Paper Contents
Abstract
Developed an OCR-based system to automate document verification processes, improving accuracy and efficiency in approval workflows. The analysis was conducted using a comprehensive dataset of official documents, including ID proofs, certificates, and financial statements, ensuring the systems robustness across various formats. Advanced preprocessing techniques were applied to optimize OCR performance and ensure precise text extraction. The system achieved an impressive accuracy of 95% by fine-tuning key parameters, significantly outperforming traditional manual methods. It was proven to be highly reliable in handling complex layouts and detecting inconsistencies in submitted documents. We concluded that the system effectively interprets text variations in documents, enabling accurate and timely verification. The final results demonstrated its potential to streamline approval processes, reducing processing time and improving reliability. Such frameworks are poised to thrive in real-time applications while expanding their capabilities through broader datasets for enhanced generalization.
Copyright
Copyright © 2024 GIRIPRIYAN S. This is an open access article distributed under the Creative Commons Attribution License.
