Datalogics CTO, Matt Kuznicki, discusses content extraction from PDFs and Datalogics PDF Alchemist.

1. Intelligently Extracting
Data from PDFs
Presented by Matt Kuznicki
Chief Technical Officer, Datalogics

2. Agenda
• Technical Challenges in PDF Data Extraction
• Key Considerations for Data Extraction
• Use Cases
• About Datalogics PDF Alchemist

3. About Me
• Chief Technical Officer at Datalogics
• Vice Chairman of PDF Association Board of Directors
• Worked extensively with PDF for over 15 years
• Active participant in the PDF standards community

4. Technical Challenges in PDF Data Extraction

5. Extraction: Technical Challenges
• PDF is a page description language – elements
typically have fixed position on a physical plane
• Elements are not necessarily defined in order of
appearance
• Richer vocabulary for expressing elements than other
formats
• Structure and semantics of elements not commonly
stated

6. At the time PDF was conceived in the 1990s, reliable rendering
for human readers was an important issue
• Focus was on retrieving the information needed to display
and print pages for peoples’ use
• Affordances to give content semantics came much later
• Community has made great strides in allowing for machine
interpretation, but proper use requires expertise in the domain
• Structure and semantics are optional – usage is still rare
• This is NOT a PDF specific issue
PDF as Page Description Language

7. PDF as Page Description Language
• PDF format most concerned with expressing exact visual
representation
• Elements are placed at fixed positions on virtual pages, in small
discrete pieces
• Not as fine-grained as individual dots in a raster file, but not as
continuous content like most HTML
• No guarantee of sentences or even letters grouped together to
form whole words in a PDF data stream
• Usually PDF files contain no information about how elements relate to
each other

8. PDF as Page Description Language
PDF pages often contain content that is a byproduct of
breaking data into page-size chunks, such as:
• Page numbers
• Page headers and footers
• Guides and information for printing
These elements are not usually considered real
document data, extracting these as content is usually
undesired.

9. Elements and Ordering
Small graphic elements can mean big extraction problems:
• Contents of a PDF page can be specified in an order very different
from how we read
• Humans automatically see a page flow that is not always present in
the PDF data stream
• Words, images and other elements on a page may have the marks
that constitute them spread far throughout the page marking stream
• Without ordering information, flow of PDF content must be
heuristically derived and is subject to differing interpretations

10. Richer Vocabulary For Elements
PDF includes a richer way to express elements than most other languages:
• Images can be in many different forms, including GIF, JPEG, PNG, JPEG 2000
and JBIG2 derived formats
• Fonts can be in several forms, including OpenType, TrueType, Type 1, CFF,
multiple master; or expressed in PDF element syntax
• Text may be expressed in a way that includes Unicode information – or in one of
hundreds of encodings – but no Unicode information is actually required
• Rich transparency and blending model allows for complex element interaction
• Content may be optionally present or absent from a page depending on a
number of different triggers and conditions

11. Structure and Semantics
Information on the structure and semantics of a PDF page is
usually not present:
• Lists are really just bunches of words and sometimes symbols
humans interpret as bullets or delimiters
• Tables are really just a series of lines and shaded boxes, and
bunches of words, that humans interpret together as rows,
columns and headers
• Paragraphs are really just bunches of words positioned on a
page in such a way that humans interpret them as sentences
grouped together
• Columns don’t exist in the PDF data stream, it’s just that us
humans see elements grouped in a way that suggests columns

12. Structure and Semantics
When creating PDFs, it is possible to include structure and
semantics into the PDF:
• Creating tagged PDF means the information for conversion is
included directly into the PDF when it’s created – at the right
time!
• Easy to convert tagged PDF into other formats and to reflow
• Not all tagged PDF is of good quality – and not all generators
emit useful tagged PDF!
Bottom line: you can’t count on getting PDF that has easily
extractable content!

13. Key Considerations For Data Extraction

14. Extraction: Key Considerations
• Content extraction means different things to different
audiences
• Know your audience and its goals
• Different goals are best met through different means

15. Extraction: Different Meanings
Let’s take a PDF that’s just one image of a scanned page:

16. Extraction: Different Meanings
Let’s take a PDF that’s just one image of a scanned page:
• Does extracting the content mean returning the image?
• Does extracting the content mean OCRing the image and
returning the text?
If the PDF is an image and text underneath – is the content the
image, the text, or both?

17. Know Your Audience’s Goals
Different audiences have different needs:
• Extraction for indexing or summarization typically requires a
pure text stream of paragraphs
• Extraction for loading contents into a database for machine
learning typically does not need appearance preservation
• Extraction for presentation on a different screen or medium
typically means content order should be preserved but the
appearance is expected to change

18. Different Goals, Different Means
Different goals mean different trade-offs:
• Indexing, machine learning, data mining – preservation of text
and reconstruction of semantics most important
• Reformatting for reflow or format conversion – balance between
text preservation and appearance preservation needed
• Reformatting for reliable viewing across devices – appearance
preservation most important, text preservation secondary
• Semantic reconstruction usually not required

19. Use Cases

20. Use Cases for Content Extraction
• Conversion to HTML for viewing PDF without a PDF viewer
• Converting PDF into a reflowable HTML representation
• Extraction of PDF contents for machine understanding

21. Viewing PDF Without a PDF Viewer
PDF extraction and conversion revolves around visual appearance:
• Extract content and into a 1 to 1 analogue in a different fixed
layout (HTML + SVG, raster image, print-out, etc.)
• Convert extracted content into different visual primatives
• Reliable viewing, but maintains disadvantages of PDF format
This is the simplest and easiest way to convert PDF content for
human reading – but doesn’t extract the content into a useful form
for machines

22. Converting PDF Into Reflowable HTML
PDF extraction and conversion balances needs of humans and
machine understanding:
• Elements are analyzed in page context and turned back into text
flows, lists, tables, and other structured elements
• Elements that can’t be expressed in HTML are usually rendered
to allow proper viewing, at the loss of search-ability
• Navigation elements – bookmarks, links – are converted into
HTML equivalents for easy browsing
• Pagination artifacts are discarded when possible
Resulting HTML is reflowable and gives good document reading
experience, but appearance typically changes somewhat to be
more “HTML-ish”

23. Extraction of PDF Contents For
Machine Understanding
PDF extraction focused on text and structure:
• Elements are analyzed in page context and turned back
into text flows, lists, tables, and other structured elements
• Text elements that can’t be expressed in HTML are usually
left as text, sacrificing visual fidelity
• Navigation elements – bookmarks, links – are converted so
that automated processes can crawl these
• Pagination artifacts should be discarded when possible

24. About Datalogics PDF Alchemist

25. Datalogics PDF Alchemist
• Works on untagged PDFs – handles existing PDFs, does not
require workflow changes or regenerating/reconstructing source
PDFs
• Turns placed words in PDFs back into reflowable text
• Re-creates tables and lists from page content
• Removes pagination artifacts such as page #s and running
headers
• Converts PDF into single-page HTML5 + CSS or into EPUB
packages
• Converts PDF forms into fixed-layout HTML forms for use in
mobile environments

26. Summary

27. Extracting Content from PDFs
Intelligently extracting content from PDF files requires:
• Seeing pages in a way like a human reads them
• Figuring our the logical structure of the pages
• Putting text back together into text flows
• Putting all these elements back together in the correct order
• Compensating intelligently for differences between PDF and
the chosen method of receiving content

28. Questions?
Matt Kuznicki
Chief Technical Officer
mattk@datalogics.com
LinkedIn: mattkuznicki
Datalogics Inc.
www.datalogics.com
Twitter: @DatalogicsInc