Blog
Guides, comparisons, and updates on PDF-to-markdown conversion.
The Definitive Guide to PDF Structure (For Developers Who Hate PDFs)
Why can't you just read the text out of a PDF? Because PDF is a page description language, not a document format. Here's exactly what's inside a PDF file, why text extraction is so painful, and what to do about it.
Why We Chose PaddleOCR Over Tesseract (And You Should Too)
Tesseract is the default choice for OCR. But vision-language models like PaddleOCR-VL-1.5 represent a fundamental shift in how machines read documents. Here's why we built on PaddleOCR and what it means for extraction quality.
PDF Parsing in 2026: Tesseract vs PyMuPDF vs Vision Models
A comprehensive comparison of every major approach to PDF text extraction — text-extraction libraries, traditional OCR, cloud OCR services, and vision-language models. Strengths, weaknesses, pricing, and when to use each.
The Hidden Cost of Bad PDF Parsing in RAG Systems
Poor PDF parsing silently destroys RAG pipeline quality. Broken tables, lost headings, and garbled text produce bad embeddings, irrelevant retrieval, and LLM hallucinations. Here's how to quantify the damage and fix it.
How to Build a RAG Pipeline with PDF Documents
A step-by-step tutorial for building a retrieval-augmented generation pipeline that ingests PDFs. Uses pdftomarkdown, LangChain, OpenAI embeddings, and ChromaDB — with complete, runnable Python code.
What Makes Good Markdown for LLMs? A Guide to Document Chunking
Not all markdown is equal when feeding documents to LLMs. Heading hierarchy, table formatting, and clean section separation directly affect chunking quality and retrieval accuracy. Here's what matters and how to chunk it.
How to Extract Tables from PDFs in Python
Camelot, tabula-py, and pdfplumber all break on complex tables — multi-header layouts, merged cells, spanning columns. Here's what fails, why it fails, and how to get clean markdown tables from any PDF with a single API call.
Document AI Without Fine-Tuning: How Vision-Language Models Changed OCR
Traditional document extraction required templates or fine-tuned models for every document type. Vision-language models like PaddleOCR-VL understand any document out of the box. Here's how the paradigm shifted.
pdfToMarkdown vs LlamaParse for RAG: A Deeper Comparison
Building a RAG pipeline that ingests PDFs? This post compares pdfToMarkdown and LlamaParse specifically for retrieval-augmented generation — framework lock-in, embedding quality, pricing at scale, and side-by-side output from the same PDF.
Automate Invoice Processing with Python: A Step-by-Step Guide
Build a complete invoice processing pipeline in Python: watch a folder for new PDFs, extract structured data with pdftomarkdown, parse fields with regex and LLM fallback, and push to your accounting system.
pdfToMarkdown vs Unstructured: The Right Tool for Your Pipeline
Unstructured is a powerful open-source library for document parsing. pdfToMarkdown is a zero-setup API that returns clean markdown. Here's when to use each.
pdfToMarkdown vs LlamaParse: PDF Parsing for LLM Pipelines
Both tools convert PDFs for LLM workflows. LlamaParse is tightly coupled to the LlamaIndex ecosystem. pdfToMarkdown is a standalone API that works with any stack. Here's the difference.
pdfToMarkdown vs Mathpix: Which PDF API Should You Use?
Mathpix is excellent for scientific papers with equations. pdfToMarkdown is the better choice for most developers. Here's how they compare on price, output quality, and ease of use.
Why PDF to Markdown? The Case for Structured Text Extraction
PDFs are everywhere, but extracting clean, structured text from them is surprisingly hard. Here's why markdown is the ideal output format and how pdfToMarkdown solves it.