Optical Character Recognition (OCR) technology, when integrated with Robotic Process Automation (RPA), provides a powerful solution for streamlining document workflows by automating the extraction and processing of data from documents. Traditional OCR tools often rely on template-based systems that struggle with semi-structured documents, but newer machine learning-based OCR solutions offer more flexibility and accuracy, particularly when paired with RPA for tasks such as document classification and data extraction. The synergy of RPA and machine learning facilitates hyper automation, enabling software bots to handle complex document processing tasks and significantly reduce manual errors and time spent on repetitive work. This integration is especially beneficial for managing structured, semi-structured, and unstructured documents, as it allows for the deployment of intelligent OCR systems that can adapt and improve over time. Despite challenges like weak data and integration issues, the combination of AI, OCR, and RPA can enhance data processing efficiency and reduce costs. Companies like Nanonets are advancing this field by offering API-integrated, machine learning-driven OCR solutions that can be incorporated into platforms like UiPath to automate document workflows seamlessly.