The Portable Document Format (PDF) is the go to file format for sharing & exchanging business data. You can view, save and print PDF files with ease.
But editing, scraping/parsing or extracting data from PDF files can be a big pain.
For example, have you ever tried to extract text from PDFs, extract tables from PDFs or make a flat PDF searchable?
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 simple. 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 or 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.
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 bank statements, invoices, receipts, passports, driver’s licenses & or any tabular data!
Copy and paste
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
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.
Want to capture data from PDF documents or convert PDF to Excel? Check out Nanonets’ PDF scraper or PDF parser to scrape PDF data or parse PDFs at scale!
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.
Here are some top PDF convertor tools/software:
💡
PDF converters are 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!
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.
💡
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.
Automated document data extraction software or AI-based OCR software like Nanonets provides the most holistic solution to the problem of extracting data from PDFs or extracting text from images.
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:
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
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 Dec 2022: this post was originally published in Oct 2020 and has since been updated numerous times.
Here’s a slide summarizing the findings in this article. Here’s an alternate version of this post.
