 1 — Upload your document

Start by uploading the file you want to process. Heyyel Data Extractor supports PDF, PNG, JPG, WebP and TIFF, so the source can be a digital PDF, scan or image.

Step 2 — Choose a document type or start from Custom / Blank

You can start from several document types, including:

• Invoice
• Receipt
• ID Card / Passport
• Business Card
• Contract
• Bank Statement
• Raw data
• Custom / Blank

Presets provide suggested fields. If the document does not fit a standard category — or if you want complete control over the result — use Custom / Blank.

Step 3 — Define exactly what you want to extract

Each field describes a piece of information you want returned.

Use precise field names

A field named date may be too vague if a contract contains a signature date, effective date, renewal date and expiration date.

Instead, use a precise name such as contract_effective_date.

Add an extraction hint when the field needs context

If the field name alone is not enough, add an instruction describing what the value means.

Extract the final amount payable after taxes and discounts.

Or for a date:

Extract the date on which the agreement becomes effective, not the signature date.

Fields can also be marked as required. Heyyel Data Extractor supports 77 languages, with automatic language detection or manual selection when needed.

Step 4 — Run the extraction and review the result

Once the fields are configured, run the extraction. The output follows the structure you defined rather than simply reproducing the PDF content.

{
  "supplier_name": "Northstar Services",
  "registration_number": "REG-58421",
  "contact_email": "finance@example.com",
  "payment_terms": "30 days",
  "approved": true
}

The extracted values can be reviewed alongside the source document, which helps when a PDF contains several similar values.

Step 5 — Export the data as JSON or CSV

Once extraction is complete, the information can be exported in a structured format.

PDF to JSON

{
  "supplier_name": "Northstar Services",
  "registration_number": "REG-58421",
  "contact_email": "finance@example.com",
  "payment_terms": "30 days"
}

PDF to CSV

This differs from converting the entire PDF to CSV because only the fields selected for extraction need to become part of the dataset.

What about scanned PDFs?

Not every PDF contains selectable text. A scanned PDF may contain images of pages, which means OCR is needed before the relevant information can be extracted.

OCR answers “What text appears on this page?” Data extraction answers “Which values correspond to the information my workflow needs?”

Raw data or specific fields: which should you use?

Common challenges when extracting data from PDFs

Different document layouts

The same information can appear in different document layouts. One supplier may display a registration number in the header while another places it inside a company information table.

Ambiguous values

An invoice may contain an invoice date, due date and payment date. A contract may contain a signature date, effective date and expiration date. The challenge is identifying which value represents the field you need.

Scanned or image-based documents

Scanned PDFs may not contain a machine-readable text layer, so OCR is required before their information can be structured.

Tables and repeated information

Invoice line items, bank transactions, products and other repeating records need to remain associated with one another rather than being returned as unrelated text.

Business-specific terminology

One document may use Customer Ref, another Account Reference, and another Client Number for the same concept. Defining the meaning of the field can therefore matter more than matching one label exactly.

When is field-based PDF extraction useful?

Which PDF extraction method should you choose?

Start with the output you need. If the final result is supposed to be a known set of fields, structured field extraction is usually a better fit than converting the entire document.

Frequently asked questions