spacy-layout

spaCy Layout: Process PDFs, Word documents and more with spaCy

This plugin integrates with Docling to bring structured processing of PDFs, Word documents and other input formats to your spaCy pipeline. It outputs clean, structured data in a text-based format and creates spaCy's familiar 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" – A new modular workflow for converting PDFs and similar documents to structured data, featuring spacy-layout and Docling.

📝 Usage

⚠️ This package requires Python 3.10 or above.

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.

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:

paths = ["one.pdf", "two.pdf", "three.pdf", ...]
for doc in layout.pipe(paths):
    print(doc._.layout)

After you've processed the documents, you can serialize the structured Doc objects in spaCy's efficient binary format, so you don't have to re-run the resource-intensive conversion.

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 or named entity recognition, use rule-based matching or anything else you can do with spaCy.

# Load the transformer-based English pipeline
# Installation: python -m spacy download en_core_web_trf
nlp = spacy.load("en_core_web_trf")
layout = spaCyLayout(nlp)

doc = layout("./starcraft.pdf")
# Apply the pipeline to access POS tags, dependencies, entities etc.
doc = nlp(doc)

Tables and tabular data

Tables are included in the layout spans with the label "table" and under the shortcut Doc._.tables. They expose a layout extension attribute, as well as an attribute data, which includes the tabular data converted to a pandas.DataFrame.

for table in doc._.tables:
    # Token position and bounding box
    print(table.start, table.end, table._.layout)
    # pandas.DataFrame of contents
    print(table._.data)

By default, the span text is a placeholder TABLE, but you can customize how a table is rendered by providing a display_table callback to spaCyLayout, which receives the pandas.DataFrame of the data. This allows you to include the table figures in the document text and use them later on, e.g. during information extraction with a trained named entity recognizer or text classifier.

def display_table(df: pd.DataFrame) -> str:
    return f"Table with columns: {', '.join(df.columns.tolist())}"

layout = spaCyLayout(nlp, display_table=display_table)

🎛️ API

Data and extension attributes

layout = spaCyLayout(nlp)
doc = layout("./starcraft.pdf")
print(doc._.layout)
for span in doc.spans["layout"]:
    print(span.label_, span._.layout)

Attribute	Type	Description
Doc._.layout	DocLayout	Layout features of the document.
Doc._.pages	list[tuple[PageLayout, list[Span]]]	Pages in the document and the spans they contain.
Doc._.tables	list[Span]	All tables in the document.
Doc._.markdown	str	Markdown representation of the document.
Doc.spans["layout"]	spacy.tokens.SpanGroup	The layout spans in the document.
Span.label_	str	The type of the extracted layout span, e.g. "text" or "section_header". See here for options.
Span.label	int	The integer ID of the span label.
Span.id	int	Running index of layout span.
Span._.layout	SpanLayout | None	Layout features of a layout span.
Span._.heading	Span | None	Closest heading to a span, if available.
Span._.data	pandas.DataFrame | None	The extracted data for table spans.

dataclass PageLayout

Attribute	Type	Description
page_no	int	The page number (1-indexed).
width	float	Page with in pixels.
height	float	Page height in pixels.

dataclass DocLayout

Attribute	Type	Description
pages	list[PageLayout]	The pages in the document.

dataclass SpanLayout

Attribute	Type	Description
x	float	Horizontal offset of the bounding box in pixels.
y	float	Vertical offset of the bounding box in pixels.
width	float	Width of the bounding box in pixels.
height	float	Height of the bounding box in pixels.
page_no	int	Number of page the span is on.

class spaCyLayout

method spaCyLayout.__init__

Initialize the document processor.

nlp = spacy.blank("en")
layout = spaCyLayout(nlp)

Argument	Type	Description
nlp	spacy.language.Language	The initialized nlp object to use for tokenization.
separator	str	Token used to separate sections in the created Doc object. The separator won't be part of the layout span. If None, no separator will be added. Defaults to "\n\n".
attrs	dict[str, str]	Override the custom spaCy attributes. Can include "doc_layout", "doc_pages", "doc_tables", "doc_markdown", "span_layout", "span_data", "span_heading" and "span_group".
headings	list[str]	Labels of headings to consider for Span._.heading detection. Defaults to ["section_header", "page_header", "title"].
display_table	Callable[[pandas.DataFrame], str] | str	Function to generate the text-based representation of the table in the Doc.text or placeholder text. Defaults to "TABLE".
docling_options	dict[InputFormat, FormatOption]	Format options passed to Docling's DocumentConverter.
RETURNS	spaCyLayout	The initialized object.

method spaCyLayout.__call__

Process a document and create a spaCy Doc object containing the text content and layout spans, available via Doc.spans["layout"] by default.

layout = spaCyLayout(nlp)
doc = layout("./starcraft.pdf")

Argument	Type	Description
source	str | Path | bytes | DoclingDocument	Path of document to process, bytes or already created DoclingDocument.
RETURNS	Doc	The processed spaCy Doc object.

method spaCyLayout.pipe

Process multiple documents and create spaCy Doc objects. You should use this method if you're processing larger volumes of documents at scale.

layout = spaCyLayout(nlp)
paths = ["one.pdf", "two.pdf", "three.pdf", ...]
docs = layout.pipe(paths)

Argument	Type	Description
sources	Iterable[str | Path | bytes]	Paths of documents to process or bytes.
YIELDS	Doc	The processed spaCy Doc object.