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NOTICE: This software is phasing out of development. Recommend to use pdfgrep instead. Example notebook with no-install plotting implementation.

pdfca (PDF Corpus/Content Analysis) can assist with managing a PDF corpus for textual characterization. It provides various commands for building and interacting with such a corpus through Pandas dataframes stored locally via Apache Arrow binaries, which the user can then pass to other software (including languages such as R) for further analysis.

Installation, setup, and help

Install Python 3. Then download or clone this repository and from the command line (Terminal, PowerShell, cmd, etc.) cd into the downloaded/cloned directory and run:

pip install . (only once per machine)

and then:

pdfca init (to initialize an empty binary file for data storage)

For more information on the program and its commands, run:

pdfca --help for an overview or use the --help flag on any command.


Run pdfca extract to pull text from PDF files (see below notes about preparing data for extraction). The program will not extract text from PDFs that are already listed in the loaded binary file. To re-extract text from certain files, run pdfca cut FILE for each file to be removed, then re-run the extract command.

The terminal may print the following error while parsing PDFs: PdfReadWarning: Xref table not zero-indexed. ID numbers for objects will be corrected. []. This is a known issue with the dependency PyPDF2 and will likely not impact the search.

Preparing PDFs

PDFs can be read from either a relative or absolute path. The names of the files will be their identifiers in the dataframe, so it's advisable to keep them as descriptive but short as is feasible (with no duplicate filenames). The program does not edit or overwrite input PDFs, meaning it can be run multiple times on the same set of files.

For successful text extraction, input PDFs must have been processed using Optical Character Recognition (OCR). A simple test for this is to open a PDF in a reader program (Adobe Reader or similar) and attempt to highlight text on several pages. If the text can be highlighted, the PDF should be ready. OCR can have variable results, and a file that has a low-quality page image or that has been processed using less-capable OCR software may have inaccurately-recognized text. To test this, copy text from multiple pages to a text file and check for errors. pdfca can only search the text it is provided, and the accuracy of its results depends on the quality of the OCR process.

pdfca extracts each individual page in each PDF, meaning that for use cases where records must be labelled with real page numbers, PDFs may need to be trimmed to the desired page range. This could be done with Adobe Acrobat or a similar piece of software.

Notes on stored data

  • For analysis beyond the basic view and search commands included, generated data may be imported to any software or language that supports the Parquet or Feather storage formats.
  • Please note that the Feather format, though more interoperable with other software and languages, is not currently designed for long-term data storage.
  • For more information, explore the Apache Arrow documentation.


The following citation may be used when referencing this program:

Quiring, T. (2018, 2019). pdfca (PDF Corpus/Content Analysis) [command-line application]. DOI: 10.5281/zenodo.1972950


Batch process text-containing PDF files for corpus and content analysis.







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