Parsr VS marker

I'm part of the team that build LlamaParse. It's net improvement compare to other PDF->Structured Text extractors (I build several in the past, includig https://github.com/axa-group/Parsr).

For character extraction, LlamaParse use a mixture of OCR / character extraction from the PDF (it's the only parser I'm aware of that address some of the buggy PDF font issues, check the 'text' mode to see raw document before reconstruction), use a mixture of heuristic and Machine learning models to reconstruct the document.

Once plug with a Recursive retrieval strategy, allow you to get Sota result on question answering over complexe text (see notebook: https://github.com/run-llama/llama_parse/blob/main/examples/...).

AMA

Link to the Github Repository and pre defined Docker Compose Build file for Parsr

I would check out https://github.com/Unstructured-IO/unstructured (what lang chain uses) or https://github.com/axa-group/Parsr (probably what unstructured copied to get their startup off the ground lol)

Things I still want to try: - Parsr

I personally haven't used it yet, but I heard some good things about Parsr: https://github.com/axa-group/Parsr

Parsing PDFs is very non-trivial process. Google and Amazon parses are largely based on OCRing. There are some advanced state-of-the-art NN-based OCR approaches but they are not very stable, but a stable industry standard is Tesseract, and nice all-in-one open source tools that brings a ton of tools together is https://github.com/axa-group/Parsr . hope this helps

You may want to try https://github.com/VikParuchuri/surya (I'm the author). I've only benchmarked against tesseract, but it outperforms it by a lot (benchmarks in repo). Happy to discuss.

You could also try https://github.com/VikParuchuri/marker for general PDF parsing (I'm also the author) - it seems like you're more focused on tables.

Hi HN - I made texify to convert equations to markdown/LaTeX for my project marker [1] then realized it could be generally useful.

Texify converts equations and surrounding text to Markdown, with embedded LaTeX (MathJax compatible).

You can either use a GUI to select equations (inline or block) from PDFs and images to convert, or use the CLI to batch convert images. It works on CPU, GPU, or MPS (Mac).

The closest open source comparisons are pix2tex and nougat - marker is more accurate than both of them for this task. However, nougat is more for entire pages, and pix2tex is more for block equations (not inline equations and text).

I trained texify for 2 days on 4x A6000 GPUs - I was pleasantly surprised how far I could get with limited GPU resources by reframing the problem to use small parameter counts/images.

Texify is licensed for commercial use, with the weights under CC-BY-SA 4.0. Fine them here - https://huggingface.co/vikp/texify .

See the texify repo for more details, benchmarks, how to install, etc.

[1] https://github.com/VikParuchuri/marker

https://github.com/VikParuchuri/marker

Both are tools to convert pdfs into Latex or Markup with latex formulas. Maybe that helps