docling-pp-doc-layout 0.2.1

A Docling plugin for PaddlePaddle PP-DocLayout-V3 model document layout detection.

docling-pp-doc-layout

A Docling plugin that provides document layout detection using the PaddlePaddle PP-DocLayout-V3 model.

This plugin seamlessly integrates with Docling's standard pipeline to replace the default layout models with PP-DocLayout-V3, enabling high-accuracy, instance segmentation-based layout analysis with polygon bounding box support, properly processed in optimized batches for enterprise scalability.

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GitHub  |  PyPI

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Overview

docling-pp-doc-layout provides the PPDocLayoutV3Model layout engine for Docling. It automatically registers itself into Docling's plugin system upon installation. When configured in a Docling DocumentConverter, it intercepts page images, batches them, and infers document structural elements (text, tables, figures, headers, etc.) using HuggingFace's transformers library.

Key Features:

• High Accuracy Layout Parsing: Uses the RT-DETR instance segmentation framework.
• Polygon Conversion: Gracefully flattens complex polygon masks to Docling-compatible bounding boxes.
• Enterprise Scalability: Configurable batch sizing avoids out-of-memory (OOM) errors on large documents.

Architecture & Integration

When you install this package, Docling discovers it automatically through standard Python package entry points.

flowchart TD
    A[Docling DocumentConverter] --> B[PdfPipeline]

    subgraph Plugin System
    C[Docling PluginManager] -.->|Discovers via entry-points| D[docling-pp-doc-layout]
    D -.->|Registers| E[PPDocLayoutV3Model]
    end

    B -->|Initialization| C
    B -->|Predict Layout Pages| E
    E -->|Batched Tensors| F[HuggingFace AutoModel]
    F -->|Raw Polygons / Boxes| E
    E -->|Post-processed Clusters & BoundingBoxes| B

Requirements

• Python 3.13+
• docling>=2.73
• transformers>=5.1.0
• torch

Installation

# with uv (recommended)
uv add docling-pp-doc-layout

# with pip
pip install docling-pp-doc-layout

Usage

Using docling-pp-doc-layout is exactly like configuring standard Docling options.

from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling_pp_doc_layout.options import PPDocLayoutV3Options

# 1. Define Pipeline Options
pipeline_options = PdfPipelineOptions()

# 2. Configure our custom PPDocLayoutV3Options
pipeline_options.layout_options = PPDocLayoutV3Options(
    batch_size=8,                  # Tweak for GPU VRAM usage
    confidence_threshold=0.5,      # Filter low-confidence detections
    model_name="PaddlePaddle/PP-DocLayoutV3_safetensors" # Target HuggingFace model repo
)

# 3. Create the converter
converter = DocumentConverter(
    format_options={
        "pdf": PdfFormatOption(pipeline_options=pipeline_options)
    }
)

# 4. Convert Document
result = converter.convert("path/to/your/document.pdf")
print("Converted elements:", len(result.document.elements))

Configuration Options

The PPDocLayoutV3Options dataclass gives you full control over the engine:

Parameter	Type	Default	Description
batch_size	int	8	How many pages to process per single step. Decrease to lower memory usage; Increase to speed up processing of large documents.
confidence_threshold	float	0.5	The minimum confidence score (0.0 - 1.0) required to keep a layout detection cluster.
model_name	str	"PaddlePaddle/PP-DocLayoutV3_safetensors"	HuggingFace repository ID. Allows overriding if you host your local copy or a fine-tuned version.

Development

If you wish to contribute or modify the plugin locally:

git clone https://github.com/DCC-BS/docling-pp-doc-layout.git
cd docling-pp-doc-layout

# Install dependencies and pre-commit hooks
make install

# Run checks (ruff, ty) and tests (pytest)
make check
make test

License

MIT © DCC Data Competence Center