Document data extraction involves the process of retrieving meaningful information from unstructured or semi-structured documents, with automated methods using AI and machine learning being particularly effective. Intelligent Document Processing (IDP) encompasses a sequence of steps to transform, categorize, and extract data from documents using AI technologies like computer vision and natural language processing, making the data actionable and relevant. The challenges in automated data extraction include dealing with diverse document types and ensuring data security, but advancements in AI tools have improved the handling of complex documents. The market for IDP solutions is growing rapidly, driven by the potential for increased productivity and cost savings, as evidenced by companies like Nanonets, which offer AI-based OCR software for efficient document processing. The choice of data extraction software depends on factors like hardware requirements, cost, technical support availability, and integration with existing systems, with both open-source and commercial options available to suit different needs.