ICR (Intelligent Character Recognition) is a technology that recognizes and digitizes handwritten or machine-printed characters from images or video streams using machine learning algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). ICR applications are used for identity verification, healthcare patient form digitization, handwritten label recognition logistics, financial document processing, and digitizing written responses in educational settings. The technology has evolved to improve accuracy and efficiency, leveraging advancements in deep learning, natural language processing, and robotic process automation. ICR can recognize and interpret handwritten text, extract checkboxes, tick marks, and other structured data elements, support multiple languages, and make handwritten documents searchable, editable, and retrievable. It has numerous benefits, including automation and efficiency, scalability and cost savings, improved decision-making, and improved data quality. The technology works through seven steps: image capture, preprocessing, binarization/segmentation, feature extraction, pattern recognition, context analysis, and post-processing. ICR can be implemented using open-source tools like Tesseract OCR, commercial tools like ABBYY FineReader, and programming languages such as Python. It has significant applications across various sectors, including healthcare, insurance, traffic management, legal analysis, and government agencies, offering substantial improvements in process efficiency and data accuracy.