A PDF parser, or PDF scraper, is a software that extracts data from PDF documents. PDF parsing is a popular approach to extract text, tables, images or data fields from batches of PDF documents.
Data stored within PDFs lacks any fundamental structure or hierarchy. They display content as a flat collection of characters and pixels on a 2D plane. Parsing PDF files is therefore much more difficult than say parsing an XML file or scraping a website. PDF parsers leverage advanced algorithms and libraries to identify/understand individual data elements within a PDF document.
A substantial proportion of business processes and communication are still driven by paper documents today. Scanning and digitizing these documents as PDFs or images allows businesses to share & store them more efficiently. 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 this traditional manual data entry process.
What sort of data can be parsed from PDFs?
Most PDF parsers can typically recognize and extract the following data from PDF documents:
- Single data fields or key value-pairs (name, ID#, dates etc.)
- Tables
- Lists or line items
- Text blocks or paragraphs of text
- Images
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:
- Headers
- Objects
- Cross reference tables
- Trailers
- Metadata (authors, document creation date, reference numbers, info about embedded images etc.)
What are common use cases for PDF parsers or PDF scrapers?
PDF parsers or scrapers are widely preferred in use cases that leverage intelligent document processing to reduce or eliminate manual data entry. This essentially covers most document management workflows such as indexing, categorisation, or classification that need to read data from PDF documents.
PDF parsing is also used as part of many business process automation workflows such as invoice processing (AP automation), expense management, resume parsing, KYC due diligence, insurance claims processing, ingesting patient records, and more.
PDF parsers are also commonly used to process data captured in forms or tables.
Popular PDF parsers to get started with
Here are some of the best PDF parsers that you can get started with:
- Smalot/PdfParser - PdfParser is a standalone PHP library that provides various tools to extract data from a PDF file.
- pdf-parse - pdf-parse is a pure javascript cross-platform module that extracts text from PDFs.
- Ikkuna/pdf2json - pdf2json is a node.js module that parses and converts PDF from binary to json format.
- adrienjoly/npm-pdfreader - Read text and parse tables from PDF files.Supports tabular data with automatic column detection, and rule-based parsing.
Apart from the modules and libraries above you can also check out business process automation software, like Nanonets, that come in-built with IDP or PDF-parsing capabilities.
Automate PDF parsing workflows with Nanonets. Sign up or schedule a demo to learn more.
