Businesses heavily rely on spreadsheets for various operational tasks, but converting information from different formats into Excel can be laborious. Recent advancements in computer vision and text understanding have improved automated data extraction techniques, allowing the transformation of PDF data into Excel-friendly formats like CSV. This process involves recognizing and converting data structures, such as tabular formats and key-value pairs, which can be challenging due to their representation in PDFs as unstructured pixels. Deep learning, particularly through neural networks and convolutional neural networks, plays a crucial role in improving optical character recognition (OCR) accuracy for extracting text from PDFs. Tools like the Google Vision API, PyTesseract, and services from companies like Nanonets offer solutions for automating this conversion process, making it accessible even to those without a programming background. The article highlights the significance of these technologies and provides tutorials for using Python and various APIs to automate data extraction and conversion into Excel-compatible formats.