Cognitive Automation Using Natural Language and Optical Character Recognition - Rahul Kiran Talaseela, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
Cognitive automation technologies, particularly Natural Language Processing (NLP) and Optical Character Recognition (OCR), are revolutionizing how organizations handle unstructured data. This article explores how these technologies transform business operations by automating the interpretation, extraction, and conversion of unstructured information from documents, emails, and contracts into actionable intelligence. The integration of these cognitive capabilities enables organizations to process document-intensive workflows with greater speed, accuracy, and consistency while reducing operational costs. Implementation examples from finance and legal departments demonstrate significant performance improvements in invoice processing, receipt management, purchase order matching, and contract analysis. The technical architecture supporting these capabilities features modular components that work together to ingest, pre-process, recognize, interpret, and integrate document information into business systems. Despite implementation challenges related to data quality, training requirements, and system integration, organizations adopting these technologies report substantial returns through increased efficiency, improved accuracy, faster processing, enhanced compliance, greater scalability, and reduced costs.
Keywords: Automation; Blockchain; Cognitive Processing; Document Extraction; Machine Learning