MinerU is a production-grade document parsing engine that converts PDFs, images, and Office documents (DOCX, PPTX, XLSX) into structured Markdown and JSON. It supports multiple backends: a fast pipeline (layout analysis + OCR + formula/table recognition), a high-accuracy hybrid mode combining VLM and OCR, and a pure VLM mode using vLLM/LMDeploy. The system is deployed as a CLI tool, FastAPI service, Gradio web UI, or Docker container, with integrations for RAG frameworks like LangChain, LlamaIndex, and Dify. The codebase is well-architected with clean abstractions for data I/O (file/S3), model initialization (singleton pattern), and output generation (client-side rendering). It handles 109 languages and includes specialized modules for mathematical formula recognition (UniMERNet), table structure recovery (SLANet+/UNet), and office document normalization. While the code is actively maintained and functionally rich, it carries some technical debt from its PaddleOCR heritage (vendored code, eval-based builders) and has a complex optional dependency matrix for hardware accelerators.
This repository provides a Python library for converting web pages into text optimized for Large Language Model (LLM) input. It fetches HTML content using Playwright (headless browser) and processes it to extract meaningful text while preserving structural information like tables, links, and images in a text format. The core processing logic converts HTML tables to markdown, handles image and link preservation (or removal), and strips unnecessary tags (script, style) to reduce noise. The library is designed as a preprocessing step before feeding web content to LLMs for tasks such as data extraction or summarization. It includes three main modules: HTML fetching, HTML processing, and a combined convenience function.