PDF-Extract-Kit

MinerU's document parsing quality rests on PDF-Extract-Kit, OpenDataLab's toolbox of state-of-the-art models for taking complex PDFs apart. It bundles layout detection with DocLayout-YOLO, YOLO-v10, and LayoutLMv3 to locate text, titles, images, tables, and formulas; inline and block formula detection plus UniMERNet for converting formula images to LaTeX; PaddleOCR for text extraction with position data; StructTable-InternVL2-1B for turning tables into LaTeX, HTML, or Markdown; and a reading order module. Each task is exposed as a module, so pipelines can mix and match components rather than adopt a monolithic converter, and Read the Docs covers installation, model weight downloads, and per-algorithm evaluation. Setup uses a conda environment with Python 3.10 and pip-installed requirements, with a separate CPU-only requirements file available, though a GPU is the standard configuration for real throughput. The license is AGPL-3.0, inherited from the YOLO code and PyMuPDF dependencies, and the repository has about 9,800 stars. Choose it for building custom extraction pipelines; choose MinerU for ready-made PDF to Markdown conversion.