Extracting tables from PDFs, often challenging due to issues like split tables and inconsistent formatting, can be streamlined using various tools and technologies. Basic methods include using MS Excel's built-in PDF import feature for simple tables or online PDF converters for quick extraction without software installation. For more complex tables, Python libraries like Camelot and Java-based Tabula offer programmatic solutions, though they require coding knowledge and may struggle with scanned PDFs. Advanced techniques involve leveraging Large Language Models, such as GPT-4, which can provide context-aware extraction through user interfaces or APIs, though they may incur costs and require careful prompt engineering. The most robust option involves AI-based Intelligent Document Processing (IDP) tools like Nanonets, which automate the entire workflow from extraction to post-processing, handling complex tabular structures with built-in OCR and offering scalable, secure solutions. Each method has its own advantages and limitations, making the choice dependent on the specific requirements and complexity of the PDF data involved.