Converting handwritten documents into digital text is a crucial yet challenging task for industries like healthcare, legal, and finance, due to the variability in handwriting styles, noise from scanning, and lack of training data for certain scripts. Manual transcription, though accurate, is time-consuming and costly, while online converters offer speed but lack precision with complex handwriting and pose security risks. Python libraries such as Tesseract allow for customizable solutions but require technical expertise and may not handle cursive writing well. AI-based Intelligent Document Processing (IDP) platforms like Nanonets offer high accuracy, context understanding, and adaptability across document types, making them the most viable option for large-scale and complex handwriting-to-text conversion tasks, despite still not achieving perfect accuracy. The choice of method largely depends on the specific requirements and resources available for the task at hand.