LLM Ingestion

Core Summary This rigorous evaluation, conducted on an L4 GPU, benchmarks three leading open-source PDF parsers—MinerU, Granite-Docling, and PaddleOCR-VL—across 12 critical dimensions. Using a diverse dataset including multi-level financial reports, double-column arXiv papers, and scanned invoices, the test highlights the current state-of-the-art in document layout analysis for LLM ingestion. ▶ IBM’s Docling dominates in structural fidelity and hierarchical parsing, positioning itself as the gold standard for enterprise-grade structured data extraction. ▶ MinerU maintains a competitive edge in academic contexts, excelling at LaTeX-style layouts and complex mathematical notation reconstruction. ▶ PaddleOCR-VL leverages the power of Vision-Language Models (VLMs) to outperform traditional OCR in handling noisy scans and multilingual edge cases, such as German invoices. Bagua Insight PDF parsing is the "dirty work" of the GenAI era, yet it remains the single biggest bottleneck for RAG performance. This benchmark signals a definitive shift from legacy OCR (Optical Character Recognition) to sophisticated Document AI that understands semantic layout. Docling’s performance suggests that IBM is successfully weaponizing its enterprise DNA into lightweight, high-performance open-source tools. Meanwhile, the convergence of VLMs into the parsing pipeline (as seen with PaddleOCR-VL) indicates that the future of document ingestion isn't just about reading text—it's about seeing and reasoning over the page as a human would. For developers, the "one-size-fits-all" parser is a myth; the real value lies in building an intelligent orchestration layer above these specialized engines. Actionable Advice 1. Decouple by Modality: Deploy MinerU for scholarly/technical content and Docling for structured business documents to maximize RAG accuracy.2. Implement VLM Routing: Use VLM-based parsers like PaddleOCR-VL specifically for low-quality scans or non-standard layouts where traditional heuristic-based parsers fail.3. Optimize for Token Efficiency: Pay close attention to how these tools handle table headers and nested lists; poor Markdown conversion leads to "hallucination-prone" embeddings in downstream LLM tasks.