This project uses the GPT model to realize intelligent parsing of PDF files and efficiently handle complex content such as typesetting, mathematical formulas, tables, pictures, and charts. Its core advantage lies in its high accuracy and average parsing cost of only $0.013 per page, which greatly improves PDF processing efficiency. This low-cost and high-effective solution has extremely high practical value for users or businesses that need to process a large number of PDF documents. This project utilizes the PyMuPDF library for initial parsing, combined with large visual models (such as GPT-4) for in-depth processing, and finally generates Markdown files that are easy to edit and use. The following are detailed steps:
This Github project uses the GPT model to parse PDF files, which can perfectly parse the layout, mathematical formulas, tables, pictures, charts and other content in PDF, with an average cost per page of $0.013. The steps to parse PDF files are as follows: 1. Use the PyMuPDF library to parse PDF into non-text areas and text areas.
Use the PyMuPDF library to parse PDF into non-text areas and text areas, and use large visualization models such as GPT-4o to parse and obtain Markdown files. 2. Use a large visualization model (such as GPT-4o) to parse and obtain Markdown files.
This project uses advanced AI technology to provide new solutions for PDF document processing, greatly reducing costs and improving efficiency. Interested users can go to Github to view the project details and experience its efficient and convenient PDF parsing function. In the future, this project is expected to be more widely used in fields such as data extraction and document automation.