# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 1 Introduction  
In recent years, the importance of daily health monitoring has grown significantly, driven by the increase in cases of patients with chronic conditions such as diabetes, hypertension, and cardiovascular diseases. Patients with this type of condition are often required to track physiological data, such as blood pressure, glucose levels, or oxygen saturation,using medical devices at home [1]. However, despite the widespread use of such devices, a challenge remains in efficiently collecting and sharing the recorded data with healthcare providers. Currently, many medical devices require manual data entry into personal health records or mobile applications, which can be considered a tedious process and prone to many errors [2;3]. This monitoring process can be optimized using intelligent systems that digitize and centralize the data present in these devices. Additionally, many medical measurement devices do not have built-in communication interfaces, such as Bluetooth or Wi-Fi, which prevents them from automatically sending data to external systems or applications. Even in cases where connectivity is available, data transmission is often restricted to proprietary applications, limiting interoperability and integration with broader healthcare platforms. Almost all recommended medical devices use a screen to show measurement values. Within those devices that use a screen, the majority use a seven-segment display [4]. Despite their simple structure, seven-segment digits can be difficult to detect and recognize. Additionally, acronyms (which can be units of measurement or the category the values represent) typically are not in seven-segment format and can have multiple fonts, adding another layer of complexity to the recognition process. The development of systems for automating the recognition of medical or industrial device displays has already been explored in previous works [1; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. A diverse range of methodologies were employed in these studies, from traditional image-processing techniques to deep learning models. Regarding studies that focused on traditional processing methods, various approaches have been employed to identify seven-segment digits. Adaptive thresholding and binarization techniques have been applied to enhance contrast and isolate digit segments from the background, as demonstrated by the works [3; 5]. Segmentation techniques have been widely used to extract individual digits from display screens. For instance, [PERSON] et al. [3] proposed an algorithm that combines adaptive thresholding, segmentation, and a predefined lookup table for digit classification. Feature extraction methods were also used to refine digit recognition by analyzing segment shapes and spatial positioning. [PERSON] et al. [6] explored this approach using Hue, Saturation, Value (HSV)-based color slicing to improve segmentation accuracy. [PERSON] et al. [4] proposed a hybrid approach that combined binarization with Maximally Stable Extremal Regions (MSER) to enhance digit segmentation in seven-segment displays. Additionally, their method incorporated machine learning, using a Multi-Layer Perceptron (MLP) classifier to improve recognition accuracy beyond that of traditional image-processing techniques. As for studies that follow a deep learning approach [1; 7; 8; 9; 10; 11; 12; 13; 14; 15], we can observe a prevalence in the use of models such as Convolutional Neural Networks and object detection frameworks. [PERSON] et al. [1] used the You Only Look Once (YOLOv3) and Single Shot MultiBox Detector (SSD) models for the detection and classification of digits and acronyms in medical devices. The study focused on three types of devices: glucometers, blood pressure monitors, and oximeters. Among the models tested, YOLOv3 demonstrated the most consistent performance. In the context of industrial devices, [PERSON] et al.