PDF Analysis Tools: Part 2 of the PDF Experiment

A while back I posted about the PDF experiment I’m doing to explore some character encoding anomalies in our PDF e-theses. The project is complete now, but I wanted to outline some of the steps we went through before I jump right to what we found. Step one was analyzing the PDFs to figure out what, exactly, was going on underneath the hood. But in order to do that we actually needed a preliminary step, which was finding a tool that would help us do that analysis effectively.

This actually turned out to be more difficult than it seemed at first. The errors we were looking for aren’t related to the common issues digital preservationists are most often concerned with, such as format identification and validation. Tools like JHOVE weren’t quite right for our purposes, because the errors we encountered were all deep within the content stream. We had plenty of perfectly valid PDFs (checked against JHOVE just to be sure) that would render fine in a PDF reader, but the text came out with unusual characters if copied and pasted into another document (any other document, we tried all the text editors we could find).

So our challenge was to find a tool that would help us analyze the text encoding within the content stream. It turned out Adobe Acrobat Pro was the most useful tool for manually inspecting the errors, since its preflight function allowed us to view the actual content stream encoding at the point in the document where we had a known error, such as a ligature that wouldn’t convert to its correct ASCII characters on copy-and-paste. However, this required that we already know the PDF had said error, and what we really wanted was a tool that would help us identify PDFs with those errors from a larger collection of PDF e-theses.

We tested 16 tools to see if any would solve this problem for us. Questions we asked as we tested each tool included:

  • Can the tool accurately and reliably diagnose the problems we are working on?
  • Does it have repair capabilities, or is it diagnostic-only?
  • Can it be run as a batch process (from the command line) or does it require a GUI?
  • Does it run on Linux, Windows, Mac?
  • Can it report on whether the document meets archival standards like PDF/X, PDF/A etc.?
  • Who creates and maintains the tool?
  • Is there good documentation/support?
  • Is there a cost?
  • Is it open source?

We were hoping to not only find a tool to suit our needs, but one that was free and open source if possible. For each tool, we went through the following steps for initial testing:

  1. Install the tool on local machine or server.
  2. Run the tool on each of 32 sample PDF theses that represent the different types of problems we had encountered.
  3. Record results/output and comments about the tool’s functions in a shared spreadsheet.
  4. After testing the tool and getting a feel for how it works, record general observations and answers to the questions above on a wiki page for the project.

After going through this process for all 16 tools, we hadn’t found a tool that did exactly what we needed. But we found that there were two tools whose text extraction features could be used to serve our purposes. One, PDFBox, was able to work with the ligature issue for most of the PDFs, providing cleanly extracted text that resolved to its correct ASCII characters. This was helpful in two ways: one, it led us to believe that the issue we were dealing with was also one of rendering rather than strictly encoding (whatever the encoding is for those ligatures, it can be converted correctly by at least one program); and two, it gave us an option for extracting readable text for both cataloging and indexing in our repository.

The other tool, PDFMiner, helped us by, in a sense, not working: on extracting the text, every single PDF that we had identified as one with ligature issues showed those ligature issues in the extracted text. Therefore, we decided that whatever the encoding is that’s causing the problem, PDFMiner is not able to resolve it to the correct ASCII characters. While this might sound like a deficiency in the tool, for our needs it actually allowed us to create the exact tool we were looking for!

My colleague Rich Wenger wrote a series of scripts that will extract text from a PDF using PDFMiner and then search for the anomalous characters that appear when there are known text encoding issues present. This allowed us to run his scripts over the entire collection of e-thesis PDFs to see how many have underlying text encoding issues. After running these scripts, we determined that about 30% of the collection presents the ligature encoding error, and a much smaller percentage presented other errors such as excessive line breaks and extra characters inserted in the encoded text. In my next post, I’ll discuss possible implications of these results and some of the steps we’re taking to mitigate these issues in the future. Stay tuned!

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