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Simple package to extract text with coordinates from programmatic PDFs

Project description

Docling Parse

PyPI version PyPI - Python Version uv Pybind11 Platforms License MIT

Simple package to extract text, paths and bitmap images with coordinates from programmatic PDFs. This package is used in the Docling PDF conversion. Below, we show a few output of the latest parser with char, word and line level output for text, in addition to the extracted paths and bitmap resources.

To do the visualizations yourself, simply run (change word into char or line),

uv run python ./docling_parse/visualize.py -i <path-to-pdf-file> -c word --interactive
original char word line
screenshot screenshot screenshot screenshot
screenshot screenshot screenshot screenshot
screenshot screenshot screenshot screenshot
screenshot screenshot screenshot screenshot
screenshot screenshot screenshot screenshot

Quick start

Install the package from Pypi

pip install docling-parse

Convert a PDF (look in the visualize.py for a more detailed information)

from docling_core.types.doc.page import TextCellUnit
from docling_parse.pdf_parser import DoclingPdfParser, PdfDocument

parser = DoclingPdfParser()

pdf_doc: PdfDocument = parser.load(
    path_or_stream="<path-to-pdf>"
)

# PdfDocument.iterate_pages() will automatically populate pages as they are yielded.
for page_no, pred_page in pdf_doc.iterate_pages():

    # iterate over the word-cells
    for word in pred_page.iterate_cells(unit_type=TextCellUnit.WORD):
        print(word.rect, ": ", word.text)

        # create a PIL image with the char cells
    img = pred_page.render_as_image(cell_unit=TextCellUnit.CHAR)
    img.show()

Use the CLI

$ docling-parse -h
usage: docling-parse [-h] -p PDF

Process a PDF file.

options:
  -h, --help         show this help message and exit
  -p PDF, --pdf PDF  Path to the PDF file

Performance Benchmarks

Characteristics of different parser versions

Version Original Word-level Snippet-level Performance
V1 screenshot Not Supported v1 snippet ~0.250 sec/page
V2 v1 word v2 snippet ~0.050 sec/page

[~5-10X faster than v1]

Timings of different parser versions

We ran the v1 and v2 parser on DocLayNet. We found the following overall behavior

parser-performance

Development

CXX

To build the parse, simply run the following command in the root folder,

rm -rf build; cmake -B ./build; cd build; make

You can run the parser from your build folder. Example from parse_v1,

% ./parse_v1.exe -h
A program to process PDF files or configuration files
Usage:
  PDFProcessor [OPTION...]

  -i, --input arg          Input PDF file
  -c, --config arg         Config file
      --create-config arg  Create config file
  -o, --output arg         Output file
  -l, --loglevel arg       loglevel [error;warning;success;info]
  -h, --help               Print usage

Example from parse_v2,

% ./parse_v2.exe -h
program to process PDF files or configuration files
Usage:
  PDFProcessor [OPTION...]

  -i, --input arg          Input PDF file
  -c, --config arg         Config file
      --create-config arg  Create config file
  -p, --page arg           Pages to process (default: -1 for all) (default:
                           -1)
  -o, --output arg         Output file
  -l, --loglevel arg       loglevel [error;warning;success;info]
  -h, --help               Print usage

If you dont have an input file, then a template input file will be printed on the terminal.

Python

To build the package, simply run (make sure uv is installed),

uv sync

The latter will only work after a clean git clone. If you are developing and updating C++ code, please use,

# uv pip install --force-reinstall --no-deps -e .
rm -rf .venv; uv venv; uv pip install --force-reinstall --no-deps -e ".[perf-tools]"

To test the package, run:

uv run pytest ./tests -v -s

Contributing

Please read Contributing to Docling Parse for details.

References

If you use Docling in your projects, please consider citing the following:

@techreport{Docling,
  author = {Deep Search Team},
  month = {8},
  title = {Docling Technical Report},
  url = {https://arxiv.org/abs/2408.09869},
  eprint = {2408.09869},
  doi = {10.48550/arXiv.2408.09869},
  version = {1.0.0},
  year = {2024}
}

License

The Docling Parse codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.

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