docling-project/docling

Features

• Document Layout Analyzers - Maps spatial relationships between text, tables, and images by applying computer vision and advanced text processing techniques to document layouts.
• Hierarchical Document Models - Organizes document content into a hierarchical tree structure that preserves the semantic and spatial relationships between individual elements.
• Structured - Extracts information from unstructured sources by applying schemas to identify and organize content into clean, typed data formats.
• Document and LLM Preparation - Converts diverse file types and web content into unified, machine-readable formats specifically optimized for downstream model training and analysis.
• Schema-Driven Extractors - Maps document regions to strongly-typed objects by validating content against predefined structural templates.
• Document Processing Pipelines - Ingests and parses unstructured files into a unified, hierarchical data model to facilitate standardized downstream processing.
• Document Layout Analysis - Parses hierarchical document structures to identify and relate text, tables, and images for intelligent content analysis.
• Schema-Based - Validates extracted document data against defined schemas to ensure structural integrity and type safety.
• Intermediate Representations - Normalizes diverse input formats into a consistent internal data model to enable uniform processing across different sources.
• Structured Data Extractors - Identifies and transforms complex document layouts into standardized, machine-readable information.
• Document Conversion Toolkits - Ships programmatic utilities to convert diverse file formats into standardized outputs for automated data processing pipelines.
• AI Frameworks - Library for parsing and ingesting diverse document formats for retrieval.
• Data Preprocessing - Unified document parsing tool for complex layouts and multi-format support.
• Document and File Processing - Converts diverse document formats into structured data.
• Document Parsing and Extraction - Prepares diverse document types for generative AI workflows.
• Conversion Engines - Transforms diverse file formats and web content into structured models using both programmatic and command-line interfaces.
• Document Intelligence Pipelines - Automates the ingestion, parsing, and structuring of unstructured files through a modular pipeline for downstream data analysis.
• Automated Document Processing - Integrates document parsing capabilities into software pipelines to enable autonomous data handling within larger application workflows.
• Processing Backends - Employs a modular architecture to dynamically load specialized engines for optical character recognition and complex visual layout analysis.
• Extraction Configurations - Defines specific input types and file formats to ensure that documents are processed according to custom requirements.
• Automated Workflow Integration - Enables integration with automated agents and server-based architectures, allowing document processing tasks to be embedded directly into complex application workflows.
• Declarative Configuration Schemas - Allows users to define extraction parameters and processing rules through external configuration files to control document parsing behavior.

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