A SECURE END-TO-END AI FRAMEWORK FOR OCRDRIVEN TEXT RECOGNITION AND SEMANTIC INTELLIGENCE IN ENTERPRISE SYSTEMS

Abstract

xThis paper presents a novel secure end-to-end AI framework that integrates OCR-driven text recognition with semantic intelligence for enterprise systems. The proposed architecture addresses critical challenges in processing heterogeneous document formats while ensuring end-to-end data confidentiality and integrity. Unlike conventional OCR pipelines that treat extraction and interpretation as separate stages, our framework employs a unified neural architecture combining vision transformers for text detection, convolutional attention for character recognition, and fine-tuned large language models for contextual semantic understanding. A key innovation is the integration of lightweight cryptographic enclaves and differential privacy mechanisms directly within the AI pipeline, enabling secure document processing without compromising model accuracy or latency. The framework implements automated redaction of sensitive entities and role-based access control at the inference layer. Experimental evaluation on enterprise document corpora demonstrates 98.2% character accuracy, 23% improvement in semantic query relevance, and end-to-end processing latency under 420ms with negligible security overhead. This work provides a blueprint for deploying privacy-preserving, semantically-aware document intelligence systems in regulated industries while maintaining compliance with data protection regulations.