Layout-Parser/layout-parser
Layout Parser
Layout-parser is a deep learning document layout parser and image analysis framework. It provides a toolkit for extracting structural information and layout patterns from scanned documents and digital images, transforming them into programmatic data structures for automated analysis.
The framework integrates layout detection with optical character recognition to convert tabular regions into machine-readable data. It utilizes neural networks to identify and classify structural elements within document images without relying on manual rule-based systems.
The system covers a broad range of document analysis capabilities, including document structure parsing, automated table extraction, and hierarchical layout representation. It also includes visualization tools to render detected elements and hierarchies over original images for result verification.
Features
- Document Layout Analysis - Provides deep learning tools to identify complex structures and layout elements from document images.
- Layout Parsing Toolkits - Provides a deep learning toolkit to detect and analyze structural elements within document images.
- Document Analysis Models - Applies neural networks to detect and classify document components without relying on manual rules.
- Document Structure Analysis - Extracts layout and text from images into specialized programmatic data structures for analysis.
- Hierarchical Representations - Organizes detected document elements into a parent-child tree structure to preserve logical information flow.
- Tabular Grid Mapping - Combines visual region detection with OCR to map textual content into structured tabular grids.
- Document Region Detectors - Uses deep learning neural networks to identify and classify structural regions within document images.
- Table Structure Reconstructions - Transforms tabular regions into machine-readable formats by reconstructing logical grids from visual elements.
- Visual Structural Elements - Locates and identifies specific structural elements within document images using deep learning models.
- Document Extraction Tools - Offers a library for parsing document images into programmatic data structures for downstream analysis.
- Tabular Data Extraction - Converts tabular data from document images into machine-readable formats using layout detection and OCR.
- Pixel Coordinate Mappings - Maps neural network bounding box outputs to normalized pixel coordinates for consistent document analysis.
- Bounding Box Visualizers - Renders detection masks and bounding boxes over original images for manual verification of parsing accuracy.
- OCR Layout Integration Pipelines - Combines layout detection with OCR in a pipeline to convert tabular regions into machine-readable data.
- Parsing Verification Overlays - Renders detected layout elements and hierarchies visually to verify automated document parsing accuracy.
- Layout Visualization Tools - Renders detected document elements and hierarchies visually to verify automated parsing accuracy.
- Unified Model Wrappers - Standardizes different deep learning backends under a single API to allow swapping detection models seamlessly.
- PDF Processing Tools - Deep learning-based tool for document layout analysis.
