Docling: Converting Complex Documents into AI-Ready Structured Representations
Documents remain one of the primary carriers of knowledge in scientific, enterprise, and governmental settings, yet their complex visual structure—comprising layouts, tables, figures, and multi-column text—poses significant challenges for modern AI systems. Large language models do not natively understand such document structures, while traditional PDF parsers often lose semantic and structural information, leading to noisy or incomplete representations. In this talk, we present Docling, an open-source document processing framework designed to transform heterogeneous documents into high-quality, structured representations suitable for AI applications. Docling combines layout analysis, OCR, table structure recognition, and document assembly into a unified pipeline that produces rich, LLM-friendly outputs such as structured JSON and Markdown while preserving visual grounding and reading order. The framework supports multiple input formats and integrates with modern AI ecosystems, enabling applications including retrieval-augmented generation, schema-based information extraction, and agent-driven workflows. We discuss the architecture of Docling, its emerging ecosystem of models and tools, and its role in enabling scalable, privacy-preserving document intelligence through local execution and open standards. By bridging the gap between visually complex documents and machine-readable knowledge, Docling provides a foundation for reliable document understanding in next-generation AI systems.