Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
📚 Process PDFs, Word documents and more with spaCy
| Date | Stars |
|---|---|
| 2026-07-24 | 909 |
| 2026-07-25 | 909 |
| 2026-07-28 | 909 |
| 2026-07-30 | 909 |
| 2026-08-06 | 909 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
# spaCy Layout: Process PDFs, Word documents and more with spaCy
This plugin integrates with [Docling](https://ds4sd.github.io/docling/) to bring structured processing of **PDFs**, **Word documents** and other input formats to your [spaCy](https://spacy.io) pipeline. It outputs clean, **structured data** in a text-based format and creates spaCy's familiar [`Doc`](https://spacy.io/api/doc) objects that let you access labelled text spans like sections or headings, and tables with their data converted to a `pandas.DataFrame`.
This workflow makes it easy to apply powerful **NLP techniques** to your documents, including linguistic analysis, named entity recognition, text classification and more. It's also great for implementing **chunking for RAG** pipelines.
> 📖 **Blog post:** ["From PDFs to AI-ready structured data: a deep dive"
](https://explosion.ai/blog/pdfs-nlp-structured-data) – A new modular workflow for converting PDFs and similar documents to structured data, featuring `spacy-layout` and Docling.
[](https://github.com/explosion/spacy-layout/actions/workflows/test.yml)
[](https://github.com/explosion/spacy-layout/releases)
[](https://pypi.org/project/spacy-layout/)
[](https://spacy.io)
## 📝 Usage
> ⚠️ This package requires **Python 3.10** or above.
```bash
pip install spacy-layout
```
After initializing the `spaCyLayout` preprocessor with an `nlp` object for tokenization, you can call it on a document path to convert it to structured data. The resulting `Doc` object includes layout spans that map into the original raw text and expose various attributes, including the content type and layout features.
```python
import spacy
from spacy_layout import spaCyLayout
nlp = spacy.blank("en")
layout = spaCyLayout(nlp)
# Process a document and create a spaCy Doc object
doc = layout("./starcraft.pdf")
# The text-based contents of the document
print(doc.text)
# Document layout including pages and page sizes
print(doc._.layout)
# Tables in the document and their extracted data
print(doc._.tables)
# Markdown representation of the document
print(doc._.markdown)
# Layout spans for different sections
for span in doc.spans["layout"]:
# Document section and token and character offsets into the text
print(span.text, span.start, span.end, span.start_char, span.end_char)
# Section type, e.g. "text", "title", "section_header" etc.
print(span.label_)
# Layout features of the section, including bounding box
print(span._.layout)
# Closest heading to the span (accuracy depends on document structure)
print(span._.heading)
```
If you need to process larger volumes of documents at scale, you can use the `spaCyLayout.pipe` method, which takes an iterable of paths or bytes instead and yields `Doc` objects:
```python
paths = ["one.pdf", "two.pdf", "three.pdf", ...]
for doc in layout.pipe(paths):
print(doc._.layout)
```
spaCy also allows you to call the `nlp` object on an already created `Doc`, so you can easily apply a pipeline of components for [linguistic analysis](https://spacy.io/usage/linguistic-features) or [named entity recognition](https://spacy.io/usage/linguistic-features#named-entities), use [rule-based matching](https://spacy.io/usage/rule-based-matching) or anything else you can do with spaCy.
```python
# Load the transformer-based English pipeline
# Installation: python -m spacy download en_core_web_trf
nlp = spacy.load("en_core_web_trf")Excerpt of 12,905 characters
Read on GitHub
Ines Montani · Founder @explosion · Germany
81
Matthew Honnibal · Founder @explosion · Germany
3
Magdalena Aniol
2
Sofie Van Landeghem · OxyKodit · Belgium
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a95f7a62e1015ab6, topic:nlp, topic:natural-language-processing, topic:spacy