The Portable Document Format (PDF) is the go to file format for sharing & exchanging business data. While you can view, save and print PDF files with ease, editing, scraping/parsing or extracting data from PDF files can be a pain.

For example, have you ever tried to extract text from PDFs or to extract tables from PDFs?  

Just try converting PDF bank statements to Excel or PDF documents to XML!

frustrated Dwight from The Office
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Challenges in PDF data extraction

Data extraction from PDFs is crucial for reorganising data according to your own requirements.

In other document formats such as DOC, XLS or CSV, extracting a portion of information is pretty straightforward. Just edit the data or copy and paste.

But this is quite challenging to do in the case of PDFs.

Editing is impossible and copy pasting just doesn’t maintain the original formatting & order - try extracting tables from a PDF!

When handling PDF data extraction in bulk, these issues can cause errors, delays and cost overruns that could seriously impact your bottomline!

Fortunately, there are solutions like Nanonets, that can extract data from PDF documents efficiently.

Let's look at the 5 most popular ways in which businesses extract data from PDFs.

5 ways to extract data from PDFs

Here are 5 different ways to extract data from PDF in an increasing order of efficiency and accuracy:


Need a smart solution for image to text, PDF to table, PDF to text, or PDF data extraction? Check out Nanonets' pre-trained data extraction AI for invoices, receipts, passports, driver's licenses & tables!

Automated data extraction using Nanonets

Copy and paste

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A copy & paste approach is the most practical option when dealing with a small number of simple PDF documents.

  • Open each PDF file
  • Selection a portion of data or text on a particular page or set of pages
  • Copy the selected information
  • Paste the copied information on a DOC, XLS or CSV file

This simple approach often results in data extraction that is erratic & error-prone. You will have to spend a considerable amount of time to reorganise the extracted information in a meaningful way.


Outsourcing manual data entry

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Handling manual data extraction from PDFs in-house for a large number of documents might become unsustainable and prohibitively expensive in the long run.

Outsourcing manual data entry is an obvious alternative that is both cheap and quick.

Online services like Upwork, Freelancer, Hubstaff Talent, Fiverr and other similar companies have an army of data entry professionals based out of middle-income countries in South Asia, South-East Asia and Africa.

While this approach can reduce data extraction costs and delays, quality control & data security are serious concerns!

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Data entry automation & automated data extraction solutions are therefore becoming more popular.


Want to capture data from PDF documents or convert PDF table to Excel? Check out Nanonets' PDF scraper or PDF parser to scrape PDF data or parse PDFs at scale!

A super-happy Nanonets user

PDF converters

PDF converters are an obvious choice for those concerned about data quality & data security.

PDF converters allow data extraction to be managed in-house while being fast and efficient. PDF converters are available as software, web-based online solutions and even mobile apps.

PDFs are most commonly converted to Excel (XLS or XLSX) or converted to CSV formats as they present tables in a neat way; PDF to XML converters are also popular.

Simply upload the PDF document and convert it into a format of your choice.

However, PDF converters are just not equipped to handle documents at scale. Bulk data extraction is just not possible and one has to repeat the data extraction process for each document, one at a time!

Here are some top PDF convertor tools/software:

  • Adobe
  • Simply PDF
  • SmallPDF
  • PDF2Go
  • PDFtoExcel
  • PDFelement
  • Nitro Pro
  • Cometdocs
  • iSkysoft PDF Converter Pro

PDF table extraction tools

table extracted from a PDF document

Very often, PDF documents contain tables along with text, images and figures. In many cases the data of interest usually lies in the tables.

PDF converters process the entire PDF document, without providing an option to limit the data extraction to a specific section in a PDF (such as specific cells, rows, columns or even tables).

PDF to table extraction tools do just that.

PDF table extraction tools/technologies such as Tabula & Excalibur allow you to select sections within a PDF by drawing a box around a table and then extracting the data into an Excel file (XLS or XLSX) or CSV.

While PDF to table tools give reasonably efficient results, you might require development effort or in-house experts to leverage the underlying technologies powering these tools to fit your own use cases.

Additionally such PDF data extraction tools only work with native PDF files and not scanned documents (which are more commonly used)!

Extracting data from PDF to Excel

You can import a PDF file directly into Excel and extract tabular data from it:

  1. Open an Excel sheet
  2. Data tab > Get Data drop-down > From File > From PDF
  3. Select your PDF file & click Import.
  4. You'll now see a Navigator pane displaying the tables & pages in your PDF along with a preview.
  5. Select a table & click Load. The table you selected will now be imported on to your Excel sheet.

This might work pretty well with simple tabular data. Complex tables or multi-page tables might throw up some formatting errors!


If your PDFs deal with invoices, receipts, passports or driver's licenses, check out Nanonets' PDF scraper or PDF data extractor to capture data from PDF documents.

Nanonets data extractor
Nanonets data extractor in action!

Automated PDF data extraction

Automated document data extraction software or AI-based OCR software like Nanonets provide the most holistic solution to the problem of extracting data from PDFs or extracting text from images. (What is OCR? - here's a detailed explainer)

They are dependable, efficient, extremely fast, competitively priced, secure & scalable.  They can also handle scanned documents as well as native PDF files.

Such automated PDF data extractors employ a combination of AI, ML/DL, OCR, RPA, pattern recognition, text recognition and other techniques to extract data accurately at scale.

Automated PDF data extraction tools, like Nanonets, use machine learning to provide pre-trained extractors that can handle specific types of documents.

Here's a quick demo of Nanonets' pre-trained table extractor:

Nanonets' pre-trained Table Extractor model

Apart from using pre-trained extraction models, you can also build your own custom AI to extract data from different documents. Here's how:

  • Collect a batch of sample documents to serve as a training set
  • Train the automated software to extract the data according to your needs
  • Test and verify
  • Run the trained software on real documents
  • Process the extracted data
How to Train your own OCR Model with Nanonets

Nanonets has many interesting use cases that could optimize your business performance, save costs and boost growth. Find out how Nanonets' use cases can apply to your product.


Update May 2022: this post was originally published in Oct 2020 and has since been updated numerous times.

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