Best PDF Parsers for RAG Applications

Sep 22, 2025

Overcoming PDF Hell

In Retrieval-Augmented Generation (RAG) and other LLM-based applications, PDF parsing is one of the trickiest challenges. PDF files are everywhere—invoices, academic papers, scans, reports—and getting reliable, semantically meaningful text out of them is harder than you’d think. Below, we explore what makes PDF parsing hard (“PDF Hell”), the major challenges, what features you need in a good parser, and compare some of the best tools & libraries (open-source and commercial) you can use.

Key Challenges When Parsing PDFs for RAG / LLM Use

Here are the major obstacles you’ll face, drawn from the article and from general experience:

Challenge	Description / Effects
Fixed layout & lack of semantic markup	PDFs are spatial: text is positioned by coordinates. Headings, paragraphs, sidebars, columns etc often are not tagged semantically. This makes simple extraction yield scrambled text order.
Multi-column, irregular layouts	Scientific papers, invoices, newsletters often have multiple columns or unpredictable layout. Tools that read line-by-line left to right may jumble the order.
Scanned PDFs / image PDFs	Some PDFs are just images (scans or photos). You need OCR + preprocessing (de-skew, noise removal, etc). Otherwise text extraction fails or is very low quality.
Mixed content	PDFs containing both native text and text embedded in images, watermarks, background images, form elements, handwritten text etc. These complicate detection and extraction workflows.
Tables	Extracting tabular data is especially hard: how to detect the table, its rows/columns, spanning over pages, borders vs visual layout etc. Pure text extractors often fail; computer-vision or hybrid approaches may help.
Orientation / rotation / skew	Pages may be rotated or scanned at odd angles. Some pages might switch between portrait/landscape. Need auto-detection & correction.
Bad generators / curves instead of text	Some PDFs store what appears to be text as vector graphics (“curves”) instead of real text glyphs. Then text extraction doesn’t work; you must fall back to OCR or image conversion.
Header/footer noise	Repeated headers, footers, page numbers, watermarks — all add noise and token waste when feeding into LLMs. Removing or ignoring them is helpful.
Searchable PDFs with poor underlying OCR	A PDF may already have a text layer, but that layer might be low quality, split text spans weirdly, double-spaced, mis-positioned, etc. Assumptions that “searchable = good” often fail.
Performance, cost, and privacy	OCR is expensive compute-wise; cloud services may raise privacy concerns; large documents slow to process. If handling many PDFs, these factors matter.

What Features a Good PDF Parser / Extraction Pipeline Should Have

To deal well with PDF Hell, a parser or extraction system should satisfy many of the following:

Layout preservation or at least metadata about positions, so that paragraphs, tables, columns etc can be reconstructed.
Mode detection / switching, e.g. detect if a page is native text, or image / scanned, or mixture, and choose different extraction strategy (text extraction vs OCR vs hybrid).
Preprocessing of images: de-skewing, noise reduction, rotation correction, contrast adjustments.
Table extraction support: detecting tables visually or via layout, reconstructing rows and columns in a useful format.
Form / interactive element support: being able to extract data from checkboxes, radio buttons, filled fields.
Header/footer detection and removal (or at least tagging) to avoid token bloat.
Scalability & performance: ability to process large numbers of pages, large documents, efficiently.
Privacy / deployment options: e.g. on-premise vs SaaS, encryption etc.
Robust error handling & fallback strategies: what to do when the PDF is weird (curves, mixed types, bad layouts etc).

Survey: Popular Tools & Libraries

Given those challenges & requirements, here are some of the leading tools / frameworks you might consider. I’ll compare them on strengths, weaknesses, suitability for RAG use.

Tool / Library	Strengths / Best For	Weaknesses / Limitations
Datalet (Marker)	Likely good for structured extraction, maybe layout awareness. Note: specific details depend on documentation.	May have limited community / maturity; possibly less flexible for unusual layouts.
llamaindex	Popular in the RAG / LLM community; good for embedding + indexing + pipelines. You can plug in different document loaders / parsers.	Parsing support depends on external libraries; handling really bad PDFs / scans may require additional modules / custom logic.
jina ai	Strong in search, embeddings, building vector indexes; likely has tools or connectors for document ingestion.	Might require configuration / customization for advanced table extraction or for scans / OCR.
Unstructured.io	Very strong in extracting structured info from complex documents; good tools for handling layout, splitting etc.	Might be more resource intensive; licensing / cost if using commercial or enterprise versions.
Vectorize.io	Good commercial options; probably optimized for speed / production usage.	May cost; may limit customization; handling odd edge cases might require fallback logic.
GroundX by eyelevel.ai	Likely focused, perhaps with custom models; possibly good quality for particular domains.	Might have less documentation / community; possibly domain-specific bias.
LangChain	Excellent orchestration framework; many existing document loaders that use PDF libraries + OCR; great for building full RAG pipelines.	LangChain itself is not a PDF extractor — quality depends on underlying extraction tool; for many edge cases you’ll need to extend/customize.
PyMuPDF (fitz)	Strong low-level library; very good for getting text, images, extracting metadata, positions. Fair speed.	Doesn’t do OCR out of the box; tables detection is minimal; complex layout rebuilding / semantic understanding has to be built on top.
pdf-js	Good for browser / NodeJS usage; rendering, interacting with PDFs in client side or server side. Can extract text.	Limited in table detection, forms, OCR; not ideal if you need heavy layout or image-based extraction.