Sarcouncil Journal of Multidisciplinary

Abstract: Optical Character Recognition (OCR) technology has been the cornerstone of enterprise digital transformation for decades, enabling organizations to bridge the difference between physical documents and digital information systems. Technology has evolved to include integrated, refined document processing solutions with key enterprise resource planning platforms, from simple character recognition. However, the technical landscape is experiencing a fundamental paradigm change with the emergence of artificial intelligence systems characterized by advanced computer vision capabilities. These AI-based options provide better performance in several dimensions, including accuracy, implementation speed, customization for documentary variations, and total cost of ownership. The integration of advanced computer vision capabilities enables these systems to maintain high accuracy across varying document conditions including poor lighting, complex backgrounds, and partially occluded text - while contextually understanding document semantics in ways traditional template-based approaches cannot achieve.. Traditional OCR vendors face challenges of survival as organizations rapidly recognize the standalone OCR system as heritage technology that can be consolidated in extensive AI strategies. This change not only affects technology providers but also shapes enterprise automation strategies, implementation approaches, and economic models for document processing. The transition represents more than technical development - it fundamentally changes how organizations extract value from unstructured document data while simultaneously reducing costs and improving operational efficiency.