# Docling Parse
[](https://pypi.org/project/docling-parse/)
[](https://pypi.org/project/docling-parse/)
[](https://python-poetry.org/)
[](https://github.com/pybind/pybind11/)
[](https://github.com/DS4SD/docling-parse/)
[](https://opensource.org/licenses/MIT)
Simple package to extract text, paths and bitmap images with coordinates from programmatic PDFs.
This package is used in the [Docling](https://github.com/DS4SD/docling) PDF conversion.
<table>
<tr>
<th>Version</th>
<th>Original</th>
<th>Word-level</th>
<th>Snippet-level</th>
<th>Performance</th>
</tr>
<tr>
<th>V1</th>
<td rowspan="2"><img src="./docs/example_visualisations/2305.14962v1.pdf_page=0.png" alt="screenshot" width="100"/></td>
<td>Not Supported</td>
<td><img src="./docs/example_visualisations/2305.14962v1.pdf_page=0.v1.png" alt="v1 snippet" width="100"/></td>
<td>~0.250 page/sec</td>
</tr>
<tr>
<th>V2</th>
<!-- The "Original" column image spans from the previous row -->
<td><img src="./docs/example_visualisations/2305.14962v1.pdf_page=0.v2.original.png" alt="v1 word" width="100"/></td>
<td><img src="./docs/example_visualisations/2305.14962v1.pdf_page=0.v2.sanitized.png" alt="v2 snippet" width="100"/></td>
<td>~0.050 page/sec <br><br>[~5-10X faster than v1]</td>
</tr>
</table>
## Quick start
Install the package from Pypi
```sh
pip install docling-parse
```
Convert a PDF (look in the [visualise.py](docling_parse/visualise.py) for a more detailed information)
```python
from docling_parse.docling_parse import pdf_parser_v2
# Do this only once to load fonts (avoid initialising it many times)
parser = pdf_parser_v2("error") # info, warning, error, fatal
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]
# <Insert your own code>
# Unload the (QPDF) document and buffers
parser.unload_document(doc_key)
# Unloads everything at once
# parser.unload_documents()
```
Use the CLI
```sh
$ 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
We ran the v1 and v2 parser on [DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet-v1.1). We found the following overall behavior
## Development
### CXX
To build the parse, simply run the following command in the root folder,
```sh
rm -rf build; cmake -B ./build; cd build; make
```
You can run the parser from your build folder. Example from parse_v1,
```sh
% ./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,
```sh
% ./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 [poetry](https://python-poetry.org/) is [installed](https://python-poetry.org/docs/#installing-with-the-official-installer)),
```
poetry install
```
To test the package, run:
```
poetry run pytest ./tests -v -s
```
## Contributing
Please read [Contributing to Docling Parse](https://github.com/DS4SD/docling-parse/blob/main/CONTRIBUTING.md) for details.
## References
If you use Docling in your projects, please consider citing the following:
```bib
@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.
Raw data
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"description": "# Docling Parse\n\n[](https://pypi.org/project/docling-parse/)\n[](https://pypi.org/project/docling-parse/)\n[](https://python-poetry.org/)\n[](https://github.com/pybind/pybind11/)\n[](https://github.com/DS4SD/docling-parse/)\n[](https://opensource.org/licenses/MIT)\n\nSimple package to extract text, paths and bitmap images with coordinates from programmatic PDFs.\nThis package is used in the [Docling](https://github.com/DS4SD/docling) PDF conversion.\n\n<table>\n <tr>\n <th>Version</th>\n <th>Original</th>\n <th>Word-level</th>\n <th>Snippet-level</th>\n <th>Performance</th>\n </tr>\n <tr>\n <th>V1</th>\n <td rowspan=\"2\"><img src=\"./docs/example_visualisations/2305.14962v1.pdf_page=0.png\" alt=\"screenshot\" width=\"100\"/></td>\n <td>Not Supported</td>\n <td><img src=\"./docs/example_visualisations/2305.14962v1.pdf_page=0.v1.png\" alt=\"v1 snippet\" width=\"100\"/></td>\n <td>~0.250 page/sec</td>\n </tr>\n <tr>\n <th>V2</th>\n <!-- The \"Original\" column image spans from the previous row -->\n <td><img src=\"./docs/example_visualisations/2305.14962v1.pdf_page=0.v2.original.png\" alt=\"v1 word\" width=\"100\"/></td>\n <td><img src=\"./docs/example_visualisations/2305.14962v1.pdf_page=0.v2.sanitized.png\" alt=\"v2 snippet\" width=\"100\"/></td>\n <td>~0.050 page/sec <br><br>[~5-10X faster than v1]</td>\n </tr>\n</table>\n\n## Quick start\n\nInstall the package from Pypi\n\n```sh\npip install docling-parse\n```\n\nConvert a PDF (look in the [visualise.py](docling_parse/visualise.py) for a more detailed information)\n\n```python\nfrom docling_parse.docling_parse import pdf_parser_v2\n\n# Do this only once to load fonts (avoid initialising it many times)\nparser = pdf_parser_v2(\"error\") # info, warning, error, fatal\n\ndoc_file = \"my-doc.pdf\" # filename\ndoc_key = f\"key={pdf_doc}\" # unique document key (eg hash, UUID, etc)\n\n# Load the document from file using filename doc_file. This only loads\n# the QPDF document, but no extracted data\nsuccess = parser.load_document(doc_key, doc_file)\n\n# Open the file in binary mode and read its contents\n# with open(pdf_doc, \"rb\") as file:\n# file_content = file.read()\n\n# Create a BytesIO object and write the file contents to it\n# bytes_io = io.BytesIO(file_content)\n# success = parser.load_document_from_bytesio(doc_key, bytes_io)\n\n# Parse the entire document in one go, easier, but could require\n# a lot (more) memory as parsing page-by-page\n# json_doc = parser.parse_pdf_from_key(doc_key)\t\n\n# Get number of pages\nnum_pages = parser.number_of_pages(doc_key)\n\n# Parse page by page to minimize memory footprint\nfor page in range(0, num_pages):\n\n # Internal memory for page is auto-deleted after this call.\n # No need to unload a specifc page \n json_doc = parser.parse_pdf_from_key_on_page(doc_key, page)\n\n if \"pages\" not in json_doc: # page could not get parsed\n continue\n\n # parsed page is the first one!\t\t\t\t \n json_page = json_doc[\"pages\"][0] \n \n\t# <Insert your own code>\n\n# Unload the (QPDF) document and buffers\nparser.unload_document(doc_key)\n\n# Unloads everything at once\n# parser.unload_documents()\n```\n\nUse the CLI\n\n```sh\n$ docling-parse -h\nusage: docling-parse [-h] -p PDF\n\nProcess a PDF file.\n\noptions:\n -h, --help show this help message and exit\n -p PDF, --pdf PDF Path to the PDF file\n```\n\n## Performance Benchmarks\n\nWe ran the v1 and v2 parser on [DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet-v1.1). We found the following overall behavior\n\n\n\n## Development\n\n### CXX\n\nTo build the parse, simply run the following command in the root folder,\n\n```sh\nrm -rf build; cmake -B ./build; cd build; make\n```\n\nYou can run the parser from your build folder. Example from parse_v1,\n\n```sh\n% ./parse_v1.exe -h\nA program to process PDF files or configuration files\nUsage:\n PDFProcessor [OPTION...]\n\n -i, --input arg Input PDF file\n -c, --config arg Config file\n --create-config arg Create config file\n -o, --output arg Output file\n -l, --loglevel arg loglevel [error;warning;success;info]\n -h, --help Print usage\n```\n\nExample from parse_v2,\n\n```sh\n% ./parse_v2.exe -h\nprogram to process PDF files or configuration files\nUsage:\n PDFProcessor [OPTION...]\n\n -i, --input arg Input PDF file\n -c, --config arg Config file\n --create-config arg Create config file\n -p, --page arg Pages to process (default: -1 for all) (default:\n -1)\n -o, --output arg Output file\n -l, --loglevel arg loglevel [error;warning;success;info]\n -h, --help Print usage\n```\n\nIf you dont have an input file, then a template input file will be printed on the terminal.\n\n\n### Python\n\nTo build the package, simply run (make sure [poetry](https://python-poetry.org/) is [installed](https://python-poetry.org/docs/#installing-with-the-official-installer)),\n\n```\npoetry install\n```\n\nTo test the package, run:\n\n```\npoetry run pytest ./tests -v -s\n```\n\n\n## Contributing\n\nPlease read [Contributing to Docling Parse](https://github.com/DS4SD/docling-parse/blob/main/CONTRIBUTING.md) for details.\n\n\n## References\n\nIf you use Docling in your projects, please consider citing the following:\n\n```bib\n@techreport{Docling,\n author = {Deep Search Team},\n month = {8},\n title = {Docling Technical Report},\n url = {https://arxiv.org/abs/2408.09869},\n eprint = {2408.09869},\n doi = {10.48550/arXiv.2408.09869},\n version = {1.0.0},\n year = {2024}\n}\n```\n\n## License\n\nThe Docling Parse codebase is under MIT license.\nFor individual model usage, please refer to the model licenses found in the original packages.\n\n",
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