Extracting data from PDFs is a crucial task across many industries, and doing it manually can be both tedious and error-prone. The document discusses six methods for extracting data from PDFs, emphasizing the importance of automating this process to save time and ensure accuracy. These methods include manual extraction, using Python libraries, online PDF converters, Optical Character Recognition (OCR), Large Language Models (LLMs), and GenAI-based solutions like Nanonets. Each method has its strengths and limitations, catering to different needs such as handling complex layouts or unstructured data. Automated tools and AI-driven solutions like Nanonets are highlighted for their ability to process large volumes of data efficiently, adapt to various document formats, and significantly reduce manual data entry time. While these technologies offer enhanced efficiency and precision, challenges such as complex layouts, inconsistent formatting, and security features like password protection still pose difficulties in PDF data extraction. Understanding these methods allows professionals to choose the most suitable tool for their specific requirements, facilitating the extraction of valuable insights from PDF documents.