Excel tables have long been a preferred method for storing structured data due to their clarity and computational capabilities, used by individuals and organizations alike for various tasks. However, extracting data from tables in printed or handwritten documents is challenging, often requiring computer vision and image processing techniques to accurately retrieve table cells from PDFs or scanned images. The article explores the importance of table cell extraction across different use cases, such as transferring electrical records, survey data collection, and payment reconciliation, highlighting the need for automated solutions. It discusses traditional and modern approaches to table cell detection, including the use of convolutional neural networks and optical character recognition (OCR) technologies, and provides tutorials for implementing these methods using tools like Python libraries and Google Vision API. The article also reviews existing market solutions for table extraction, including offerings from tech giants like Google and Amazon, as well as specialized services like Nanonets, which provide flexible, template-independent extraction capabilities. Concluding with the significance of digitalization, the article underscores the transformative potential of these technologies in automating labor-intensive tasks.