Advanced PDF parsing for python
Project description
Burdoc: Advanced PDF Parsing for Python
A python library for extracting structured text, images, and tables from PDFs with context and reading order.
Table Of Contents
- Table Of Contents
- About the Project
- Quickstart
- Usage
- Roadmap
- Built With
- Contributing
- License
- Authors
- Acknowledgements
About the Project
Why Another PDF Parsing Library?
Excellent question! Between pdfminer, PyMuPDF, Tika, and many others there are a plethora of tools for parsing PDFs, but nearly all are focused on the initial step of pulling out raw content, not on representing the documents actual meaning. Burdoc's goal is to generate a rich semantic representation of a PDF, including headings, reading order, tables, and images that can be used for downstream processing.
Key Features
-
Rich Document Representation: Burdoc is able to identify most common types of text, including:
- Paragraphs
- Headings
- Lists (ordered and unordered)
- Headers, footers and sidebars,
- Visual Asides such as read-out boxes
-
Structured Output: Burdoc generates a comprehensive JSON representation of the text. Unlike many other tools it preserves information such metadata, fonts, and original bounding boxes to give downstream users as much information as is needed.
-
Complex Reading Order Inference: Burdoc uses a multi-stage algorithm to infer reading order even in complex pages with changing numbers of columns, split sections, and asides.
-
ML-Powered Table Extraction: Burdoc makes use of the latest machine learning models for identifying tables, alongside a rules-based approach to identify inline tables.
Limitations
- OCR: As Burdoc relies on high-precision font and location information for it's processing it is likely to perform badly when parsing OCR'd files.
- Right-to-Left Text: All parsing is for left-to-right languages only.
- Complex Figures: Areas with large amounts of text arranged around figures in a arbitrary fashion will not be extracted correctly.
- Forms: Currently Burdoc has no way to recognise complex forms.
Quickstart
More detailed information on running Burdoc can be found here - Docs
Prerequisites
ML Prerequisites
The transformer-based table detection use by Burdoc by default can be quite slow on CPU, often taking several seconds per page, you'll see a large performance increase by running it on a GPU. To avoid messing around with package versions after the fact, it's generally better to install GPU drivers and GPU accelerated versions of PyTorch first if available.
Installation
To install burdoc from pip
pip install burdoc
To build it directly from source
git clone https://github.com/jennis0/burdoc
cd burdoc
pip install .
Developer Install
To reproduce the development environment for running builds, tests, etc. use
pip install burdoc[dev]
or
git clone https://github.com/jennis0/burdoc
cd burdoc
pip install -e ".[dev]"
Usage
Burdoc can be used as a library or directly from the command line depending on your usecase.
Command Line
usage: burdoc [-h] [--pages PAGES] [--no-ml-tables] [--images] [--single-threaded] [--profile] [--debug] in_file [out_file]
positional arguments:
in_file Path to the PDF file you want to parse
out_file Path to file to write output to. Defaults to [in-file-stem].json
optional arguments:
-h, --help show this help message and exit
--pages PAGES List of pages to process. Accepts comma separated list and ranges specified with '-'
--no-ml-tables Turn off ML table finding. Defaults to False.
--images Extract images from PDF and store in output. This can lead to very large output JSON files. Default is False
--single-threaded Force Burdoc to run in single-threaded mode
--profile Dump timing information at end of processing
--debug Dump debug messages to log
Library
from burdoc import BurdocParser
parser = BurdocParser(
use_ml_table_finding: bool=False, # Use ML table detection
extract_images: bool=False, # Store extracted images
generate_page_images: bool=False, # Generate and store images of each PDF page
max_threads: Optional[int]=None # Maximum number of threads to use. Set to None to use default or 1
# to force single threaded
)
content = parser.read('file.pdf')
Roadmap
See the open issues for a list of proposed features (and known issues).
Built With
Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- If you have suggestions for adding or removing projects, feel free to open an issue to discuss it, or directly create a pull request after you edit the README.md file with necessary changes.
- Please make sure you check your spelling and grammar.
- Create individual PR for each suggestion.
Creating A Pull Request
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
License
Distributed under the MIT License. See LICENSE for more information.
Authors
- jennis0 - Github Profile
Acknowledgements
- ShaanCoding - ReadME-Generator
- ImgShields
Project details
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