8 post(s)
A hands-on comparison of five ways to extract tables from PDFs in Python: pdfplumber, Camelot, Tabula, AWS Textract, and manual regex. With code, benchmarks, and honest pros and cons.
Three practical approaches to extracting structured data from PDFs into JSON: regex on raw text, template-based extraction, and AI-powered extraction with code for each.
The hidden costs of cobbling together Puppeteer, pdfcpu, and Ghostscript for PDF tasks. How a single API replaces your entire PDF toolchain.
A practical guide to PDF OCR: how to check if a PDF actually needs OCR, Tesseract vs cloud APIs, and when you should skip OCR entirely by generating PDFs with real text layers.
A practical guide to parsing PDFs for retrieval-augmented generation. Covers chunking strategies, PyMuPDF vs Marker vs LlamaParse, and code for extracting and embedding PDF content.
A head-to-head comparison of Kreuzberg, PyMuPDF, and pdfplumber for Python PDF parsing. Benchmarks, architecture differences, and code examples to help you pick the right tool.
An honest comparison of AWS Textract, Google Document AI, Adobe PDF Extract, and open-source alternatives for PDF text extraction in 2026.
A practical guide to extracting text from PDFs in Python. Covers PyMuPDF, pdfplumber, and when you should skip extraction entirely and just generate a new PDF.