How to extract data from PDFs: 5 methods compared

To extract data from a PDF, you have 5 options. You can type it in by hand, convert the PDF to Excel or Word, use a table extraction tool, build a rule-based template for each layout, or use AI-based extraction that finds fields on any layout. Copying or converting works for a handful of files; invoices, statements and forms that arrive every day in many layouts need AI extraction with validation.

Why extracting data from PDFs is hard#

PDF was built to make a page look the same everywhere, not to make its data reusable. It stores characters and their positions, not "this is the invoice total".

• Layouts vary

  Every vendor, bank and manufacturer lays out invoices, statements and spec sheets differently.

• Scans have no text

  A scanned PDF is a picture of the page. Nothing can be copied until OCR reads it.

• Structure is complex

  Multi-column pages, nested tables, and tables that continue onto the next page.

• Volume breaks manual fixes

  A process that works for 10 files a week breaks at 10,000.

A basic PDF parser returns the page's text in rough reading order, with nothing to say which value is which. Layout analysis and extraction turn it into fields.

First, check what kind of PDF you have#

Try selecting the text in a PDF viewer; what happens tells you the first step.

To make a scanned PDF searchable, or to turn it into an editable Word file, a desktop OCR app is enough. See the best OCR software.

5 ways to extract data from PDFs#

We wouldn't shortlist tools by whether they're marketed as OCR, document AI, document intelligence or an IDP platform, since those names all point at the same category. We'd rather take two or three candidates and run them on our own invoices, statements or forms to see which one actually gets the fields right without days of setup.

1. Manual data entry

Someone reads each PDF and types the values in. It takes no setup, but is slow and error-prone at volume.

2. PDF converters

Adobe Acrobat Pro and similar tools turn a whole PDF into Excel or Word. Complex layouts come out with split cells and merged columns.

3. PDF table extraction tools

Open-source libraries such as pdfplumber, Camelot and Tabula return a PDF's tables as rows and columns. This pulls the transactions from a text-based bank statement (tested on Python 3.11 with pdfplumber 0.11.10; pip install pdfplumber).

import pdfplumber

with pdfplumber.open("statement.pdf") as pdf:
    for page in pdf.pages:
        for table in page.extract_tables():
            header, *rows = table
            for row in rows:
                print(dict(zip(header, row)))

Output:

{'Date': '07/02/2026', 'Description': 'Payroll deposit', 'Amount': '2,450.00'}
{'Date': '07/05/2026', 'Description': 'Rent payment', 'Amount': '-1,800.00'}
{'Date': '07/19/2026', 'Description': 'Card purchase', 'Amount': '-86.40'}

It works because the table has ruling lines and a text layer. For scans and tables that run across pages, see table extraction from PDF.

4. Rule-based templates

A template reads each field from a fixed spot on a known layout, such as the value to the right of "Total Due". Every new layout needs a new template; see zonal OCR.

5. AI-based (intelligent) PDF extraction

Models trained on many examples find fields from text, layout and visual cues, so a new layout usually needs no setup. They read scans, join tables across pages and give every value a confidence score.

Play video: Process ANY Complex Document with AI | No-Code Data Extraction Solution

How to convert PDF data to Excel or Google Sheets#

For bank statements, see how to convert PDF bank statements to Excel.

How to extract pages from a PDF#

Extracting pages copies the pages you pick into a new PDF; it doesn't pull out the data on them.

In Python, pypdf copies a range of pages into a new file (tested with pypdf 6.19).

from pypdf import PdfWriter

writer = PdfWriter()
writer.append("packet.pdf", (2, 5))  # pages 3 to 5; numbering starts at 0
writer.write("pages-3-to-5.pdf")

For PDFs that hold several documents, such as a loan packet, classification and splitting cut the file where each document starts and label each part. In Docsumo, auto-classification and splitting are on the Business plan.

Common PDF extraction failures and fixes#

How to automate PDF data extraction#

Automated PDF processing runs the same steps on every file, from intake to export. Automated document processing covers it in depth.

1. Pick the documentsStart with your highest-volume type, such as invoices or bank statements.
2. Define the fieldsList what you need, including tables, and the format for each.
3. Connect intakeUploads, an API or a shared inbox, depending on how the PDFs reach you.
4. Extract and validatePre-trained models where they exist; rules and custom models for the rest.
5. Review exceptionsA person checks only low-confidence or failed fields, each shown on the PDF.
6. ExportDownload to Excel, or send results to your ERP or loan system through an API and webhooks.

Checks to run on extracted PDF data#

These checks catch most errors before they reach your systems.

• Totals add upLine items and tax sum to the total; transactions reconcile to the closing balance.
• Required fields are presentEvery field your system needs, in the right format.
• Dates are validThey parse, fall in a sensible range and run in order.
• Names match your recordsVendor, customer or account details agree with your master data.
• Uncertain values were reviewedAnything below your confidence threshold went to a person first.

In Docsumo, fields below your confidence threshold go to a reviewer, who clicks a field to see its source line on the page, and you can add your own checks as workflow steps, either an AI step or your own Python code. Try it free for 14 days on up to 1,000 pages; see pricing.

• 99%99%field-level accuracy across 250+ document types
• 95%+95%+of documents processed straight through, without manual review

The method that survives at your volume#

Copy, convert or a table tool works fine for a handful of files. Once PDFs arrive every day in a mix of layouts and scans, and the numbers have to be right, that approach stops scaling and AI-based extraction with validation and review takes over. The best AI data extraction software compares the tools.

Book a demo with a few of your own PDFs, or start a free trial.

Frequently asked questions#