A Review on the Application of Optical Character Recognition (OCR) in Robotics

Optical Character Recognition (OCR), once primarily associated with scanned document processing, has evolved into a pivotal perceptual competence for robots operating in human-centered environments. This review synthesizes the evolution, integration, applications, and lingering challenges of OCR technology in robotics. The paper traces the paradigm shift from classical handcrafted methodologiesincluding stroke-based and region-based techniquesto contemporary deep learning architectures, encompassing convolutional, transformer-based, and vision-language models. It further investigates the integration paradigms of OCR within robotic perception pipelines across heterogeneous platforms, including mobile robots, manipulators, autonomous vehicles, and aerial drones. Real-world deployment domainssuch as logistics automation, service robotics, medical assistance systems, autonomous driving, and infrastructure inspectionare elaborated to elucidate the practical efficacy and deployment constraints of robotic OCR. Particular attention is paid to robotics-specific challenges, including motion blur, extreme viewpoints, environmental degradation, limited onboard computation, and safety-critical latency requirements. Despite substantial progress in recent years, robust and real-time scene text understanding in unconstrained real-world environments remains an open research frontier. Finally, the review identifies promising future research directions aimed at enabling more reliable, efficient, and context-aware reading capabilities for robots in real-world scenarios.