Looking at the code, this converts PDF pages to images, then transcribes each image. I might have expected a pdftotext post-processor. The complexity of PDF I guess ...
https://github.com/ocrmypdf/OCRmyPDF
No LLMs required.
No luck. They just could not grasp it. So they kept using their process of printing out the file on paper and then scanning it back in as a PDF image file.
I finally quit trying. Now of course they've seen the light and are painstakingly OCRing all that old stuff.
Python: PyPdf2, PdfMiner.six, Grobid, PyMuPdf; pytesseract (C++)
paperetl is built on grobid: https://github.com/neuml/paperetl
annotateai: https://github.com/neuml/annotateai :
> annotateai automatically annotates papers using Large Language Models (LLMs). While LLMs can summarize papers, search papers and build generative text about papers, this project focuses on providing human readers with context as they read.
pdf.js-hypothes.is: https://github.com/hypothesis/pdf.js-hypothes.is:
> This is a copy of Mozilla's PDF.js viewer with Hypothesis annotation tools added
Hypothesis is built on the W3C Web Annotations spec.
dokieli implements W3C Web Annotations and many other Linked Data Specs: https://github.com/dokieli/dokieli :
> Implements versioning and has the notion of immutable resources.
> Embedding data blocks, e.g., Turtle, N-Triples, JSON-LD, TriG (Nanopublications).
A dokieli document interface to LLMs would be basically the anti-PDF.
Rust crates: rayon handles parallel processing, pdf-rs, tesseract (C++)
pdf-rs examples/src/bin/extract_page.rs: https://github.com/pdf-rs/pdf/blob/master/examples/src/bin/e...