What question did this study set out to answer?

The aim is to develop a real-time OCR pipeline that ensures predictable timing for edge vision applications.

April 25, 2026Open Access

RT-OCR : Real-Time OCR Pipeline with Formal Timing Guarantees for Edge Vision Systems

Key Points

The aim is to develop a real-time OCR pipeline that ensures predictable timing for edge vision applications.
Developed a timing-aware pipeline architecture for OCR on edge devices.
Implemented an adaptive workload controller to manage processing demands.
Conducted performance evaluation on Jetson Nano and Raspberry Pi 5 with 10,000 consecutive frames.
Achieved 73% reduction in latency jitter.
Improved worst-case execution time (WCET) by 32%.
Demonstrated 97.5% accuracy with 100% compliance to deadlines.

Abstract

Deep learning OCR achieves high accuracy but exhibits unpredictable latency, preventing deployment in safety-critical systems like autonomous vehicles and industrial robotics. RT-OCR provides formal worst-case execution time (WCET) bounds on edge platforms through a timing-aware pipeline architecture, adaptive workload controller, and measurement-based WCET characterization on Jetson Nano and Raspberry Pi 5. Evalua- tion demonstrates 73% jitter reduction, 32% WCET improvement, and 97.5% accuracy with 100% deadline compliance across 10,000 consecutive frames.

RT-OCR : Real-Time OCR Pipeline with Formal Timing Guarantees for Edge Vision Systems

Key Points

Abstract

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