My use case is research papers. That means very clear text, combined with graphs of varying form and quality and finally occasional formulas. Two approaches I had most, but not full, success with are: 1) converting to image with pdf2image, then reading with pytesseract 2) throwing whole pdfs into pypdf 3) experimental multimodal models You can get more if you make content more predictable (if you know this part is go…
Depending on how much structure you want to extract before passing the pdf contents to the next step in your pipeline, this paper[1] might be helpful in surfacing more options. It's a review/benchmark of numerous tools applied to the information extraction of academic documents. I haven't been through to evaluate the solutions they examined, but it's how I discovered GROBID and IMO lays out the strengths of each approach clearly.