pdf4py 0.1.0

A PDF parser written in Python3 with no external dependencies.

pdf4py

A PDF parser written in Python 3 with no external dependencies.

The package pdf4py allows the user to analyze a PDF file at a very low level and in a very flexible way by giving access to its atomic components, the PDF objects. All through a very simple API that can be used to build higher level functionalities (e.g. text and/or image extraction). In particular, it defines the class Parser that reads the Cross Reference Table of a PDF document and uses its entries to give the user the ability to locate PDF objects within the file and parse them into suitable Python objects.

DISCLAIMER: this package hasn't reached a stable version (>= 1.0.0) yet. Although the parser API is quite simple it may change suddenly from one release to the next one. All breaking changes will be properly notified in the release notes.

Quick example

Here is a quick demonstration on how to use pdf4py. You can find more at the tutorials page.

>>> from pdf4py.parser import Parser
>>> fp = open('tests/pdfs/0000.pdf', 'rb')
>>> parser = Parser(fp)
>>> info_ref = parser.trailer['Info']
>>> print(info_ref)
PDFReference(object_number=114, generation_number=0)
>>> info = parser.parse_reference(info_ref)
>>> print(info)
{'Creator': PDFLiteralString(value=b'PaperCept Conference Management System'),
    ... , 'Producer': PDFLiteralString(value=b'PDFlib+PDI 7.0.3 (Perl 5.8.0/Linux)')}
>>> creator = info['Creator'].value.decode('utf8')
>>> print(creator)
PaperCept Conference Management System

Installation and updates

You can install pdf4py using pip:

python3 -m pip install pdf4py

or download one of the releases and use the setup.py script.

The master branch is used for development and it is not advised to use it in production.

For this package the semantic versioning (specification 2.0.0) is adopted.

Extracting text or images

Extracting text from a PDF and other higher level analysis tasks are not natively supported as of now because of two reasons:

• their complexity is not trivial and would require a not indifferent amount of work which now I prefer investing into developing a complete and reliable parser;
• they are conceptually different tasks from PDF parsing, since the PDF does not define the concept of document as a sequence of paragraphs, images, and other objects that can be normally considered content.

Therefore, they require a separate implementation built on top of pdf4py. In don't exclude that in future these functionalities will be made available as modules in this package, but I am not planning to do it anytime soon.

Why this package

One day at work I was asked to analyze some PDF files. To my surprise I had discovered that there was not an established Python module to easily parse a PDF document. In order to understand why I delved into the PDF 1.7 specification: since that moment I've got interested more and more in the inner workings of one of the most important and ubiquitous file format. And what's a better way to understand the PDF than writing a parser for it?

Documentation

You can read the documentation on readthedocs.io.

Contributing

Contributions are more than welcome! Please, when writing code or documentation for this package remind:

• to use the numpy docstring conventions for documenting code.
• to follow the Python guideline (PEP 8) when writing code.
• pdf4py is designed to be readable and easy to work with. I prefer readability over (not so significant) performance improvements.
• pdf4py is designed to be modular, flexible but also easy to use. It shouldn't be complicated for the user to perform one particular task.
• to adopt as much as possible a test-driven development process. Each contribution must be accompanied by a test addition/modification.

If you are wondering in which way you can help, check the TODO list. For now it will do as a simple "road map".

If you have found a bug, please file a new issue here on GitHub. Proposing fixes, changes and additions can be done through a pull request.