Performance Analysis of an Offline Text Detection System Based on Edge AI A Case Study of DokuScan Pro - Defi Pujianto; Kadarsih Kadarsih; Sri Hartati (Journal of Computer Networks, Architecture and High Performance Computing, 2026)
The growing use of mobile document scanning applications has increased the demand for text detection systems that can operate reliably in offline and on-device environments. Although Edge AI enables local inference without network dependency, system-level empirical evidence regarding its performance under real-world mobile usage conditions remains limited. This study presents a system-level evaluation of an offline Edge AI–based text detection system for mobile document scanning, using DokuScan Pro as a case study. The evaluation was conducted on 40 document images captured under varying lighting conditions, capture angles, and background characteristics. System performance was assessed using precision, recall, F1-score, and inference time to characterize on-device behavior rather than algorithmic novelty. Experimental results show that the system achieved a precision of 1.00, a recall of 0.975, and an F1-score of approximately 0.98, with an average inference time of 63.8 ms per image during fully offline execution on mobile devices. These results indicate stable system-level performance under real-world document scanning conditions with controlled computational overhead. This study provides empirical system-level insights into the feasibility and practical limitations of deploying Edge AI–based text detection in offline mobile document scanning applications, thereby complementing existing model-centric research with evidence from real-world, on-device evaluation.