docling-parse


Namedocling-parse JSON
Version 3.4.0 PyPI version JSON
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home_pageNone
SummarySimple package to extract text with coordinates from programmatic PDFs
upload_time2025-02-18 10:59:01
maintainerPeter Staar
docs_urlNone
authorPeter Staar
requires_python<4.0,>=3.9
licenseMIT
keywords docling pdf parser
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # Docling Parse

[![PyPI version](https://img.shields.io/pypi/v/docling-parse)](https://pypi.org/project/docling-parse/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/docling-parse)](https://pypi.org/project/docling-parse/)
[![Poetry](https://img.shields.io/endpoint?url=https://python-poetry.org/badge/v0.json)](https://python-poetry.org/)
[![Pybind11](https://img.shields.io/badge/build-pybind11-blue)](https://github.com/pybind/pybind11/)
[![Platforms](https://img.shields.io/badge/platform-macos%20|%20linux%20|%20windows-blue)](https://github.com/DS4SD/docling-parse/)
[![License MIT](https://img.shields.io/github/license/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. 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`),

```sh
poetry run python ./docling_parse/visualize.py -i <path-to-pdf-file> -c word --interactive
```

<table>
  <tr>
    <th>original</th>
    <th>char</th>
    <th>word</th>
    <th>line</th>
  </tr>
  <tr>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_1.orig.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_1.char.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_1.word.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_1.line.png" alt="screenshot" width="170"/></td>
  </tr>
  <tr>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_3.orig.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_3.char.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_3.word.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_3.line.png" alt="screenshot" width="170"/></td>
  </tr>
  <tr>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_4.orig.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_4.char.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_4.word.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/ligatures_01.pdf.page_4.line.png" alt="screenshot" width="170"/></td>
  </tr>
  <tr>
    <td><img src="./docs/visualisations/table_of_contents_01.pdf.page_1.orig.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/table_of_contents_01.pdf.page_1.char.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/table_of_contents_01.pdf.page_1.word.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/table_of_contents_01.pdf.page_1.line.png" alt="screenshot" width="170"/></td>
  </tr>
  <tr>
    <td><img src="./docs/visualisations/table_of_contents_01.pdf.page_4.orig.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/table_of_contents_01.pdf.page_4.char.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/table_of_contents_01.pdf.page_4.word.png" alt="screenshot" width="170"/></td>
    <td><img src="./docs/visualisations/table_of_contents_01.pdf.page_4.line.png" alt="screenshot" width="170"/></td>
  </tr>  
</table>

## Quick start

Install the package from Pypi

```sh
pip install docling-parse
```

Convert a PDF (look in the [visualize.py](docling_parse/visualize.py) for a more detailed information)

```python
from docling_parse.document import SegmentedPdfPageLabel
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.yield_cells(label=SegmentedPdfPageLabel.WORD):
        print(word.rect, ": ", word.text)    

    # create a PIL image with the char cells
    img = pred_page.render(label=SegmentedPdfPageLabel.CHAR)
    img.show()
```

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

### Characteristics of different parser versions

<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 sec/page </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 sec/page <br><br>[~5-10X faster than v1]</td>
  </tr>
</table>

### Timings of different parser versions

We ran the v1 and v2 parser on [DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet-v1.1). We found the following overall behavior

![parser-performance](./docs/dln-v1.png)

## 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.


            

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    "description": "# Docling Parse\n\n[![PyPI version](https://img.shields.io/pypi/v/docling-parse)](https://pypi.org/project/docling-parse/)\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/docling-parse)](https://pypi.org/project/docling-parse/)\n[![Poetry](https://img.shields.io/endpoint?url=https://python-poetry.org/badge/v0.json)](https://python-poetry.org/)\n[![Pybind11](https://img.shields.io/badge/build-pybind11-blue)](https://github.com/pybind/pybind11/)\n[![Platforms](https://img.shields.io/badge/platform-macos%20|%20linux%20|%20windows-blue)](https://github.com/DS4SD/docling-parse/)\n[![License MIT](https://img.shields.io/github/license/DS4SD/docling-parse)](https://opensource.org/licenses/MIT)\n\nSimple 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. 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.\n\nTo do the visualizations yourself, simply run (change `word` into `char` or `line`),\n\n```sh\npoetry run python ./docling_parse/visualize.py -i <path-to-pdf-file> -c word --interactive\n```\n\n<table>\n  <tr>\n    <th>original</th>\n    <th>char</th>\n    <th>word</th>\n    <th>line</th>\n  </tr>\n  <tr>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_1.orig.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_1.char.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_1.word.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_1.line.png\" alt=\"screenshot\" width=\"170\"/></td>\n  </tr>\n  <tr>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_3.orig.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_3.char.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_3.word.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_3.line.png\" alt=\"screenshot\" width=\"170\"/></td>\n  </tr>\n  <tr>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_4.orig.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_4.char.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_4.word.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/ligatures_01.pdf.page_4.line.png\" alt=\"screenshot\" width=\"170\"/></td>\n  </tr>\n  <tr>\n    <td><img src=\"./docs/visualisations/table_of_contents_01.pdf.page_1.orig.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/table_of_contents_01.pdf.page_1.char.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/table_of_contents_01.pdf.page_1.word.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/table_of_contents_01.pdf.page_1.line.png\" alt=\"screenshot\" width=\"170\"/></td>\n  </tr>\n  <tr>\n    <td><img src=\"./docs/visualisations/table_of_contents_01.pdf.page_4.orig.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/table_of_contents_01.pdf.page_4.char.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/table_of_contents_01.pdf.page_4.word.png\" alt=\"screenshot\" width=\"170\"/></td>\n    <td><img src=\"./docs/visualisations/table_of_contents_01.pdf.page_4.line.png\" alt=\"screenshot\" width=\"170\"/></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 [visualize.py](docling_parse/visualize.py) for a more detailed information)\n\n```python\nfrom docling_parse.document import SegmentedPdfPageLabel\nfrom docling_parse.pdf_parser import DoclingPdfParser, PdfDocument\n\nparser = DoclingPdfParser()\n\npdf_doc: PdfDocument = parser.load(\n    path_or_stream=\"<path-to-pdf>\"\n)\n\n# PdfDocument.iterate_pages() will automatically populate pages as they are yielded.\nfor page_no, pred_page in pdf_doc.iterate_pages():\n\n    # iterate over the word-cells\n    for word in pred_page.yield_cells(label=SegmentedPdfPageLabel.WORD):\n        print(word.rect, \": \", word.text)    \n\n    # create a PIL image with the char cells\n    img = pred_page.render(label=SegmentedPdfPageLabel.CHAR)\n    img.show()\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\n## Performance Benchmarks\n\n### Characteristics of different parser versions\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 sec/page </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 sec/page <br><br>[~5-10X faster than v1]</td>\n  </tr>\n</table>\n\n### Timings of different parser versions\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![parser-performance](./docs/dln-v1.png)\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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