Layout Detection v4.5.0¶

Detect document layout regions (tables, figures, headers, text blocks, etc.) in PDFs using ONNX-based deep learning models. Enables table extraction, figure isolation, reading-order reconstruction, and selective OCR.

Model¶

Layout detection uses the RT-DETR v2 model, an ONNX-based deep learning model that detects 17 layout element classes: text blocks, tables, figures, headers, footers, captions, code, lists, sections, formulas, footnotes, page headers/footers, titles, checkboxes, key-value regions, and document indexes.

When to Enable¶

Recommended for: complex multi-column PDFs, scanned documents, academic papers, business forms, and any document where layout understanding improves extraction accuracy.

Less beneficial for: simple single-column text documents, high-throughput pipelines where latency is critical (consider GPU acceleration), or documents already well-handled by PDF structure trees.

Performance Impact¶

171-document PDF corpus, CPU only. GPU acceleration significantly reduces the time penalty.

Configuration¶

from kreuzberg import ExtractionConfig, LayoutDetectionConfig, extract_file

config = ExtractionConfig(
    layout=LayoutDetectionConfig(
        confidence_threshold=0.5,
        apply_heuristics=True,
        table_model="tatr",
    )
)
result = await extract_file("document.pdf", config=config)

const result = await extract("document.pdf", {
  layout: {
    confidenceThreshold: 0.5,
    applyHeuristics: true,
    tableModel: "tatr",
  },
});

use kreuzberg::core::{ExtractionConfig, LayoutDetectionConfig};

let config = ExtractionConfig {
    layout: Some(LayoutDetectionConfig {
        confidence_threshold: Some(0.5),
        apply_heuristics: true,
        table_model: Some("tatr".to_string()),
        ..Default::default()
    }),
    ..Default::default()
};

[layout]
apply_heuristics = true
# table_model = "tatr"

# Enable layout detection with default settings
kreuzberg extract document.pdf --layout --content-format markdown

# Custom confidence threshold
kreuzberg extract document.pdf --layout-confidence 0.5 --content-format markdown

# Specific table model
kreuzberg extract document.pdf --layout --layout-table-model slanet_wired

# Combined with GPU acceleration
kreuzberg extract document.pdf --layout --acceleration coreml

Table Structure Models v4.5.3¶

When layout detection identifies a table region, a table structure model analyzes rows, columns, headers, and spanning cells.

GPU Acceleration¶

Layout detection uses ONNX Runtime with automatic provider selection:

To override:

config = ExtractionConfig(
    layout=LayoutDetectionConfig(),
    acceleration=AccelerationConfig(provider="cuda", device_id=0)
)

See AccelerationConfig reference for details.

Layout Classes¶

The RT-DETR v2 model detects 17 layout classes:

Acknowledgments¶

• Docling — RT-DETR v2 model and layout classification approach
• TATR — Table structure recognition with ONNX
• PaddleOCR — SLANeXT table structure and PP-LCNet classifier models

Related¶

• Configuration Reference — full field reference
• Element-Based Output — using layout-aware results