Real-time Optical Character Recognition in Manufacturing Using YOLOv8 and Embedded Systems for Engraved Characters on a Metal Surface

This study introduces a YOLOv8-based Optical Character Recognition (OCR) system specifically optimized for engraved character recognition, aiming to facilitate digital transformation and enhance smart manufacturing processes. To overcome limitations of manual part identification and quality inspection prevalent in conventional manufacturing environments, this study employed engraved character data from metal scroll compressor components. A lightweight deep learning model was designed and deployed on a Raspberry Pi platform to enable real-time character recognition. In a controlled laboratory environment, more than 150 images were acquired and processed through data augmentation and normalization techniques. The YOLOv8 object detection model was trained under various lighting and angular conditions, achieving high recognition performance (recall: 94.8%, mAP: 88.8%). Additionally, a post-processing algorithm was implemented to organize detected characters by their positions and classes, thereby reconstructing final product identification codes. Results confirmed the feasibility of real-time quality inspection and the potential for process automation in manufacturing environments. Future research needs to focus on enhancing recognition precision through improved post-processing techniques for reverse-oriented characters and diverse text layouts, while also exploring alternative embedded platforms to further optimize system efficiency.