Docling Advanced Features

Overview

This skill covers advanced Docling features for handling complex documents, optimizing performance, and using the Granite vision-language model for enhanced document understanding.

Advanced topics:

• Granite model for scanned PDFs and complex layouts
• Advanced configuration options
• Performance optimization techniques
• Complex document handling (tables, formulas, multi-column)
• Troubleshooting challenging scenarios

Granite Model

Granite Docling is a vision-language model (258M parameters) that enhances document processing for scanned PDFs and complex layouts.

What is Granite

Granite Docling:

• AI model for document understanding
• Processes scanned documents with OCR
• Better table extraction than standard parsing
• Handles multi-column layouts
• Understands document structure visually

When to use Granite:

• Scanned PDFs (not born-digital)
• Poor-quality documents
• Complex layouts (multi-column, unusual formatting)
• Tables with complex structures
• Documents with mixed content (text + images + formulas)

When NOT to use Granite:

• Clean, born-digital PDFs (standard parser is faster)
• Simple documents
• Performance is critical (Granite is slower)

Installation

# Install with Granite support
uv add "docling[granite]"

# Verify
python -c "from docling.models import GraniteModel; print('Granite available')"

System requirements:

• Additional ~250MB for model weights
• More RAM (8GB+ recommended)
• Slower processing (~3x standard)

Usage

from docling.document_converter import DocumentConverter
from docling.datamodel.pipeline_options import PipelineOptions

# Configure with Granite
options = PipelineOptions(
    do_ocr=True,                    # Enable OCR
    ocr_engine="granite"            # Use Granite model
)

converter = DocumentConverter(options=options)
result = converter.convert("scanned_document.pdf")

# Process as usual
markdown = result.document.export_to_markdown()

Granite vs Standard OCR

Aspect	Granite	Tesseract (Standard)
Accuracy	Higher	Good
Table extraction	Excellent	Basic
Layout understanding	Advanced	Basic
Speed	Slower (~90s/100pg)	Faster (~60s/100pg)
Model size	~250MB	~5MB
Use case	Complex documents	Simple scans

For detailed Granite usage, see references/granite-guide.md.

Advanced Configuration

Pipeline Options

Full configuration control:

from docling.document_converter import DocumentConverter
from docling.datamodel.pipeline_options import PipelineOptions, TableFormatOption

options = PipelineOptions(
    do_table_structure=True,        # Extract table structure
    do_ocr=False,                   # Disable OCR (if not needed)
    do_picture_classifier=False,    # Disable image classification
    table_format=TableFormatOption.MARKDOWN,  # Table format
)

converter = DocumentConverter(options=options)

Key options:

• do_table_structure (bool): Extract table structure (default: True)

  • True: Full table parsing with cells and structure
  • False: Tables as plain text (faster)

• do_ocr (bool): Enable OCR for scanned documents (default: False)

  • True: Process scanned PDFs
  • False: Skip OCR (faster for born-digital)

• do_picture_classifier (bool): Classify images (default: False)

  • True: Detect image types (charts, photos, diagrams)
  • False: Skip classification (faster)

• table_format: How to represent tables

  • TableFormatOption.MARKDOWN: Markdown tables (readable)
  • TableFormatOption.HTML: HTML tables (structured)
  • TableFormatOption.TEXT: Plain text (simple)

Performance Optimization

Disable unused features:

# For maximum speed (text-only extraction)
options = PipelineOptions(
    do_table_structure=False,
    do_ocr=False,
    do_picture_classifier=False
)

# For OCR documents only
options = PipelineOptions(
    do_ocr=True,
    do_table_structure=False,
    do_picture_classifier=False
)

Batch processing optimization:

from concurrent.futures import ThreadPoolExecutor
from pathlib import Path

converter = DocumentConverter(options)  # Reuse converter

def process_pdf(pdf_path):
    """Process single PDF."""
    return converter.convert(str(pdf_path))

# Process in parallel
with ThreadPoolExecutor(max_workers=4) as executor:
    pdf_files = list(Path("data/").glob("*.pdf"))
    results = list(executor.map(process_pdf, pdf_files))

Memory optimization:

# Process one at a time for large PDFs
for pdf in large_pdfs:
    result = converter.convert(pdf)
    # Save result immediately
    save_to_file(result)
    # Clear memory
    del result

For complete optimization guide, see references/performance-optimization.md.

Complex Document Handling

Multi-Column Layouts

Docling automatically detects multi-column layouts:

from docling.document_converter import DocumentConverter

converter = DocumentConverter()
result = converter.convert("multi_column_paper.pdf")

# Docling preserves reading order across columns
markdown = result.document.export_to_markdown()

Reading order: Docling respects logical reading order, not visual layout.

Table Extraction

Advanced table handling:

from docling.document_converter import DocumentConverter
from docling.datamodel.pipeline_options import PipelineOptions, TableFormatOption

# Configure for best table extraction
options = PipelineOptions(
    do_table_structure=True,
    table_format=TableFormatOption.MARKDOWN
)

converter = DocumentConverter(options)
result = converter.convert("document_with_tables.pdf")

# Extract tables specifically
for item in result.document.iterate_items():
    if item.label == "table":
        print(f"Table on page {item.prov[0].page_no if item.prov else 'N/A'}:")
        print(item.text)

Complex tables:

• Merged cells: Handled automatically
• Nested tables: Flattened or preserved (depends on structure)
• Multi-page tables: Split by page

Formulas and Mathematical Content

Docling attempts to extract mathematical formulas:

# Extract formulas
for item in result.document.iterate_items():
    if item.label == "formula":
        print(f"Formula: {item.text}")

Note: Formula extraction quality varies. For complex math, consider specialized tools (MathPix, LaTeX-OCR).

Images and Figures

Handle images in documents:

options = PipelineOptions(
    do_picture_classifier=True  # Enable image classification
)

converter = DocumentConverter(options)
result = converter.convert("document.pdf")

# Find figures
for item in result.document.iterate_items():
    if item.label == "figure":
        print(f"Figure on page {item.prov[0].page_no if item.prov else 'N/A'}")

        # Find associated caption
        if hasattr(item, 'parent') and item.parent:
            for sibling in item.parent.children:
                if sibling.label == "caption":
                    print(f"Caption: {sibling.text}")

For detailed complex document patterns, see references/complex-documents.md.

Troubleshooting Advanced Scenarios

Issue: Garbled Text from Scanned PDF

Cause: OCR disabled or poor scan quality

Solution:

# Enable Granite for better OCR
options = PipelineOptions(
    do_ocr=True,
    ocr_engine="granite"
)
converter = DocumentConverter(options)

Issue: Tables Not Extracted Correctly

Cause: Complex table structure or table detection disabled

Solution:

# Enable full table processing
options = PipelineOptions(
    do_table_structure=True,
    table_format=TableFormatOption.HTML  # Try different format
)

# For very complex tables, consider Granite
options.ocr_engine = "granite"

Issue: Slow Processing

Cause: Unnecessary features enabled

Solution:

# Disable unused features
options = PipelineOptions(
    do_ocr=False,                    # If not scanned
    do_picture_classifier=False,     # If images not important
    do_table_structure=False         # If tables not important
)

# Process in parallel
from concurrent.futures import ThreadPoolExecutor
with ThreadPoolExecutor(max_workers=4) as executor:
    results = executor.map(process_pdf, pdf_files)

Issue: Out of Memory

Cause: Large PDFs or batch processing

Solution:

# Process one at a time
for pdf in pdfs:
    result = converter.convert(pdf)
    save_result(result)
    del result  # Free memory

# Or reduce batch size
# Or increase system RAM

For comprehensive troubleshooting, see references/troubleshooting.md.

Use Case Examples

Use Case 1: Scanned Invoice Processing

from docling.document_converter import DocumentConverter
from docling.datamodel.pipeline_options import PipelineOptions
from docling.chunking import HybridChunker

# Configure for scanned invoices
options = PipelineOptions(
    do_ocr=True,
    ocr_engine="granite",
    do_table_structure=True  # Invoices often have tables
)

converter = DocumentConverter(options)
chunker = HybridChunker()

# Process invoice
result = converter.convert("scanned_invoice.pdf")
chunks = list(chunker.chunk(result.document))

# Extract with metadata
for chunk in chunks:
    if chunk.meta.doc_items and chunk.meta.doc_items[0].label == "table":
        print("Found invoice table:")
        print(chunk.text)

Use Case 2: Research Paper with Complex Layout

# Multi-column paper with tables and formulas
options = PipelineOptions(
    do_table_structure=True,
    do_picture_classifier=True,  # Classify figures
    table_format=TableFormatOption.MARKDOWN
)

converter = DocumentConverter(options)
result = converter.convert("research_paper.pdf")

# Docling handles multi-column automatically
# Extract in logical reading order
from docling.chunking import HierarchicalChunker
chunker = HierarchicalChunker()
chunks = list(chunker.chunk(result.document))

# Chunks follow paper structure (Abstract, Intro, Methods, etc.)

Use Case 3: Mixed Quality Document Set

# Some scanned, some born-digital
def process_adaptive(pdf_path):
    """Adaptively process based on document quality."""

    # Try without OCR first (faster)
    converter = DocumentConverter()

    try:
        result = converter.convert(pdf_path)

        # Check if extraction looks good
        text = result.document.export_to_markdown()

        if len(text) < 100 or text.count('�') > 5:
            # Likely scanned or poor quality, retry with Granite
            print(f"Retrying {pdf_path} with Granite...")
            options = PipelineOptions(do_ocr=True, ocr_engine="granite")
            converter_ocr = DocumentConverter(options)
            result = converter_ocr.convert(pdf_path)

        return result

    except Exception as e:
        print(f"Error processing {pdf_path}: {e}")
        return None

Quick Reference

Enable Granite Model

from docling.document_converter import DocumentConverter
from docling.datamodel.pipeline_options import PipelineOptions

options = PipelineOptions(do_ocr=True, ocr_engine="granite")
converter = DocumentConverter(options)

Optimize for Speed

options = PipelineOptions(
    do_table_structure=False,
    do_ocr=False,
    do_picture_classifier=False
)

Extract Tables

for item in result.document.iterate_items():
    if item.label == "table":
        print(item.text)

Parallel Processing

from concurrent.futures import ThreadPoolExecutor

with ThreadPoolExecutor(max_workers=4) as executor:
    results = list(executor.map(converter.convert, pdf_files))

Additional Resources

Reference Files

For detailed guidance:

• references/granite-guide.md - Complete Granite model usage guide
• references/performance-optimization.md - Performance tuning and benchmarks
• references/complex-documents.md - Handling complex document structures
• references/troubleshooting.md - Advanced troubleshooting scenarios

Example Files

Working examples in examples/:

• granite_processing.py - Scanned PDF processing with Granite
• table_extraction.py - Advanced table extraction patterns
• batch_optimization.py - Optimized batch processing

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This skill covers advanced Docling features. For basics, see docling-fundamentals skill. For chunking strategies, see docling-chunking skill.