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.
A complete tutorial for building a Python document pipeline that queries a database, formats data with Jinja2, generates PDFs via API, and delivers them via email or S3.
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.
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.
Build an automated invoice processing pipeline that turns raw transaction data into branded PDF invoices. Complete working example with HTML template and API integration.
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.
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.