With the advent of Industry 4.0 era, smart manufacturing is rapidly developing, which puts higher requirements on data acquisition (DAQ). Currently, enterprises face problems in data acquisition such as a variety of equipment types, equipment heterogeneity, customized development, high costs, and so on. To solve this problem, this paper proposes an innovative approach to address the challenges in data acquisition by combining You Only Look Once version 8 (YOLOv8) and Optical Character Recognition (OCR) technology. First, the data area to be collected is labeled, and then the YOLOv8 model is trained. The model with the shortest recognition time and highest efficiency is selected with comparable detection effects to fully guarantee real-time data collection. Second, to ensure the accuracy of data collection, the OCR model is trained a second time using the dataset, improving performance compared to the most effective PaddleOCR by 8 percentage points. Additionally, considering the aging and weak computational capacity of individual devices, this study adopts a client/server architecture, deploying the computational load required for recognition to the server to achieve generalization, stability, and reliable operation of the system. Lastly, the scheme is tested on 16 types of 30 devices.
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