https://github.com/docling-project/docling-parse
Simple package to extract text with coordinates from programmatic PDFs
https://github.com/docling-project/docling-parse
Last synced: 4 months ago
JSON representation
Simple package to extract text with coordinates from programmatic PDFs
- Host: GitHub
- URL: https://github.com/docling-project/docling-parse
- Owner: docling-project
- License: mit
- Created: 2024-08-06T07:55:41.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2026-02-20T06:56:17.000Z (4 months ago)
- Last Synced: 2026-02-20T11:24:16.287Z (4 months ago)
- Language: C++
- Homepage:
- Size: 185 MB
- Stars: 239
- Watchers: 4
- Forks: 53
- Open Issues: 53
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Maintainers: MAINTAINERS.md
Awesome Lists containing this project
README
# Docling Parse
[](https://pypi.org/project/docling-parse/)
[](https://pypi.org/project/docling-parse/)
[](https://github.com/astral-sh/uv)
[](https://github.com/pybind/pybind11/)
[](https://github.com/docling-project/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/docling-project/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
uv run python ./docling_parse/visualize.py -i -c word --interactive
```
original
char
word
line
## 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_core.types.doc.page import TextCellUnit
from docling_parse.pdf_parser import DoclingPdfParser, PdfDocument
parser = DoclingPdfParser()
pdf_doc: PdfDocument = parser.load(
path_or_stream=""
)
# 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
```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
*Coming soon - benchmarks will be updated for the current parser version.*
For historical V1 vs V2 benchmarks, see [legacy_performance_benchmarks.md](./docs/legacy_performance_benchmarks.md).
## Development
### CXX
To build the parser, 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:
```sh
% ./parse.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)
--password arg Password for accessing encrypted, password-protected files
-o, --output arg Output file
-l, --loglevel arg loglevel [error;warning;success;info]
-h, --help Print usage
```
If you don't have an input file, a template input file will be printed on the terminal.
### Python
To build the package, simply run (make sure [uv](https://docs.astral.sh/uv/) is [installed](https://docs.astral.sh/uv/getting-started/installation)),
```sh
uv sync
```
The latter will only work after a clean `git clone`. If you are developing and updating C++ code, please use,
```sh
# 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:
```sh
uv run pytest ./tests -v -s
```
## Contributing
Please read [Contributing to Docling Parse](https://github.com/docling-project/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 = {Docling 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.
## LF AI & Data
Docling (and also docling-parse) is hosted as a project in the [LF AI & Data Foundation](https://lfaidata.foundation/projects/).
### IBM ❤️ Open Source AI
The project was started by the AI for knowledge team at IBM Research Zurich.