Optical character recognition (OCR) software has evolved beyond its traditional use of digitizing paper documents. Modern OCR applications now utilize AI and machine learning to automate end-to-end data capture workflows for a wide range of business documents, extracting meaningful information from unstructured data. To choose the best OCR software for specific needs, it's essential to consider factors such as accuracy rates, data extraction throughput, processing time, pricing, support, and technical expertise required in-house. Reviewing comparative matrices, benchmarking against other OCR applications, and testing on a comprehensive sample dataset can help make an informed decision. Key considerations include the ability to extract data from specific document types, such as invoices, receipts, bank statements, or medical prescriptions, as well as integrating with existing ERPs and business software. By understanding the strengths and limitations of various OCR solutions, businesses can select the most suitable tool for their unique use cases, streamlining workflows and reducing manual entry errors.