Docling Parse

Simple package to extract text with coordinates from programmatic PDFs. This package is part of the Docling conversion.

Quick start

Install the package from Pypi

pip install docling-parse

Convert a PDF

from docling_parse.docling_parse import pdf_parser

# Do this only once to load fonts (avoid initialising it many times)
parser = pdf_parser()

# parser.set_loglevel(1) # 1=error, 2=warning, 3=success, 4=info

doc_file = "my-doc.pdf" # filename
doc_key = f"key={pdf_doc}" # unique document key (eg hash, UUID, etc)

# Load the document from file using filename doc_file. This only loads
# the QPDF document, but no extracted data
success = parser.load_document(doc_key, doc_file)

# Open the file in binary mode and read its contents
# with open(pdf_doc, "rb") as file:
#      file_content = file.read()

# Create a BytesIO object and write the file contents to it
# bytes_io = io.BytesIO(file_content)
# success = parser.load_document_from_bytesio(doc_key, bytes_io)

# Parse the entire document in one go, easier, but could require
# a lot (more) memory as parsing page-by-page
# json_doc = parser.parse_pdf_from_key(doc_key)	

# Get number of pages
num_pages = parser.number_of_pages(doc_key)

# Parse page by page to minimize memory footprint
for page in range(0, num_pages):

    # Internal memory for page is auto-deleted after this call.
    # No need to unload a specifc page 
    json_doc = parser.parse_pdf_from_key_on_page(doc_key, page)

    if "pages" not in json_doc:  # page could not get parsed
       continue

    # parsed page is the first one!				  
    json_page = json_doc["pages"][0] 
    
    page_dimensions = [json_page["dimensions"]["width"], json_page["dimensions"]["height"]]

    # find text cells
    cells=[]
    for cell_id,cell in enumerate(json_page["cells"]):
    	cells.append([page,
	              cell_id,
		      cell["content"]["rnormalized"], # text
	              cell["box"]["device"][0], # x0 (lower left x)
		      cell["box"]["device"][1], # y0 (lower left y)
		      cell["box"]["device"][2], # x1 (upper right x)
		      cell["box"]["device"][3], # y1 (upper right y)	
		      ])

    # find bitmap images
    images=[]
    for image_id,image in enumerate(json_page["images"]):
    	images.append([page,
	               image_id,
	               image["box"][0], # x0 (lower left x)
		       image["box"][1], # y0 (lower left y)
		       image["box"][2], # x1 (upper right x)
		       image["box"][3], # y1 (upper right y)
		       ])

    # find paths
    paths=[]
    for path_id,path in enumerate(json_page["paths"]):
    	paths.append([page,
	              path_id,
	              path["x-values"], # array of x values
	              path["y-values"], # array of y values
		      ])

# Unload the (QPDF) document and buffers
parser.unload_document(doc_key)

# Unloads everything at once
# parser.unload_documents()

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

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 with

./parse.exe <input-file> <optional-logging:true>

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 poetry is installed),

poetry build

To test the package, run,

poetry run pytest ./tests/test_parse.py

Contributing

Please read Contributing to Docling Parse for details.

References

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

@software{Docling,
author = {Deep Search Team},
month = {7},
title = {{Docling}},
url = {https://github.com/DS4SD/docling},
version = {main},
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.