Intelligent Laboratory Asset Monitoring Using OCR-Integrated Borrowing Logging and Predictive Equipment Maintenance
DOI:
https://doi.org/10.5281/zenodo.21637523Keywords:
asset tracking, equipment optimization, lab management, optical character recognition, predictive maintenanceAbstract
Effective laboratory management demands accurate asset tracking, reduced transaction latency, and reliable record integrity to support operational efficiency and research continuity. This study proposes and evaluates an OCR-driven intelligent laboratory asset monitoring system designed to automate equipment logging without requiring additional tagging infrastructure. Comparative analysis against Manual & Excel, QR Code, and RFID-based systems demonstrates that the proposed framework significantly improves operational performance, reducing average logging time from 80 seconds to 37 seconds while increasing inventory record accuracy from 70% to 96.5%. By leveraging optical character recognition to capture existing printed or engraved equipment identifiers, the system minimizes deployment complexity and integrates seamlessly within legacy laboratory environments. The centralized database architecture enables real-time validation, structured identification enforcement, and transparent status transitions, enhancing data consistency and user confidence. Despite RFID achieving marginally higher peak accuracy, the OCR-driven approach attained the highest usability rating, reflecting strong user acceptance and workflow efficiency. Overall, the findings position the proposed system as a scalable, cost-effective, and infrastructure-independent solution that balances efficiency, accuracy, and usability, providing a practical model for the digital transformation of laboratory asset management.
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