PDF Document Layout Analysis
| Property | Value |
|---|---|
| Author | HURIDOCS |
| Model Type | Document Layout Analysis |
| Requirements | 4GB RAM, 6GB GPU (optional) |
| GitHub | Repository |
What is pdf-document-layout-analysis?
This innovative model service provides comprehensive PDF document analysis capabilities, offering both visual and non-visual approaches to segment and classify different elements within PDF documents. At its core, it employs two distinct technologies: a Vision Grid Transformer (VGT) model trained on the DocLayNet dataset, and LightGBM models that process XML information extracted via Poppler.
Implementation Details
The service implements a dual-model approach: The primary visual model (VGT) "sees" the entire page context, while the lighter LightGBM models process structural information. The system supports 11 different categories including captions, footnotes, formulas, lists, headers, pictures, tables, and more. It's capable of maintaining proper reading order and can handle complex document layouts.
- Advanced OCR integration with Tesseract and ocrmypdf
- Docker-based deployment with optional GPU support
- Flexible API endpoints for different extraction needs
- Support for multiple output formats including LaTeX and markdown
Core Capabilities
- Accurate page segmentation and element classification
- Intelligent reading order determination
- Table extraction in multiple formats (markdown, LaTeX, HTML)
- Formula extraction in LaTeX format
- High performance with 96.2% overall accuracy on PubLayNet dataset
Frequently Asked Questions
Q: What makes this model unique?
The model's dual-approach architecture sets it apart, offering both high-accuracy visual processing and resource-efficient non-visual processing options. Users can choose between performance and speed based on their needs.
Q: What are the recommended use cases?
The model is ideal for document processing pipelines, academic research, content extraction systems, and any application requiring structured extraction of content from PDFs. It's particularly useful when dealing with complex documents containing mixed content types.
