Medical device interoperability is critical for improving healthcare quality. However, widespread adoption has been hindered by the prevalence of legacy devices lacking native communication capabilities. Replacing or retrofitting these devices is often infeasible owing to high financial costs and regulatory challenges. This study aimed to solve this problem by developing a noninvasive system that enables real-time data integration from heterogeneous medical devices. We developed a system using optical character recognition (OCR) to read numerical data from the screens of multiple medical devices using a camera or capture card. For numerical recognition, we developed two AlexNet-based models trained from scratch: a general-purpose model for standard digits, and a font-specific model for superimposed digits that occur during screen refresh transitions. The training data for the 55 classes of the font-specific model include synthetically generated images of overlapping digits created using a logical OR operation. The accuracy of the system was evaluated against frame-by-frame visual confirmation. Furthermore, its ability to perform real-time analysis was validated by integrating it with the Cartor hemodynamic simulator during a surgical procedure on a canine model of induced myocardial infarction. The general-purpose model achieved an overall accuracy of 99.84%, while the font-specific model achieved perfect accuracy of 100%, successfully resolving all cases of digit overlap. In the animal experiment, the system succeeded to track changes in cardiac function in real time, calculated from six hemodynamic parameters, with an update frequency of once every 0.63 s. The system captured the dynamic response of the left ventricular ejection curve index (SL) to multiple coronary artery ligations performed to induce myocardial infarction, where the SL value initially remained stable before showing a clear decline. This study demonstrates that an OCR-based approach can serve as a practical and effective bridge technology that enables existing non-networked medical devices to connect with advanced analytical tools such as physiological simulators, facilitating real-time clinical decision support without requiring costly equipment replacement. This paper presents a feasible method for advancing data-driven medical care in diverse clinical settings.