edspdf

KO | EN

gitlite — search

#python #hacktoberfest #react #golang #go #linux #docker #macos #windows #kubernetes #awesome-list #ios #mcp #openai

(★ 65)

EDS-PDF is a generic, pure-Python framework for text extraction from PDF documents. It provides the machinery to use rule- or machine-learning-based approaches to classify text blocs between body and meta-data.

edspdf Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

![Tests](https://img.shields.io/github/actions/workflow/status/aphp/edspdf/tests.yml?branch=main&label=tests&style=flat-square)
[![Documentation](https://img.shields.io/github/actions/workflow/status/aphp/edspdf/documentation.yml?branch=main&label=docs&style=flat-square)](https://aphp.github.io/edspdf/latest/)
[![PyPI](https://img.shields.io/pypi/v/edspdf?color=blue&style=flat-square)](https://pypi.org/project/edspdf/)
[![Coverage](https://raw.githubusercontent.com/aphp/edspdf/coverage/coverage.svg)](https://raw.githubusercontent.com/aphp/edspdf/coverage/coverage.txt)
[![DOI](https://zenodo.org/badge/517726737.svg)](https://zenodo.org/badge/latestdoi/517726737)

# EDS-PDF

EDS-PDF provides a modular framework to extract text information from PDF documents.

You can use it out-of-the-box, or extend it to fit your specific use case. We provide a pipeline system and various utilities for visualizing and processing PDFs, as well as multiple components to build complex models:complex models:
- 📄 [Extractors](https://aphp.github.io/edspdf/latest/pipes/extractors) to parse PDFs (based on [pdfminer](https://github.com/euske/pdfminer), [mupdf](https://github.com/aphp/edspdf-mupdf) or [poppler](https://github.com/aphp/edspdf-poppler))
- 🎯 [Classifiers](https://aphp.github.io/edspdf/latest/pipes/box-classifiers) to perform text box classification, in order to segment PDFs
- 🧩 [Aggregators](https://aphp.github.io/edspdf/latest/pipes/aggregators) to produce an aggregated output from the detected text boxes
- 🧠 Trainable layers to incorporate machine learning in your pipeline (e.g., [embedding](https://aphp.github.io/edspdf/latest/pipes/embeddings) building blocks or a [trainable classifier](https://aphp.github.io/edspdf/latest/pipes/box-classifiers/trainable/))

Visit the [:book: documentation](https://aphp.github.io/edspdf/) for more information!

## Getting started

### Installation

Install the library with pip:

```bash
pip install edspdf
```

### Extracting text

Let's build a simple PDF extractor that uses a rule-based classifier. There are two
ways to do this, either by using the [configuration system](#configuration) or by using
the pipeline API.

Create a configuration file:

<h5 a><strong><code>config.cfg</code></strong></h5>

```ini
[pipeline]
pipeline = ["extractor", "classifier", "aggregator"]

[components.extractor]
@factory = "pdfminer-extractor"

[components.classifier]
@factory = "mask-classifier"
x0 = 0.2
x1 = 0.9
y0 = 0.3
y1 = 0.6
threshold = 0.1

[components.aggregator]
@factory = "simple-aggregator"
```

and load it from Python:

```python
import edspdf
from pathlib import Path

model = edspdf.load("config.cfg")  # (1)
```

Or create a pipeline directly from Python:

```python
from edspdf import Pipeline

model = Pipeline()
model.add_pipe("pdfminer-extractor")
model.add_pipe(
    "mask-classifier",
    config=dict(
        x0=0.2,
        x1=0.9,
        y0=0.3,
        y1=0.6,
        threshold=0.1,
    ),
)
model.add_pipe("simple-aggregator")
```

This pipeline can then be applied (for instance with this [PDF](https://github.com/aphp/edspdf/raw/main/tests/resources/letter.pdf)):

```python
# Get a PDF
pdf = Path("/Users/perceval/Development/edspdf/tests/resources/letter.pdf").read_bytes()
pdf = model(pdf)

body = pdf.aggregated_texts["body"]

text, style = body.text, body.properties
```

See the [rule-based recipe](https://aphp.github.io/edspdf/latest/recipes/rule-based) for a step-by-step explanation of what is happening.

## Citation

If you use EDS-PDF, please cite us as below.

```bibtex
@software{edspdf,
  author  = {Dura, Basile and Wajsburt, Perceval and Calliger, Alice and Gérardin, Christel and Bey, Romain},
  doi     = {10.5281/zenodo.6902977},
  license = {BSD-3-Clause},
  title   = {{EDS-PDF: Smart text extraction from PDF documents}},
  url     = {https://github.com/aphp/edspdf}
}
```

## Acknowledgement

We would like to thank [Assistance Publique – Hôpitaux de Paris](https://www.aphp.fr/) and
[AP-HP Foundation](https://fondationrechercheaphp.fr/) for funding this project.

edspdf

edspdf Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

edspdf

edspdf Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README