While organizations exchange data & information electronically, a substantial amount of business processes are still driven by paper documents (invoices, receipts, POs etc.). Scanning these documents, as PDFs or images, allows businesses to share & store them more efficiently online.
But in most cases the data stored in these scanned documents is still not machine-readable and needs to be extracted manually; a time-consuming, error-prone & inefficient process!
PDF parsers replace the traditional manual data entry process by extracting data, text or images from non editable formats such as the PDF. Document parsing solutions are available as libraries for developers or as dedicated PDF parser software. PDF parsers or PDF parsing technology power popular solutions that allow users to:
How does PDF parsing work?
PDF parsers leverage advanced algorithms to identify individual data elements in a PDF document.
PDF parsing thus facilitates the extraction of information from non editable file formats and presents it in a convenient and machine-readable manner. Data that is parsed from PDFs in this manner is easier to organize, analyze and reuse in organizational workflows. Advanced PDF parsing techniques can be tapped to convert PDF data to database entries.
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Challenges Involved in Scraping or Parsing PDFs
PDF parsing is difficult.
PDF documents are non editable and do not have a standard format. Also data stored in PDFs is inherently flat & unstructured – they contain no order or hierarchy or tag.
Essentially, a PDF simply displays characters/pixels at a set coordinate on a 2D plane. The PDF format doesn’t differentiate between text, images, tables or other elements.
Recognizing or parsing data becomes quite challenging when the data isn’t represented in a structured hierarchical manner.
PDFs can store massive amounts of data over multiple pages; embedding rich media types and attachments. And organizations tend to deal with a lot of PDF documents.
PDF parsers are equipped to recognize and extract data from PDF documents at scale!
What Kind of Data Can be Parsed from PDFs
PDF parser software (such as Nanonets) can typically recognize and extract the following data from PDF documents:
- Text paragraphs
- Single data fields (dates, tracking numbers, …)
- Key value-pairs
Command line PDF parsing tools (preferred by developers) like PDFParser, pdf-parser.py, make-pdf, pdfid.py etc. can predominantly pull out the following properties that describe the physical structure of PDF documents:
- Metadata (authors, document creation date, reference numbers, info about embedded images etc.)
- Text from ordered pages
- Cross reference table
PDF Parsing Use Cases
PDF parsers or PDF scrapers are widely preferred in use cases that deal with intelligent document processing or business process automation. This essentially covers any organizational document management workflow that needs to automatically extract data from PDF documents:
Companies spanning the Finance, Construction, Healthcare, Insurance, Banking, Hospitality, & Automobile industries use PDF parsers like Nanonets to parse or scrape PDFs for valuable data. (Check out OCR finance or OCR accounting for more details)
Benefits of Parsing PDF documents
Parsing PDF documents used in your organization’s workflows can greatly optimize your business processes. Automated PDF parsers, such as Nanonets, can further streamline business processes by leveraging automation, AI & ML capabilities to drastically reduce inefficiencies. Here are some of the benefits of PDF parsing:
- Save time & money that can be spent more fruitfully
- Reduce dependence on manual processes & data entry
- Eliminate errors, duplication and rework
- Improve accuracy while increasing scale
- Reduce document processing durations
- Optimize workflows & internal data exchange
- Eliminate the use & storage of physical documents
- Turn unstructured data into structured formats such as XML, JSON, Excel or CSV
How to Parse PDF Files with Nanonets
Nanonets PDF parser has pre-trained models for specific document types such as invoices, receipts, passports, driver’s license, resumes and more. Just login & select the appropriate pre-trained model for your use case, add the PDF files, test & verify, and finally export the extracted data in a convenient structure format. Follow these instructions to extract text or tables from PDF documents with Nanonets pre-trained PDF parser models.
If the pre-trained models do not meet the specific requirements of your use case, build a custom PDF parser model with Nanonets. Follow these instructions to parse PDFs with a custom PDF parser:
- Just upload some training PDF files
- Annotate the PDFs to highlight the text/data of interest
- Train the model
- And finally test & verify the model on a bunch of sample PDF documents pertinent to your use case.
Why Nanonets is the Best PDF Parser
Nanonets is an accurate & robust PDF parser that is easy to set up and use, offering convenient pre-trained models for popular organizational use cases. Parse PDFs in seconds or train a model to parse data from PDFs at scale. The advantages of using Nanonets over other PDF parsers go far beyond just better accuracy:
- Nanonets can extract on-page data while command line PDF parsers only extract objects, headers & metadata such as (title, #pages, encryption status etc.)
- Nanonets PDF parsing technology isn’t template-based. Apart from offering pre-trained models for popular use cases, Nanonets PDF parsing algorithm can also handle unseen document types!
- Apart from handling native PDF documents, Nanonets in-built OCR capabilities allows it to handle scanned documents and images as well!
- Robust automation features with AI and ML capabilities.
- Nanonets handles unstructured data, common data constraints, multi-page PDF documents, tables and multi-line items with ease.
- Nanonets is essentially a no-code tool that can continuously learn and re-train itself on custom data to provide outputs that require no post-processing.
Update May 2022: this post was originally published in April 2021 and has since been updated multiple times.