Developed a high-accuracy PDF-to-CSV conversion algorithm using Python that extracts every table into separate CSV files, generates a full-document text file, and outputs structural metadata files detailing table outlines, dimensions, and formatting—robust across any document layout. Built a modular extraction pipeline using Docling, PyMuPDF, Pandas, and a 3-layer OCR stack.
The CSV Converter is a sophisticated document processing tool designed to extract tabular data from PDF documents with high accuracy. The system handles complex document layouts and converts tables into structured CSV format while preserving formatting and relationships between data elements.
The conversion pipeline employs a modular architecture that processes documents through multiple stages: initial structure analysis, table detection, OCR processing with fallback layers, and finally CSV generation with metadata preservation.
The 3-layer OCR stack ensures maximum accuracy by attempting extraction with progressively more specialized OCR engines, allowing the system to handle documents with varying quality, fonts, and layouts. The metadata output provides detailed information about table structure, enabling downstream applications to understand the relationships and formatting of extracted data.