A mobile-based course grade management system with a multi-layer security architecture integrating hybrid OCR
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A mobile-based course grade management system with a multi-layer security architecture integrating hybrid OCR
Abstract
Digital transformation in higher education requires course grade management systems that provide efficient, secure, and mobile-based data processing. This study proposes a mobile course grade management system with a multi-layer security architecture integrating Hybrid OCR to support the automatic recognition of handwritten scores from paper-based grade sheets. The proposed system consists of a Gateway API Server, Database Server, AI Server, and a mobile application. Hybrid OCR combines EasyOCR and a Convolutional Neural Network (CNN) for handwritten score recognition, while Human Verification and Feedback Learning enable instructors to validate recognition results and accumulate verified samples for future model retraining. The multi-layer security architecture strengthens device authentication, session authentication, and secure data communication. Experimental results on real course grade sheets collected at Tay Nguyen University show that the CNN achieved an accuracy of 95.17% on the independent test set. The multiprocessing architecture reduced the average processing time to 4.19 seconds per grade sheet, representing an improvement of approximately 40.3% compared with sequential processing. The system operated reliably while preserving lecturer verification before official grade submission (Tay Nguyen University, 2025). The proposed system demonstrates the feasibility of deploying secure mobile-based course grade management in credit-based education environments (Tay Nguyen University, 2021) and provides a foundation for continuous model improvement through Feedback Learning.
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