The guide discusses the process of extracting text from images effortlessly using various methods. Manual methods include converting images to PDF and then copying text, using Microsoft Word to convert picture to text, and extracting text in Google Drive. These methods are slow, tedious, and generally inefficient for large volumes of images. Semi-automated methods involve leveraging open-source OCR libraries like Pytesseract and Large Language Models (LLMs) to extract and process extracted text. However, these methods require coding proficiency and may not produce the desired results, especially with complex data formatting. Automated methods utilize cutting-edge technology like Optical Character Recognition (OCR) and LLMs to convert multiple images to text online, providing enterprise-grade security, SLAs around uptime, and features like signature detection. These methods can handle large volumes of images accurately and retain source formatting. The guide emphasizes the importance of selecting an appropriate method for extracting accurate text from images, considering factors such as image clarity, orientation, file size, and maintaining original text formatting.