Automated Data Extraction — Document Processing

MinerU is a document parsing pipeline designed to transform unstructured files into machine-readable, structured data. It utilizes deep learning models to perform layout analysis, identifying document regions and extracting complex content such as mathematical expressions. By combining these neural network inferences with geometric heuristics, the system reconstructs the reading order and structural hierarchy of documents to ensure accurate data representation. The project distinguishes itself through a multi-stage processing workflow that integrates layout detection, optical character recognition, and formula extraction into a unified pipeline. It serializes all extracted features and spatial coordinates into a standardized format, ensuring that output remains consistent for downstream integration. To support verification, the tool includes a diagnostic suite that generates visual overlays, allowing users to inspect segmentation boundaries and reading order directly against the original source files. The software provides a comprehensive framework for automated data extraction, organizing parsed elements into a page-based structure suitable for large-scale information retrieval. It is distributed as a Python-based package, with documentation and installation instructions available in the repository.