spaCyLayout and PDF Extraction

Key Features of spaCyLayout

Multi-format Support

Process PDFs, Word documents, and other formats seamlessly, offering flexibility for diverse document types.

Structured Output

Extracts clean, structured data in text-based formats, simplifying subsequent analysis.

Integration with spaCy

Creates spaCy Doc objects with labeled spans and tables for seamless integration into spaCy workflows.

Chunking Support

Supports text chunking, useful for applications like Retrieval-Augmented Generation (RAG) pipelines.

import spacy

from spacy_layout import spaCyLayout

nlp = spacy.load("en_core_web_sm")

layout = spaCyLayout(nlp)

# Assuming you have a PDF file named 'document.pdf'

doc = layout("document.pdf")

# Extract the full text

print(doc.text)

# Extract tables as DataFrames

for table in doc._.tables:

    print(f"Table {table.i}:")

    print(table._.data)

    print("\n")

# Access layout spans with labels and attributes

for span in doc.spans["layout"]:

    print(f"Span type: {span.label_}, Text: {span.text}")

Advanced Features of spaCyLayout

Customizable Table Rendering

Customize table rendering with the display_table callback function.

Hierarchical Section Detection

Detect and organize sections using headings for improved structure.

Multi-page Document Support

Seamlessly handle multi-page documents without losing context.

Pipeline Integration

Combine spaCyLayout with spaCy’s other NLP components for enhanced processing.

Best Practices

Preprocessing

Remove unnecessary elements (e.g., headers, footers, page numbers) for cleaner output.

Model Fine-Tuning

Fine-tune spaCy models for domain-specific documents to improve accuracy.

Error Handling

Handle unexpected PDF structures gracefully to avoid processing failures.

Optimized Chunking

Experiment with chunking strategies for the right balance of detail and coherence.